AI Engineering Bootcamp: Become an AI-Enabled Software Engineer who Builds, Orchestrates and Distributes
LIVE Bootcamp Designed & Taught by AI Industry Experts & Engineering Leaders
The AI revolution is here. Engineers who can build with LLMs, design agents, and deploy AI at scale are in the highest demand.
AI Engineering Bootcamp is a LIVE, weekend program transforming experienced developers into AI Engineers. Over 9 weekends, master the full AI stack: RAG, agents, multi-agent systems, MCP, fine-tuning, cost optimisation, observability, and cloud deployment, taught by industry experts trusted by 1M+ learners.
9
Weekends
18
Live Sessions
8+
Real Projects
1
Capstone Project
1 Year
Access to LIVE Class Recording
232+
Enrolled Learners
Join our Bootcamp
Learn In-demand Skills
Get Hired by Top Companies
Codebasics Promise
We stand by the quality of our content. Not satisfied? No questions asked refund until 14th March 2026
Added Benefit
If you have already taken our paid AI Engineering course(s), you will pay only the difference (This Bootcamp Price - Money You Paid for AI Engineering Course(s) Price)
9
Weekends
18
Live Sessions
8+
Real Projects
1
Capstone Project
1 Year
Access to LIVE Class Recording
232+
Enrolled Learners
What Makes This Bootcamp Different?
-
100% LIVE, instructor-led sessions every weekend. Real-time interaction, Q&A, and doubt clearing.
-
Covers the FULL AI engineering spectrum, from LLM fundamentals, embeddings & vector databases to RAG, agents, multi-agent systems, fine-tuning, context engineering, cost optimisation, and cloud deployment. This isn’t just an agentic framework course
-
Designed and taught by AI industry experts & engineering leaders with real-world experience building and shipping AI systems at scale
-
Master the complete modern AI stack: Python, FastAPI, LangChain, LangGraph, DSPy, MCP, Qdrant, LangSmith, Azure AI, Unsloth, Ollama & more
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Production-first mindset: Cost optimisation, caching, rate limiting, model routing & scaling the engineering challenges that matter in real products
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LLM Fine-Tuning with LoRA/QLoRA + running models locally with Ollama go beyond API wrappers
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Context Engineering & MCP (Model Context Protocol), the newest paradigms that top AI teams are adopting right now
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Guardrails, evaluations, adversarial attacks & compliance - build AI that’s safe for production
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Capstone project with architectural guidance + live project showdown in front of experts
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Live interview prep tips with industry AI leaders to prepare you for real AI engineering interviews
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Integrated soft skills training: AI Product Thinking, Personal Branding, Stakeholder Management, Time Management & Deep Work
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Practical job assistance: Resume building, LinkedIn optimisation, interview preparation, everything you need to land the AI engineer role
Hear It From
Our Happy Learners
Our content is rated 4.9/5 from 23769+ Learners
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SQL
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Machine Learning
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Python
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Completing this course has been an incredibly rewarding experience. Taught by Dhaval Patel, the Deep Learning: Beginner to Advanced course stands out for its clarity, structure, and practical depth.
From the fundamentals of neural networks to the intricacies of CNNs, RNNs, and Transformers, every concept is explained in a way that is accessible yet technically solid. What sets this course apart is the strong focus on hands-on learning — using PyTorch to build real-world projects that reinforce the theory.
Key strengths of the course:
✅ Clear and intuitive explanations
✅ Strong emphasis on real-world applications
✅ Step-by-step PyTorch implementation
✅ Coverage of cutting-edge topics like Transformers
✅ Practical tips and insights drawn from industry experience
Whether you're new to deep learning or looking to strengthen your foundation, this course is an excellent investment of time and effort. I walked away not just with knowledge, but with confidence to apply deep learning in real projects.
A big thank you to Dhaval Patel and the Codebasics team for making deep learning approachable and applicable.
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Codebasics’ machine learning course is excellent—better than 90% of industry courses. It’s easy to follow, well-structured, and simple to understand. I highly recommend it to anyone interested in learning machine learning and considering building their skills with this course.
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Landed a Job
Thank you Dhaval Sir and the entire Codebasics team for coming up with such courses which are super rare to find. Totally worth taking it. All the concepts were taught very easily and very intuitively so that we can grasp and digest the fundamentals within no time. The best part I like about the courses on codebasics.io is that they try to replicate real industry experiences in the form of Peter Panday and Tony Sharma. Well done. Keep up the good work. Will take every course from codebasics.io which is relevant for me. Cheers !!
Math & Stats
Landed a Job
This is the best AI course that I have gone through. Thank you Dhaval.
It would be great if you can create advanced courses with hands on real world complex problems to equip certain roles like AI Technical leads, Architects, Technical Managers
Hello CodeBasics team and fellow students!
I’ve just completed the Python course from the GEN-AI bootcamp, and I really want to thank Mr. Patel for conducting such engaging lectures and practical sessions. I had the chance to apply what I learned on two real-life projects (I’m saying two because some projects were excluded in GEN-AI, but these two were highly relevant).
The exercises after each unit were a great way to reinforce learning. My advice: take this course and practice consistently. Once you finish the course, keep practicing to truly master the concepts.
Thank you, CodeBasics! I’m proud to be part of this family and to have completed the first step in my GEN-AI/Data Science journey.
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Landed a Job
Overview
What you'll learn in
this AI Engineering Bootcamp
Welcome to The Gen AI and Data Science Bootcamp Experience
01h:55m:15s on-demand video
|
21 Lectures
Live Sessions
03h:58m:14s on-demand video
|
2 Lectures
Python: Beginner to Advanced For Data Professionals
17h:39m:41s on-demand video
|
114 Lectures
2:
Welcome to the Python Experience
7 Lectures
-
2.1: Course Introduction
Free -
2.2: What Benefits You Get By Learning Python?
Free -
2.3: Why Python is so Popular?
Free -
2.4: Who can Enroll In This Course? Any Prerequisites?
Free -
2.5: How is This Different From Free Codebasics YouTube Playlist?
Free -
2.6: What Kind Of Computer Do I Need For This Course?
Free -
2.7: Course Content Overview
Free
9:
Python Basics: Functions, Dictionaries, Tuples and File Handling
9 Lectures
-
9.1: Functions
-
9.2: Dictionary and Tuples
-
9.3: Modules and Pip
-
9.4: File Handling
-
9.5: Quiz: Functions, Dictionaries, Tuples and File Handling
-
9.6: Peter’s Request to Tony
-
9.7: Exercise: Functions, Dictionaries, Tuples and File Handling
-
9.8: Two Deadly Viruses Infecting Learners
-
9.9: Chapter Summary
19:
Project 2: Expense Tracking System
11 Lectures
-
19.1: Problem Statement & Tech Architecture
-
19.2: Database CRUD Operations
-
19.3: Automated Tests Setup for CRUD
-
19.4: Expense Management: Backend (FastAPI)
-
19.5: Expense Management: Logging
-
19.6: Streamlit Introduction
-
19.7: Expense Management: Frontend (Streamlit)
-
19.8: Analytics: Backend (FastAPI)
-
19.9: Analytics: Frontend (Streamlit)
-
19.10: README and Requirements.txt
-
19.11: Exercise
24:
Bonus Medical Data Extraction Project: Prescription Document
10 Lectures
-
24.1: Technical Architecture of the Project
Free -
24.2: Installation of Necessary Libraries
-
24.3: Extract text from a pdf document
-
24.4: Thresholding in OpenCV
-
24.5: Regular Expressions or Regex
-
24.6: Regex Exercise
-
24.7: Python class for prescription
-
24.8: Code Refactoring
-
24.9: Unit Tests using pytest
-
24.10: I Need a Favour
Online Credibility
00h:24m:12s on-demand video
|
4 Lectures
Build In Public
03h:04m:40s on-demand video
|
5 Lectures
SQL for Data Science
02h:02m:08s on-demand video
|
86 Lectures
5:
SQL Basics: Data Retrieval - Single Table
14 Lectures
-
5.1: Install MySQL: Windows
Free -
5.2: Install MySQL: Linux, Mac
Free -
5.3: Import Movies Dataset in MySQL
Free -
5.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.10: HAVING Clause
Free -
5.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.13: The Data God’s Blessing
Free -
5.14: Quiz
8:
SQL Basics: Database Creation & Updates
17 Lectures
-
8.1: Database Normalization and Data Integrity
-
8.2: Entity Relationship Diagram (ERD)
-
8.3: Mentor Talk: Art of Googling
-
8.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
-
8.5: Data Types: String (VARCHAR, CHAR, ENUM)
-
8.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
-
8.7: Data Types: JSON, Spatial (JSON, GEOMETRY)
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8.8: Luck Favors the LinkedIn Post
-
8.9: Primary key
-
8.10: Foreign Key
-
8.11: Create a Database From an Entity Relationship Diagram - ERD
-
8.12: Import Data From a CSV File Into a Database
-
8.13: Insert Statement
-
8.14: Update and Delete
-
8.15: I Need a Favour
-
8.16: Expect the Unexpected: The Intermission Scene
-
8.17: Quiz
9:
AtliQ Hardware & Problem Statement
8 Lectures
-
9.1: The Rise of Databases at AtliQ
Free -
9.2: Relational vs No-SQL Database
-
9.3: AtliQ Hardware’s Business Model
-
9.4: Profit & Loss Statement
-
9.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog
-
9.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import
-
9.7: Simplified: What is Kanban?
-
9.8: Quiz
10:
SQL Advanced: Finance Analytics
10 Lectures
-
10.1: Backlog Grooming Meeting: Gross Sales Report
-
10.2: User-Defined SQL Functions
-
10.3: Exercise: User-Defined SQL Functions
-
10.4: Gross Sales Report: Monthly Product Transactions
-
10.5: Gross Sales Report: Total Sales Amount
-
10.6: Exercise: Yearly Sales Report
-
10.7: Stored Procedures: Monthly Gross Sales Report
-
10.8: Stored Procedure: Market Badge
-
10.9: Benefits of Stored Procedures
-
10.10: Quiz
11:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
11.1: Problem Statement and Pre-Invoice Discount Report
-
11.2: Performance Improvement # 1
-
11.3: Performance Improvement # 2
-
11.4: Database Views: Introduction
-
11.5: Database Views: Post Invoice Discount, Net Sales
-
11.6: Exercise: Database Views
-
11.7: Top Markets and Customers
-
11.8: Exercise: Top Products
-
11.9: The Two Most Important Skills for the Data Analyst
-
11.10: Window Functions: OVER Clause
-
11.11: Window Functions: Using it in a Task
-
11.12: Exercise: Window Functions: OVER Clause
-
11.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.14: Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.15: 5 Ways SQL is Used in the Industry
-
11.16: Quiz
12:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
12.1: Supply Chain Basics : Simplified
-
12.2: Problem Statement
-
12.3: Create a Helper Table
-
12.4: Database Triggers
-
12.5: Database Events
-
12.6: Temporary Tables & Forecast Accuracy Report
-
12.7: Exercise: CTE, Temporary Tables
-
12.8: Subquery vs CTE vs Views vs Temporary Table
-
12.9: User Accounts and Privileges
-
12.10: Database Indexes: Overview
-
12.11: Database Indexes: Composite Index
-
12.12: Database Indexes: Index Types
-
12.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
12.14: Quiz
Math and Statistics For AI, Data Science
12h:49m:38s on-demand video
|
98 Lectures
1:
Welcome to Math and Statistics Experience
7 Lectures
-
1.1: Importance of Math and Stats in Data Science Career
Free -
1.2: How Is This Course Different from Other Courses on the Internet?
Free -
1.3: What Support Do You Provide If I Have Questions?
Free -
1.4: Who Should Take This Course?
Free -
1.5: Pre-requisites
Free -
1.6: System Requirements
Free -
1.7: Course Overview
Free
5:
Pandas, Matplotlib and Seaborn Basics
8 Lectures
-
5.1: Pandas Introduction and Installation
Free -
5.2: Dataframe Basics
Free -
5.3: Read, Write Excel and CSV Files
Free -
5.4: Handle Missing Data - Part 1
Free -
5.5: Handle Missing Data - Part 2
Free -
5.6: Grouping Data
Free -
5.7: Data Concatenation and Merging
Free -
5.8: Data Visualization with Matplotlib and Seaborn
Free
6:
Measures Of Central Tendency and Dispersion
18 Lectures
-
6.1: Descriptive vs. Inferential Statistics
Free -
6.2: Measures of Central Tendency: Mean, Median, Mode
Free -
6.3: Percentile
-
6.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
6.5: Quiz
-
6.6: Exercise
-
6.7: Measures of Dispersion: Range, IQR
-
6.8: Box or Whisker Plot
-
6.9: Outlier Treatment Using IQR and Box Plot
-
6.10: Quiz
-
6.11: Exercise
-
6.12: Measures of Dispersion: Variance and Standard Deviation
-
6.13: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
6.14: Correlation
-
6.15: Correlation vs Causation
-
6.16: Quiz
-
6.17: Exercise
-
6.18: Chapter Summary
10:
Phase 1: Find Target Market
21 Lectures
-
10.1: Data Validation Of Acquired Data
-
10.2: Data Understanding, MySQL Setup
-
10.3: Data Import in Jupyter Notebook
-
10.4: Data Cleaning: Handle NULL Values (Annual Income)
-
10.5: Data Cleaning: Treat Outliers (Annual Income)
-
10.6: Data Visualization: Annual Income
-
10.7: Exercise: Treat Outliers in Age Column
-
10.8: Exercise Solution: Treat Outliers in Age Column
-
10.9: Data Visualization: Age, Gender, Location
-
10.10: Peter’s Nightmare
-
10.11: Data Cleaning: Credit Score Table - Part 1
-
10.12: Data Cleaning: Credit Score Table - Part 2
-
10.13: Correlation among Credit Profile Variables
-
10.14: Exercise: Handle NULL Values in Transactions Table
-
10.15: Exercise Solution: Handle NULL Values in Transactions Table
-
10.16: Peter’s Confusion: IQR or Std Dev?
-
10.17: Data Cleaning: Treat Outliers using IQR (Transaction Amount)
-
10.18: Data Visualization: Transactions Table
-
10.19: Finalize the Target Group
-
10.20: Phase 1 Feedback Meeting With Stakeholders
-
10.21: Get Ready For Phase 2
11:
Central Limit Theorem
12 Lectures
-
11.1: Random Sampling & Sample Bias
-
11.2: The Law of Large Numbers
-
11.3: Central Limit Theorem, Sampling Distribution
-
11.4: Case Study: Solar Panels
-
11.5: Standard Error
-
11.6: Quiz
-
11.7: Z Score Table (Z-Table)
-
11.8: Quiz
-
11.9: Confidence Interval
-
11.10: Confidence Interval: Estimate Car Miles
-
11.11: Exercise
-
11.12: Chapter Summary
12:
Hypothesis Testing
19 Lectures
-
12.1: Null vs Alternate Hypothesis
-
12.2: Z Test, Rejection Region
-
12.3: Housing Inflation Test: Rejection Region
-
12.4: Quiz
-
12.5: Exercise
-
12.6: p-Value
-
12.7: Housing Inflation Test: p-Value
-
12.8: Quiz
-
12.9: Exercise
-
12.10: One-Tailed vs Two-Tailed Test
-
12.11: Type 1 and Type 2 Errors
-
12.12: Quiz
-
12.13: Statistical Power & Effect Size
-
12.14: A/B Testing
-
12.15: A/B Testing Using Z Test
-
12.16: A/B Testing: Drug Trial
-
12.17: Quiz
-
12.18: Exercise
-
12.19: Chapter Summary
Online Credibility & Domain Knowledge Course
00h:31m:00s on-demand video
|
5 Lectures
Master Machine Learning for Data Science & AI: Beginner to Advanced
22h:41m:19s on-demand video
|
201 Lectures
4:
Python Basics
43 Lectures
-
4.1: MUST WATCH: Go through this chapter ONLY IF
-
4.2: Skip This Chapter - Quiz
-
4.3: Setup Environment (Local Python and Google Colab)
-
4.4: Variables
-
4.5: Variables - Quiz
-
4.6: Variables - Exercise
-
4.7: Numbers
-
4.8: Numbers - Quiz
-
4.9: Numbers- Exercise
-
4.10: Strings
-
4.11: Strings - Quiz
-
4.12: Strings - Exercise
-
4.13: Lists
-
4.14: Lists - Quiz
-
4.15: Lists - Exercise
-
4.16: Install Pycharm
-
4.17: If Condition
-
4.18: If Condition - Quiz
-
4.19: If Condition - Exercise
-
4.20: For Loop
-
4.21: For loop - Quiz
-
4.22: For loop - Exercise
-
4.23: Functions
-
4.24: Functions -Quiz
-
4.25: Functions - Exercise
-
4.26: Dictionary and Tuples
-
4.27: Dictionary and Tuples - Quiz
-
4.28: Dictionary and Tuples - Exercise
-
4.29: Modules and Pip
-
4.30: Modules and Pip - Quiz
-
4.31: Modules and Pip - Exercise
-
4.32: File Handling
-
4.33: File Handling - Quiz
-
4.34: File handling - Exercise
-
4.35: Classes and Objects
-
4.36: Classes and Objects - Quiz
-
4.37: Classes and Objects - Exercise
-
4.38: Inheritance
-
4.39: Inheritance - Quiz
-
4.40: Inheritance - Exercise
-
4.41: Exception Handling
-
4.42: Exception Handling - Quiz
-
4.43: Exception Handling - Exercise
6:
Data preprocessing and Visualization
12 Lectures
-
6.1: MUST WATCH: Go through this chapter ONLY IF
-
6.2: Skip This Chapter - Quiz
-
6.3: Pandas Introduction and Installation
-
6.4: Dataframe Basics
-
6.5: Read, Write Excel and CSV Files
-
6.6: Handle Missing Data - Part 1
-
6.7: Handle Missing Data - Part 2
-
6.8: Grouping Data
-
6.9: Data Concatenation and Merging
-
6.10: Data Visualization Using Matplotlib and Seaborn
-
6.11: Data God Showing the way
-
6.12: Quiz
7:
Math & Statistics for Data Science, AI
46 Lectures
-
7.1: MUST WATCH: Go through this chapter ONLY IF
-
7.2: Skip This Chapter - Quiz
-
7.3: Descriptive vs. Inferential Statistics
-
7.4: Measures of Central Tendency: Mean, Median, Mode
-
7.5: Percentile
-
7.6: Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
7.7: Quiz
-
7.8: Exercise
-
7.9: Measures of Dispersion: Range, IQR
-
7.10: Box or Whisker Plot
-
7.11: Outlier Treatment Using IQR and Box Plot
-
7.12: Quiz
-
7.13: Exercise
-
7.14: Measures of Dispersion: Variance and Standard Deviation
-
7.15: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
7.16: Correlation
-
7.17: Correlation vs Causation
-
7.18: Quiz
-
7.19: Exercise
-
7.20: Probability Basics
-
7.21: Quiz
-
7.22: Addition and Multiplication Rule
-
7.23: Quiz
-
7.24: Conditional Probability and Bayes Theorem
-
7.25: Quiz
-
7.26: What Is a Distribution?
-
7.27: Skewness
-
7.28: Normal Distribution
-
7.29: Detect Outliers Using Normal Distribution
-
7.30: Quiz
-
7.31: Exercise
-
7.32: Z Score
-
7.33: Standard Normal Distribution (SND)
-
7.34: Quiz
-
7.35: Exercise
-
7.36: Random Sampling & Sample Bias
-
7.37: The Law of Large Numbers
-
7.38: Central Limit Theorem, Sampling Distribution
-
7.39: Case Study: Solar Panels
-
7.40: Standard Error
-
7.41: Quiz
-
7.42: Z Score Table (Z-Table)
-
7.43: Quiz
-
7.44: Confidence Interval
-
7.45: Confidence Interval: Estimate Car Miles
-
7.46: Exercise
8:
Supervised Machine Learning: Regression
29 Lectures
-
8.1: Simple Linear Regression
Free -
8.2: Multiple Linear Regression
Free -
8.3: Quiz
-
8.4: Exercise
-
8.5: Cost Function
-
8.6: Derivatives and Partial Derivatives
-
8.7: Chain Rule
-
8.8: Quiz
-
8.9: Exercise
-
8.10: Gradient Descent Theory
-
8.11: Gradient Descent: Python Implementation
-
8.12: Why MSE (and not MAE)?
-
8.13: Model Evaluation: Train, Test Split
-
8.14: Model Evaluation: Metrics
-
8.15: Peter Pandey Flexes his ML skills on LinkedIn
-
8.16: Quiz
-
8.17: Exercise
-
8.18: Data Preprocessing: One Hot Encoding
-
8.19: Quiz
-
8.20: Polynomial Regression
-
8.21: Quiz
-
8.22: Exercise
-
8.23: Overfitting and Underfitting
-
8.24: Reasons and Remedies For Overfitting / Underfitting
-
8.25: L1 and L2 Regularization
-
8.26: Bias Variance Trade Off
-
8.27: Quiz
-
8.28: Exercise
-
8.29: Chapter Summary
9:
Supervised Machine Learning: Classification
31 Lectures
-
9.1: Introduction to Classification
Free -
9.2: Logistic Regression: Binary Classification
Free -
9.3: Model Evaluation: Accuracy, Precision and Recall
-
9.4: Quiz
-
9.5: Exercise
-
9.6: Model Evaluation: F1 Score, Confusion Matrix
-
9.7: Logistic Regression: Multiclass Classification
-
9.8: Cost Function: Log Loss
-
9.9: Quiz
-
9.10: Exercise
-
9.11: Support Vector Machine (SVM)
-
9.12: Data Pre-processing: Scaling
-
9.13: Sklearn Pipeline
-
9.14: Quiz (disabled)
-
9.15: Quiz
-
9.16: Exercise
-
9.17: Naive Bayes: Theory
-
9.18: Naive Bayes: SMS Spam Classification
-
9.19: Quiz
-
9.20: Exercise
-
9.21: Decision Tree: Theory
-
9.22: Decision Tree: Salary Classification
-
9.23: I Need a Favour
-
9.24: Quiz
-
9.25: Exercise
-
9.26: Handle Class Imbalance: Theory
-
9.27: Handle Class Imbalance Using imblearn: Churn Prediction
-
9.28: Quiz
-
9.29: Exercise
-
9.30: Get inspired by Peter Pandey
-
9.31: Chapter Summary
10:
Ensemble Learning
21 Lectures
-
10.1: What is Ensemble Learning?
Free -
10.2: Majority Voting, Average and Weighted Average
-
10.3: Bagging
-
10.4: Bagging: Random Forest
-
10.5: Random Forest: Raisin Classification
-
10.6: Quiz
-
10.7: Exercise
-
10.8: Boosting: AdaBoost
-
10.9: Gradient Boosting: Regression Walk Through
-
10.10: Gradient Boosting: Regression Math
-
10.11: Gradient Boosting: Revenue Prediction
-
10.12: Quiz
-
10.13: Exercise
-
10.14: Gradient Boosting: Classification
-
10.15: XGBoost: Walk Through
-
10.16: XGBoost: California Housing Prediction
-
10.17: XGBoost: Synthetic Data Classification
-
10.18: XGBoost: Benefits
-
10.19: Quiz
-
10.20: Exercise
-
10.21: Chapter Summary
11:
Model Evaluation & Fine Tuning
16 Lectures
-
11.1: Introduction
-
11.2: Model Evaluation: ROC Curve & AUC
-
11.3: Cost Benefit Analysis Using ROC in Sklearn
-
11.4: Quiz
-
11.5: Exercise
-
11.6: K Fold Cross Validation
-
11.7: Stratified K Fold Cross Validation
-
11.8: Hyperparameter Tuning: GridsearchCV
-
11.9: Hyperparameter Tuning: RandomizedSearchCV
-
11.10: Quiz
-
11.11: Exercise
-
11.12: Model Selection Guide
-
11.13: Luck favors the LinkedIn post
-
11.14: Selecting the Right Evaluation Metric
-
11.15: Quiz
-
11.16: Chapter Summary
12:
ML Project Life Cycle
10 Lectures
-
12.1: 10 Stages of AI Project Life Cycle
Free -
12.2: Requirements and Scope of Work (SOW)
Free -
12.3: Data Collection
-
12.4: Data Cleaning & Exploratory Data Analysis
-
12.5: Feature Engineering
-
12.6: Model Selection & Training
-
12.7: Model Fine Tuning
-
12.8: Model Deployment
-
12.9: Monitoring and Feedback Using ML Ops
-
12.10: Chapter Summary
14:
Unsupervised Learning
13 Lectures
-
14.1: Introduction
-
14.2: K Means Clustering: Theory
-
14.3: K Means Clustering: Customer Segmentation
-
14.4: Hierarchical Clustering: Theory
-
14.5: Hierarchical Clustering: Customer Segmentation
-
14.6: Quiz
-
14.7: Exercise
-
14.8: DBSCAN: Theory
-
14.9: DBSCAN: Practical Implementation
-
14.10: Peter AI
-
14.11: Quiz
-
14.12: Exercise
-
14.13: Chapter Summary
15:
Project 1: Healthcare Premium Prediction (Regression)
16 Lectures
-
15.1: The Rise of AtliQ AI
Free -
15.2: Project Charter Meeting
Free -
15.3: Scope of Work, Task Planning in JIRA
-
15.4: Data Collection
-
15.5: Data Cleaning & EDA - Part 1
-
15.6: Data Cleaning & EDA - Part 2
-
15.7: Feature Engineering
-
15.8: Model Training, Fine Tunning
-
15.9: 98% Model Accuracy, Really?
-
15.10: Error Analysis
-
15.11: Model Segmentation
-
15.12: Request More Data
-
15.13: Model Retraining
-
15.14: Build App Using Streamlit
-
15.15: Deployment
-
15.16: Exercise
16:
Project 2: Credit Risk Modelling (Classification)
19 Lectures
-
16.1: Peter's Promotion: New Project
Free -
16.2: Domain Understanding: NBFC & Credit Approvals
Free -
16.3: Scope of Work & Tech Architecture
Free -
16.4: Data Collection
-
16.5: Quick Intro to Data Leakage
-
16.6: Data Cleaning
-
16.7: Exploratory Data Analysis (EDA)
-
16.8: Feature Engineering – Part 1
-
16.9: Weight of Evidence (WOE), Information Value (IV)
-
16.10: Feature Engineering – Part 2
-
16.11: Model Training & Evaluation
-
16.12: Introduction to Optuna
-
16.13: Model Fine Tuning Using Optuna
-
16.14: Intro To Rank Ordering & KS Statistic
-
16.15: Model Evaluation Using KS Statistic & Gini Coefficient
-
16.16: Streamlit App
-
16.17: Business Presentation
-
16.18: Deployment
-
16.19: Exercise
17:
ML Ops & Cloud Tools
22 Lectures
-
17.1: What is ML Ops?
Free -
17.2: Importance of ML Ops in Your Career
Free -
17.3: ML Flow: Purpose and Overview
Free -
17.4: ML Flow: Experiment Tracking
-
17.5: ML Flow: Model Registry
-
17.6: ML Flow: Centralized Server Using Dagshub
-
17.7: Quiz
-
17.8: What is API?
-
17.9: FastAPI Basics
-
17.10: Build FastAPI Server For Credit Risk Project
-
17.11: Quiz
-
17.12: Git Version Control System
-
17.13: Introduction to ML Cloud Platforms
-
17.14: AWS Sagemaker: Account Setup
-
17.15: AWS Sagemaker: Sagemaker Studio
-
17.16: AWS Sagemaker: 4 Ways to Train Model
-
17.17: AWS Sagemaker: Built In Algorithms
-
17.18: AWS Sagemaker: Script Mode
-
17.19: Quiz
-
17.20: Data Drift Detection Using PSI & CSI
-
17.21: PSI & CSI: Practical Implementation
-
17.22: Quiz
Job Assistance Portal, ATS Resume & Portfolio Website
01h:23m:48s on-demand video
|
14 Lectures
Deep Learning: Beginner to Advanced
13h:10m:01s on-demand video
|
85 Lectures
3:
Getting Started
10 Lectures
-
3.1: Who is Peter Pandey?
Free -
3.2: Peter Pandey’s journey to learn Deep Learning?
Free -
3.3: Neural Networks: The Foundation of Deep Learning
Free -
3.4: Deep Learning vs Statistical ML: When to Use What?
-
3.5: Neural Network Architectures
-
3.6: Real World Applications of Deep Learning
-
3.7: Tooling: PyTorch vs Tensorflow
-
3.8: Tooling: GPU, TPU
-
3.9: Quiz
-
3.10: Chapter Summary
11:
Convolutional Neural Networks (CNN)
10 Lectures
-
11.1: What is CNN? Convolution, Kernels, Pooling and Beyond
-
11.2: Padding and Strides
-
11.3: CIFAR10 Image Classification using CNN
-
11.4: Data Augmentation
-
11.5: Transfer Learning
-
11.6: Pre-trained Models – ResNet, EfficientNet, MobileNet etc.
-
11.7: Caltech101 Classification Using Transfer Learning
-
11.8: Quiz
-
11.9: Exercise
-
11.10: Chapter Summary
13:
Transformers
15 Lectures
-
13.1: Introduction to Transformer Architecture
-
13.2: Word Embeddings
-
13.3: Contextual Embeddings
-
13.4: Overview of Encoder and Decoder
-
13.5: Tokenization, Positional Embeddings
-
13.6: Attention Mechanism
-
13.7: Multi Headed Attention
-
13.8: Decoder
-
13.9: How Transformers are Trained?
-
13.10: Hugging Face: BERT Basics
-
13.11: Hugging Face: Spam Classification Using BERT
-
13.12: Hugging Face: Next Word Prediction Using GPT2
-
13.13: Quiz
-
13.14: Exercise
-
13.15: Chapter Summary
14:
Project: Car Damage Detection
10 Lectures
-
14.1: AtliQ AI’s First Big Client: Vroom Cars
-
14.2: Problem Statement & SOW
-
14.3: Data Load and Transformation
-
14.4: Model Training with CNN
-
14.5: Model Training with CNN and Regularization
-
14.6: Model Training using Transfer Learning
-
14.7: Hyperparameter Tunning using Optuna
-
14.8: Model Evaluation and Export
-
14.9: Streamlit App
-
14.10: FastAPI Server
Natural Language Processing
06h:59m:43s on-demand video
|
27 Lectures
3:
Text Representation
11 Lectures
-
3.1: Introduction to Text Representation
-
3.2: Label and One Hot Encoding
-
3.3: Bag of Words (BOW)
-
3.4: Bag of n-grams
-
3.5: TF-IDF
-
3.6: Word Embeddings: Theoretical Foundation
-
3.7: Word Embeddings in Spacy
-
3.8: News Classification using Spacy Word Embeddings
-
3.9: Quiz
-
3.10: Exercise
-
3.11: Chapter Summary
Gen AI to Agentic AI with Business Projects
20h:09m:47s on-demand video
|
130 Lectures
3:
Introduction to Generative AI and Agentic AI
9 Lectures
-
3.1: What is Generative AI?
Free -
3.2: Traditional AI vs Gen AI
Free -
3.3: What are AI Agents and Agentic AI?
Free -
3.4: Gen AI vs AI Agents vs Agentic AI
Free -
3.5: Real-world Applications for Gen AI & Agentic AI
-
3.6: Steps to Build Gen AI and Agentic Applications
-
3.7: Quiz
-
3.8: Exercise
-
3.9: Chapter Summary
5:
Gen AI: Langchain and Prompting Essentials
11 Lectures
-
5.1: Elements of a Good Prompt
-
5.2: Zero-Shot, One-Shot, and Few-Shot Prompting
-
5.3: LangChain Installation
-
5.4: Groq and Ollama Setup
-
5.5: Calling LLM from Langchain
-
5.6: Prompt Templates & Chains
-
5.7: Output Parser
-
5.8: Build Financial Data Extraction App
-
5.9: Quiz
-
5.10: Exercise
-
5.11: Chapter Summary
8:
Gen AI: Business Project 2 - E-Commerce Chatbot
11 Lectures
-
8.1: Problem Statement
-
8.2: SOW & Technical Architecture
-
8.3: Implement FAQ Handling
-
8.4: Routing using semantic-router
-
8.5: Streamlit UI: FAQ Handling
-
8.6: SQLite Database Setup
-
8.7: Implement Product Handling: SQL Query Generation
-
8.8: Implement Product Handling: Data Comprehension
-
8.9: Streamlit UI: Product Questions Handling
-
8.10: Bonus: Web Scraping
-
8.11: Exercise
13:
Agentic AI: Business Project 3
9 Lectures
-
13.1: Problem Statement & Tech Architecture
Free -
13.2: HR Management System (HRMS) APIs
-
13.3: Seed Data for HRMS
-
13.4: MCP Tools for Employee Management
-
13.5: Google App Password Setup for Emails
-
13.6: MCP Tools for Emails
-
13.7: MCP Prompt to Onboard a New Employee
-
13.8: MCP Tools for Tickets Management
-
13.9: Exercise
19:
Enterprise Cloud Agent Development: Amazon Bedrock AgentCore
9 Lectures
-
19.1: Introduction & Project Overview
Free -
19.2: Building a Local Agent
Free -
19.3: AWS Account Setup
Free -
19.4: AWS CLI Setup
Free -
19.5: Deploying Agent on AgentCore Runtime
Free -
19.6: Add Memory to Our Agent
Free -
19.7: Overview of Observability & Other Features
Free -
19.8: Quiz
-
19.9: AgentCore Interview Questions
Free
21:
Building Stateful AI Agents with LangGraph
12 Lectures
-
21.1: Introduction
Free -
21.2: LangChain vs LangGraph
Free -
21.3: Environment Setup
Free -
21.4: Build Your First LangGraph
Free -
21.5: Graphs with Conditional Logic
Free -
21.6: Build a Simple Chatbot
Free -
21.7: AI Agents With Tools
Free -
21.8: AI Agents With Memory
Free -
21.9: Tracing With LangSmith
Free -
21.10: Human In The Loop - HITL
Free -
21.11: Quiz
-
21.12: LangGraph Interview Questions
Free
22:
Autonomous Multi-Agent Systems : CrewAI
9 Lectures
-
22.1: Introduction & Setup
Free -
22.2: Building a Simple Agent
Free -
22.3: Agent With Tools
Free -
22.4: Building The Crew
Free -
22.5: Crew With Tool Integration
Free -
22.6: Project Overview And Problem Statement
Free -
22.7: Project Step-by-Step Development
Free -
22.8: Quiz
-
22.9: Crew AI Interview Questions
Free
23:
Bonus: Transformer Architecture
13 Lectures
-
23.1: Introduction to Transformer Architecture
-
23.2: Word Embeddings
-
23.3: Contextual Embeddings
-
23.4: Overview of Encoder and Decoder
-
23.5: Tokenization, Positional Embeddings
-
23.6: Attention Mechanism
-
23.7: Multi Headed Attention
-
23.8: Decoder
-
23.9: How Transformers are Trained?
-
23.10: Hugging Face: BERT Basics
-
23.11: Hugging Face: Spam Classification Using BERT
-
23.12: Hugging Face: Next Word Prediction Using GPT2
-
23.13: Exercise
Start Applying for Jobs -DS
00h:02m:00s on-demand video
|
1 Lectures
Interview Preparation / Job Assistance - DS
00h:02m:55s on-demand video
|
2 Lectures
Virtual Internship
00h:09m:49s on-demand video
|
10 Lectures
2:
Week1
29 Lectures
-
2.1: Welcome Note
-
2.2: Your Onboarding Letter
-
2.3: Welcome Note from Your Manager
-
2.4: Let's Dive Right Into Week 1!
-
2.5: Getting Help From Your Mentors / Seniors
-
2.6: Your First Task
-
2.7: Incoming Task Email 1
-
2.8: Have You Completed This Task?
-
2.9: Quality Check 1
-
2.10: Quality Check 2
-
2.11: Quality Check 3
-
2.12: Quality Check 4
-
2.13: Quality Check 5
-
2.14: Quality Check 6
-
2.15: Quality Check 7
-
2.16: Quality Check 8
-
2.17: Quality Check 9
-
2.18: Quality Check 10
-
2.19: Congratulations, You Have Completed the First Task of Your Internship.
-
2.20: Your Second Task
-
2.21: Incoming Task Email 2
-
2.22: Have You Completed the Assigned Task?
-
2.23: Presentation Submission (Visualization Task)
-
2.24: Congratulations! You Have Completed 2 Tasks in a Row!
-
2.25: You Need Scrum Training
-
2.26: Incoming Task Email 3
-
2.27: Have You Completed the Assigned Task?
-
2.28: Scrum Knowledge Check
-
2.29: Congratulations, You Have Completed Week 1 Successfully.
3:
Week 2
26 Lectures
-
3.1: Let's Dive Right into Week 2!
-
3.2: Can You Handle this Project?
-
3.3: Incoming Task Email 1
-
3.4: Have You Completed the Assigned Task?
-
3.5: Quality Check 1
-
3.6: Quality Check 2
-
3.7: Quality Check 3
-
3.8: Quality Check 4
-
3.9: Quality Check 5
-
3.10: Quality Check 6
-
3.11: Quality Check 7
-
3.12: Quality Check 8
-
3.13: Quality Check 9
-
3.14: Quality Check 10
-
3.15: Congratulations on Finishing This Task!
-
3.16: SQL Query Debugging
-
3.17: Incoming Task Email 2
-
3.18: Quality Check 1
-
3.19: Quality Check 2
-
3.20: Quality Check 3
-
3.21: Quality Check 4
-
3.22: Quality Check 5
-
3.23: Quality Check 6
-
3.24: Quality Check 7
-
3.25: Quality Check 8
-
3.26: Congratulations on Successfully Completing the Task!
4:
Week 3 & 4
26 Lectures
-
4.1: Let's Dive Right into the Final 2 Weeks!
-
4.2: Can You Step Up for a Client Task?
-
4.3: Incoming Task Email: Data Cleaning
-
4.4: Please Don't Share Datasets
-
4.5: Have You Completed the Data Cleaning?
-
4.6: Quality Check 1
-
4.7: Quality Check 2
-
4.8: Quality Check 3
-
4.9: Quality Check 4
-
4.10: Incoming Task Email: Feature Engineering
-
4.11: Have You Completed the Feature Engineering Task?
-
4.12: Quality Check 1
-
4.13: Quality Check 2
-
4.14: Quality Check 3
-
4.15: Quality Check 4
-
4.16: Let's Start Predictive Modeling
-
4.17: Incoming Task Email: Predictive Modeling
-
4.18: Have You Completed the Modeling Task?
-
4.19: Quality Check
-
4.20: Incoming Task Email: MLflow Deployment
-
4.21: Submission of DagsHub link | ML flow
-
4.22: Incoming Task Email: Streamlit App Development
-
4.23: Have You Completed the Streamlit App Development?
-
4.24: Can You Present to Client?
-
4.25: Presentation Submission (CodeX Project)
-
4.26: End Note
Virtual Internship 2
00h:10m:58s on-demand video
|
5 Lectures
1:
Week 5
14 Lectures
-
1.1: Let's Dive Right into Week 5!
-
1.2: DL Project For Our Logistics Client from California
-
1.3: Incoming Task Email 1
-
1.4: Have You Completed the Assigned Task?
-
1.5: Congratulations You Have Finished The First Task Of Week 5!
-
1.6: Incoming Task Email 2
-
1.7: Have You Completed the Assigned Task?
-
1.8: Quality Check 1
-
1.9: Quality Check 2
-
1.10: Congratulations! You Have Completed 2 Tasks in a Row!
-
1.11: Incoming Task Email 3
-
1.12: Have You Completed This Task?
-
1.13: Quality Check
-
1.14: Congratulations, You Have Completed Week 5 Successfully.
2:
Week 6
12 Lectures
-
2.1: Let's Dive Right Into Week 6!
-
2.2: Can We Try an Alternative approach
-
2.3: Incoming Task Email 1
-
2.4: Have You Completed This Task?
-
2.5: Quality Check 1
-
2.6: Quality Check 2
-
2.7: Congratulations on Finishing This Task!
-
2.8: Incoming Task Email 2
-
2.9: Have You Built the Streamlit App for Client Demo
-
2.10: Manage Your GitHub Reporsitory
-
2.11: Have you pushed your code to GitHub?
-
2.12: Congratulations on Successfully Completing the Task!
3:
Week 7
8 Lectures
-
3.1: Let's Dive Right Into Week 7!
-
3.2: Business Context
-
3.3: Incoming Task Email 1
-
3.4: Have you completed this task?
-
3.5: Congratulations you have completed the first task of Week 7
-
3.6: Incoming Task Email 2
-
3.7: Have you completed this task?
-
3.8: Congratulations, You Have Completed Week 7 Successfully.
Get Your Certificate
00:00 on-demand video
|
0 Lectures
AI for Everyone
16h:37m:39s on-demand video
|
67 Lectures
3:
Demystify AI: Machine Learning
10 Lectures
-
3.1: What is Machine Learning
Free -
3.2: Classification vs Regression
Free -
3.3: Supervised vs Unsupervised Learning
Free -
3.4: ML Algorithms Overview
-
3.5: Tooling for ML
-
3.6: Introduction: Building Your Online Credibility on LinkedIn
-
3.7: Task: Register Your Voice on AI
-
3.8: Do This to Get Max Out of This Course
-
3.9: Takeaways & Jargons
-
3.10: Quiz
4:
Demystify AI: Deep Learning
8 Lectures
-
4.1: What is Deep Learning
Free -
4.2: Deep Learning vs Statistical Machine Learning: When to use What?
-
4.3: Credits Scoring to ChatGPT: Overview of Neural Network Architectures
-
4.4: Tooling for Deep Learning
-
4.5: Bank Employee To AI Engineer: Transition Story
-
4.6: Task: Get Engagement by Sharing an AI Learning Resource
-
4.7: Takeaways & Jargons
-
4.8: Quiz
5:
AI/ML Project Lifecycle
15 Lectures
-
5.1: 10 Stages of AI Project Lifecycle
Free -
5.2: Requirements and Scope of Work (SOW)
-
5.3: Data Collection
-
5.4: Data Preparation & Exploratory Data Analysis
-
5.5: Feature Engineering
-
5.6: Model Selection & Training
-
5.7: Model Evaluation Metrics (Accuracy, Prediction, Recall & F1 Score)
-
5.8: Model Evaluation Metrics: When to use which Metric?
-
5.9: Model Fine Tuning
-
5.10: Model Deployment
-
5.11: Deployment & Monitoring Using ML Ops
-
5.12: Online Credibility: Engage Meaningfully
-
5.13: Task: Post About AI/ML Project Steps
-
5.14: Takeaways & Jargons
-
5.15: Quiz
6:
Demystify AI: Gen AI, LLMs and NLP
11 Lectures
-
6.1: What is Generative AI (or Gen AI)?
-
6.2: Evolution of Gen AI Models
-
6.3: What is LLM? Analogy Based Simple Explanation
-
6.4: What is NLP?
-
6.5: Embeddings and Vector Database
-
6.6: Retrieval Augmented Generation (RAG)
-
6.7: Prompt Engineering
-
6.8: Tooling for Gen AI, LLM
-
6.9: Task: Share Your Unique Perspectives on Gen AI
-
6.10: Takeaways & Jargons
-
6.11: Quiz
7:
Data & AI
9 Lectures
-
7.1: The Role of Data in AI
Free -
7.2: Data Infrastructure in a Company
-
7.3: Data Collection: Overview
-
7.4: Data Storage and Transformation: Overview
-
7.5: Data Distribution: Privacy, Ethics & Governance
-
7.6: Online Credibility: Mental Model of Content Creation
-
7.7: Task: Emphasize the Importance of Data in AI
-
7.8: Takeaways & Jargons
-
7.9: Quiz
8:
6 Industry Case Studies
9 Lectures
-
8.1: Text Classification: Support Ticket Prioritization
-
8.2: Image Classification: Crop Yield Detection
-
8.3: RAG-Based Gen AI: ChatGPT for Private Organizational Data
-
8.4: Chatbot: Food Delivery Chatbot
-
8.5: LLM Powered Real Estate Chatbot
-
8.6: Recommendation System: Book Recommendations
-
8.7: Task: Build a Case Study on a Company that leverages AI
-
8.8: Takeaways & Jargons
-
8.9: Quiz
12:
Supplementary Learning (Industry Projects)
7 Lectures
-
12.1: Intro: How to use this supplementary learning
-
12.2: Statistical ML Project: Build a House Prediction System
-
12.3: Deep Learning Project: Build a Potato Disease Classification System
-
12.4: NLP Project: Build a chatbot using Dialog Flow
-
12.5: Gen AI Project: Build a Q & A system in retail domain
-
12.6: Gen AI Project: Build a news research tool in finance domain
-
12.7: Quiz
SQL Beginner to Advanced For Data Professionals
11h:39m:30s on-demand video
|
86 Lectures
5:
SQL Basics: Data Retrieval - Single Table
15 Lectures
-
5.1: Install MySQL: Windows
Free -
5.2: Install MySQL: Linux, Mac
Free -
5.3: Import Movies Dataset in MySQL
Free -
5.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.10: HAVING Clause
Free -
5.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.13: The Data God’s Blessing
Free -
5.14: Quiz
-
5.15: Chapter Summary
8:
SQL Basics: Database Creation & Updates
18 Lectures
-
8.1: Database Normalization and Data Integrity
-
8.2: Entity Relationship Diagram (ERD)
-
8.3: Mentor Talk: Art of Googling
-
8.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
-
8.5: Data Types: String (VARCHAR, CHAR, ENUM)
-
8.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
-
8.7: Data Types: JSON, Spatial (JSON, GEOMETRY)
-
8.8: Luck Favors the LinkedIn Post
-
8.9: Primary key
-
8.10: Foreign Key
-
8.11: Create a Database From an Entity Relationship Diagram - ERD
-
8.12: Import Data From a CSV File Into a Database
-
8.13: Insert Statement
-
8.14: Update and Delete
-
8.15: I Need a Favour
-
8.16: Expect the Unexpected: The Intermission Scene
-
8.17: Quiz
-
8.18: Chapter Summary
9:
AtliQ Hardware & Problem Statement
9 Lectures
-
9.1: The Rise of Databases at AtliQ
Free -
9.2: Relational vs No-SQL Database
-
9.3: AtliQ Hardware’s Business Model
-
9.4: Profit & Loss Statement
-
9.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog
-
9.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import
-
9.7: Simplified: What is Kanban?
-
9.8: Quiz
-
9.9: Chapter Summary
10:
SQL Advanced: Finance Analytics
10 Lectures
-
10.1: Backlog Grooming Meeting: Gross Sales Report
-
10.2: User-Defined SQL Functions
-
10.3: Exercise: User-Defined SQL Functions
-
10.4: Gross Sales Report: Monthly Product Transactions
-
10.5: Gross Sales Report: Total Sales Amount
-
10.6: Exercise: Yearly Sales Report
-
10.7: Stored Procedures: Monthly Gross Sales Report
-
10.8: Stored Procedure: Market Badge
-
10.9: Benefits of Stored Procedures
-
10.10: Quiz
11:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
11.1: Problem Statement and Pre-Invoice Discount Report
-
11.2: Performance Improvement # 1
-
11.3: Performance Improvement # 2
-
11.4: Database Views: Introduction
-
11.5: Database Views: Post Invoice Discount, Net Sales
-
11.6: Exercise: Database Views
-
11.7: Top Markets and Customers
-
11.8: Exercise: Top Products
-
11.9: The Two Most Important Skills for the Data Analyst
-
11.10: Window Functions: OVER Clause
-
11.11: Window Functions: Using it in a Task
-
11.12: Exercise: Window Functions: OVER Clause
-
11.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.14: Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.15: 5 Ways SQL is Used in the Industry
-
11.16: Quiz
13:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
13.1: Supply Chain Basics : Simplified
-
13.2: Problem Statement
-
13.3: Create a Helper Table
-
13.4: Database Triggers
-
13.5: Database Events
-
13.6: Temporary Tables & Forecast Accuracy Report
-
13.7: Exercise: CTE, Temporary Tables
-
13.8: Subquery vs CTE vs Views vs Temporary Table
-
13.9: User Accounts and Privileges
-
13.10: Database Indexes: Overview
-
13.11: Database Indexes: Composite Index
-
13.12: Database Indexes: Index Types
-
13.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
13.14: Quiz
Live Webinars
40h:39m:20s on-demand video
|
29 Lectures
4:
Career Development Session
5 Lectures
-
4.1: Beginners Guide to Job Seeking - Sep 23
-
4.2: 6 Free Internet Tools to Get an Interview Call - Oct 23
-
4.3: Smart Job Assistance Portal & Expert Resume Insights
-
4.4: The Secret Behind Resumes and Portfolios That Landed Jobs: Decode with your Talent Manager
-
4.5: Strategic Job Search with Google, LinkedIn and Naukri - 22nd November
6:
Expert Webinars
11 Lectures
-
6.1: Freelancing in Data Analytics & Building Your Credibility on LinkedIn - By Zain Altaf
-
6.2: The Practical Power BI Workflow and UAT Process Every Analyst Should Know - By Trilochan Tripathy
-
6.3: Transitioning from Non-Tech to Data Analytics - Journey and Tips by Shail Sahu
-
6.4: PL-300 Certification: What You Need to Know & How to Prepare - Anmol Malviya
-
6.5: How to Approach Scenario-Based Questions and Guesstimates in the Interviews - Shashank Singh
-
6.6: Tips and Tricks to Approach Data Analyst Interviews - Gaurav Agrawal
-
6.7: How I would prepare for Data Analyst Interviews If I had to start over - Munna Das
-
6.8: Key Lessons and Interview Tips from My Journey as a Data Analyst - Bharath Kumar G
-
6.9: My Life as a Data Analyst at Ford Motors- Raghavan P
-
6.10: Data Analytics Freelancing Essentials - Santhanalakshmi Ponnurasan
-
6.11: How to differentiate your work - Ashish Babaria
Live Problem-Solving Sessions
04h:40m:35s on-demand video
|
4 Lectures
Personal Branding (LinkedIn & Beyond) for All Professionals
02h:06m:45s on-demand video
|
38 Lectures
7:
Create Your Own Posts
9 Lectures
-
7.1: Mental Model of Content Creation
-
7.2: 6 Fundamental ways to create a post with real examples
-
7.3: Effective Template for Posting
-
7.4: 10 plug-n-play post templates
-
7.5: Treating your comments like Posts
-
7.6: I need a favor
-
7.7: Quiz
-
7.8: Activity: Create Your First Post
-
7.9: Activity: Write 3 comments like a Post
12:
Burning Questions
12 Lectures
-
12.1: I don’t get any engagements in my posts, what should I do?
-
12.2: Is spending this much time and being active on LinkedIn worth it?
-
12.3: I’m not an expert—what do I even talk about?
-
12.4: Is LinkedIn Premium required to grow on LinkedIn?
-
12.5: Do I really need a personal brand if I’m not trying to become an influencer?
-
12.6: Isn’t LinkedIn just for job seekers? I’m not looking for a new job.
-
12.7: How long does it take before I start seeing results?
-
12.8: What if my current employer doesn’t like me being active on LinkedIn?
-
12.9: Can I build a brand if I’m a freelancer/consultant and not in a full-time role?
-
12.10: How do I create content when I don’t have time?
-
12.11: Is it necessary to create content, or can I build a brand just by engaging with others?
-
12.12: What should I do if I receive negative comments or criticism on my posts?
Practice Room: Python for Gen AI & Data Science
00h:02m:00s on-demand video
|
0 Lectures
Practice Room: Math & Stats for Gen AI & Data Science
00:00 on-demand video
|
0 Lectures
Practice Room: Machine Learning
00:00 on-demand video
|
0 Lectures
Practice Room: Deep Learning
00:00 on-demand video
|
0 Lectures
Practice Room: NLP for Gen AI & Data Science
00:00 on-demand video
|
0 Lectures
Practice Arena
1:
DS SQL Practice Room
SQL
7
Questions
-
1.1: Deduplication – Latest Record
-
1.2: Window Functions – Nth Highest Salary
-
1.3: Gaps & Islands – Login Streak
-
1.4: Cohort Retention Table Analysis
-
1.5: MoM Growth
-
1.6: Overlapping Bookings
-
1.7: Percentiles – 95th Percentile Latency
Welcome to The Gen AI and Data Science Bootcamp Experience
01h:55m:15s on-demand video
|
21
Lectures
01h:55m:15s on-demand video
|
21 Lectures
Live Sessions
03h:58m:14s on-demand video
|
2
Lectures
03h:58m:14s on-demand video
|
2 Lectures
Python: Beginner to Advanced For Data Professionals
17h:39m:41s on-demand video
|
114
Lectures
17h:39m:41s on-demand video
|
114 Lectures
2:
Welcome to the Python Experience
7 Lectures
-
2.1: Course Introduction
Free -
2.2: What Benefits You Get By Learning Python?
Free -
2.3: Why Python is so Popular?
Free -
2.4: Who can Enroll In This Course? Any Prerequisites?
Free -
2.5: How is This Different From Free Codebasics YouTube Playlist?
Free -
2.6: What Kind Of Computer Do I Need For This Course?
Free -
2.7: Course Content Overview
Free
9:
Python Basics: Functions, Dictionaries, Tuples and File Handling
9 Lectures
-
9.1: Functions
-
9.2: Dictionary and Tuples
-
9.3: Modules and Pip
-
9.4: File Handling
-
9.5: Quiz: Functions, Dictionaries, Tuples and File Handling
-
9.6: Peter’s Request to Tony
-
9.7: Exercise: Functions, Dictionaries, Tuples and File Handling
-
9.8: Two Deadly Viruses Infecting Learners
-
9.9: Chapter Summary
19:
Project 2: Expense Tracking System
11 Lectures
-
19.1: Problem Statement & Tech Architecture
-
19.2: Database CRUD Operations
-
19.3: Automated Tests Setup for CRUD
-
19.4: Expense Management: Backend (FastAPI)
-
19.5: Expense Management: Logging
-
19.6: Streamlit Introduction
-
19.7: Expense Management: Frontend (Streamlit)
-
19.8: Analytics: Backend (FastAPI)
-
19.9: Analytics: Frontend (Streamlit)
-
19.10: README and Requirements.txt
-
19.11: Exercise
24:
Bonus Medical Data Extraction Project: Prescription Document
10 Lectures
-
24.1: Technical Architecture of the Project
Free -
24.2: Installation of Necessary Libraries
-
24.3: Extract text from a pdf document
-
24.4: Thresholding in OpenCV
-
24.5: Regular Expressions or Regex
-
24.6: Regex Exercise
-
24.7: Python class for prescription
-
24.8: Code Refactoring
-
24.9: Unit Tests using pytest
-
24.10: I Need a Favour
Online Credibility
00h:24m:12s on-demand video
|
4
Lectures
00h:24m:12s on-demand video
|
4 Lectures
Build In Public
03h:04m:40s on-demand video
|
5
Lectures
03h:04m:40s on-demand video
|
5 Lectures
SQL for Data Science
02h:02m:08s on-demand video
|
86
Lectures
02h:02m:08s on-demand video
|
86 Lectures
5:
SQL Basics: Data Retrieval - Single Table
14 Lectures
-
5.1: Install MySQL: Windows
Free -
5.2: Install MySQL: Linux, Mac
Free -
5.3: Import Movies Dataset in MySQL
Free -
5.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.10: HAVING Clause
Free -
5.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.13: The Data God’s Blessing
Free -
5.14: Quiz
8:
SQL Basics: Database Creation & Updates
17 Lectures
-
8.1: Database Normalization and Data Integrity
-
8.2: Entity Relationship Diagram (ERD)
-
8.3: Mentor Talk: Art of Googling
-
8.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
-
8.5: Data Types: String (VARCHAR, CHAR, ENUM)
-
8.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
-
8.7: Data Types: JSON, Spatial (JSON, GEOMETRY)
-
8.8: Luck Favors the LinkedIn Post
-
8.9: Primary key
-
8.10: Foreign Key
-
8.11: Create a Database From an Entity Relationship Diagram - ERD
-
8.12: Import Data From a CSV File Into a Database
-
8.13: Insert Statement
-
8.14: Update and Delete
-
8.15: I Need a Favour
-
8.16: Expect the Unexpected: The Intermission Scene
-
8.17: Quiz
9:
AtliQ Hardware & Problem Statement
8 Lectures
-
9.1: The Rise of Databases at AtliQ
Free -
9.2: Relational vs No-SQL Database
-
9.3: AtliQ Hardware’s Business Model
-
9.4: Profit & Loss Statement
-
9.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog
-
9.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import
-
9.7: Simplified: What is Kanban?
-
9.8: Quiz
10:
SQL Advanced: Finance Analytics
10 Lectures
-
10.1: Backlog Grooming Meeting: Gross Sales Report
-
10.2: User-Defined SQL Functions
-
10.3: Exercise: User-Defined SQL Functions
-
10.4: Gross Sales Report: Monthly Product Transactions
-
10.5: Gross Sales Report: Total Sales Amount
-
10.6: Exercise: Yearly Sales Report
-
10.7: Stored Procedures: Monthly Gross Sales Report
-
10.8: Stored Procedure: Market Badge
-
10.9: Benefits of Stored Procedures
-
10.10: Quiz
11:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
11.1: Problem Statement and Pre-Invoice Discount Report
-
11.2: Performance Improvement # 1
-
11.3: Performance Improvement # 2
-
11.4: Database Views: Introduction
-
11.5: Database Views: Post Invoice Discount, Net Sales
-
11.6: Exercise: Database Views
-
11.7: Top Markets and Customers
-
11.8: Exercise: Top Products
-
11.9: The Two Most Important Skills for the Data Analyst
-
11.10: Window Functions: OVER Clause
-
11.11: Window Functions: Using it in a Task
-
11.12: Exercise: Window Functions: OVER Clause
-
11.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.14: Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.15: 5 Ways SQL is Used in the Industry
-
11.16: Quiz
12:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
12.1: Supply Chain Basics : Simplified
-
12.2: Problem Statement
-
12.3: Create a Helper Table
-
12.4: Database Triggers
-
12.5: Database Events
-
12.6: Temporary Tables & Forecast Accuracy Report
-
12.7: Exercise: CTE, Temporary Tables
-
12.8: Subquery vs CTE vs Views vs Temporary Table
-
12.9: User Accounts and Privileges
-
12.10: Database Indexes: Overview
-
12.11: Database Indexes: Composite Index
-
12.12: Database Indexes: Index Types
-
12.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
12.14: Quiz
Math and Statistics For AI, Data Science
12h:49m:38s on-demand video
|
98
Lectures
12h:49m:38s on-demand video
|
98 Lectures
1:
Welcome to Math and Statistics Experience
7 Lectures
-
1.1: Importance of Math and Stats in Data Science Career
Free -
1.2: How Is This Course Different from Other Courses on the Internet?
Free -
1.3: What Support Do You Provide If I Have Questions?
Free -
1.4: Who Should Take This Course?
Free -
1.5: Pre-requisites
Free -
1.6: System Requirements
Free -
1.7: Course Overview
Free
5:
Pandas, Matplotlib and Seaborn Basics
8 Lectures
-
5.1: Pandas Introduction and Installation
Free -
5.2: Dataframe Basics
Free -
5.3: Read, Write Excel and CSV Files
Free -
5.4: Handle Missing Data - Part 1
Free -
5.5: Handle Missing Data - Part 2
Free -
5.6: Grouping Data
Free -
5.7: Data Concatenation and Merging
Free -
5.8: Data Visualization with Matplotlib and Seaborn
Free
6:
Measures Of Central Tendency and Dispersion
18 Lectures
-
6.1: Descriptive vs. Inferential Statistics
Free -
6.2: Measures of Central Tendency: Mean, Median, Mode
Free -
6.3: Percentile
-
6.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
6.5: Quiz
-
6.6: Exercise
-
6.7: Measures of Dispersion: Range, IQR
-
6.8: Box or Whisker Plot
-
6.9: Outlier Treatment Using IQR and Box Plot
-
6.10: Quiz
-
6.11: Exercise
-
6.12: Measures of Dispersion: Variance and Standard Deviation
-
6.13: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
6.14: Correlation
-
6.15: Correlation vs Causation
-
6.16: Quiz
-
6.17: Exercise
-
6.18: Chapter Summary
10:
Phase 1: Find Target Market
21 Lectures
-
10.1: Data Validation Of Acquired Data
-
10.2: Data Understanding, MySQL Setup
-
10.3: Data Import in Jupyter Notebook
-
10.4: Data Cleaning: Handle NULL Values (Annual Income)
-
10.5: Data Cleaning: Treat Outliers (Annual Income)
-
10.6: Data Visualization: Annual Income
-
10.7: Exercise: Treat Outliers in Age Column
-
10.8: Exercise Solution: Treat Outliers in Age Column
-
10.9: Data Visualization: Age, Gender, Location
-
10.10: Peter’s Nightmare
-
10.11: Data Cleaning: Credit Score Table - Part 1
-
10.12: Data Cleaning: Credit Score Table - Part 2
-
10.13: Correlation among Credit Profile Variables
-
10.14: Exercise: Handle NULL Values in Transactions Table
-
10.15: Exercise Solution: Handle NULL Values in Transactions Table
-
10.16: Peter’s Confusion: IQR or Std Dev?
-
10.17: Data Cleaning: Treat Outliers using IQR (Transaction Amount)
-
10.18: Data Visualization: Transactions Table
-
10.19: Finalize the Target Group
-
10.20: Phase 1 Feedback Meeting With Stakeholders
-
10.21: Get Ready For Phase 2
11:
Central Limit Theorem
12 Lectures
-
11.1: Random Sampling & Sample Bias
-
11.2: The Law of Large Numbers
-
11.3: Central Limit Theorem, Sampling Distribution
-
11.4: Case Study: Solar Panels
-
11.5: Standard Error
-
11.6: Quiz
-
11.7: Z Score Table (Z-Table)
-
11.8: Quiz
-
11.9: Confidence Interval
-
11.10: Confidence Interval: Estimate Car Miles
-
11.11: Exercise
-
11.12: Chapter Summary
12:
Hypothesis Testing
19 Lectures
-
12.1: Null vs Alternate Hypothesis
-
12.2: Z Test, Rejection Region
-
12.3: Housing Inflation Test: Rejection Region
-
12.4: Quiz
-
12.5: Exercise
-
12.6: p-Value
-
12.7: Housing Inflation Test: p-Value
-
12.8: Quiz
-
12.9: Exercise
-
12.10: One-Tailed vs Two-Tailed Test
-
12.11: Type 1 and Type 2 Errors
-
12.12: Quiz
-
12.13: Statistical Power & Effect Size
-
12.14: A/B Testing
-
12.15: A/B Testing Using Z Test
-
12.16: A/B Testing: Drug Trial
-
12.17: Quiz
-
12.18: Exercise
-
12.19: Chapter Summary
Online Credibility & Domain Knowledge Course
00h:31m:00s on-demand video
|
5
Lectures
00h:31m:00s on-demand video
|
5 Lectures
Master Machine Learning for Data Science & AI: Beginner to Advanced
22h:41m:19s on-demand video
|
201
Lectures
22h:41m:19s on-demand video
|
201 Lectures
4:
Python Basics
43 Lectures
-
4.1: MUST WATCH: Go through this chapter ONLY IF
-
4.2: Skip This Chapter - Quiz
-
4.3: Setup Environment (Local Python and Google Colab)
-
4.4: Variables
-
4.5: Variables - Quiz
-
4.6: Variables - Exercise
-
4.7: Numbers
-
4.8: Numbers - Quiz
-
4.9: Numbers- Exercise
-
4.10: Strings
-
4.11: Strings - Quiz
-
4.12: Strings - Exercise
-
4.13: Lists
-
4.14: Lists - Quiz
-
4.15: Lists - Exercise
-
4.16: Install Pycharm
-
4.17: If Condition
-
4.18: If Condition - Quiz
-
4.19: If Condition - Exercise
-
4.20: For Loop
-
4.21: For loop - Quiz
-
4.22: For loop - Exercise
-
4.23: Functions
-
4.24: Functions -Quiz
-
4.25: Functions - Exercise
-
4.26: Dictionary and Tuples
-
4.27: Dictionary and Tuples - Quiz
-
4.28: Dictionary and Tuples - Exercise
-
4.29: Modules and Pip
-
4.30: Modules and Pip - Quiz
-
4.31: Modules and Pip - Exercise
-
4.32: File Handling
-
4.33: File Handling - Quiz
-
4.34: File handling - Exercise
-
4.35: Classes and Objects
-
4.36: Classes and Objects - Quiz
-
4.37: Classes and Objects - Exercise
-
4.38: Inheritance
-
4.39: Inheritance - Quiz
-
4.40: Inheritance - Exercise
-
4.41: Exception Handling
-
4.42: Exception Handling - Quiz
-
4.43: Exception Handling - Exercise
6:
Data preprocessing and Visualization
12 Lectures
-
6.1: MUST WATCH: Go through this chapter ONLY IF
-
6.2: Skip This Chapter - Quiz
-
6.3: Pandas Introduction and Installation
-
6.4: Dataframe Basics
-
6.5: Read, Write Excel and CSV Files
-
6.6: Handle Missing Data - Part 1
-
6.7: Handle Missing Data - Part 2
-
6.8: Grouping Data
-
6.9: Data Concatenation and Merging
-
6.10: Data Visualization Using Matplotlib and Seaborn
-
6.11: Data God Showing the way
-
6.12: Quiz
7:
Math & Statistics for Data Science, AI
46 Lectures
-
7.1: MUST WATCH: Go through this chapter ONLY IF
-
7.2: Skip This Chapter - Quiz
-
7.3: Descriptive vs. Inferential Statistics
-
7.4: Measures of Central Tendency: Mean, Median, Mode
-
7.5: Percentile
-
7.6: Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
7.7: Quiz
-
7.8: Exercise
-
7.9: Measures of Dispersion: Range, IQR
-
7.10: Box or Whisker Plot
-
7.11: Outlier Treatment Using IQR and Box Plot
-
7.12: Quiz
-
7.13: Exercise
-
7.14: Measures of Dispersion: Variance and Standard Deviation
-
7.15: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
7.16: Correlation
-
7.17: Correlation vs Causation
-
7.18: Quiz
-
7.19: Exercise
-
7.20: Probability Basics
-
7.21: Quiz
-
7.22: Addition and Multiplication Rule
-
7.23: Quiz
-
7.24: Conditional Probability and Bayes Theorem
-
7.25: Quiz
-
7.26: What Is a Distribution?
-
7.27: Skewness
-
7.28: Normal Distribution
-
7.29: Detect Outliers Using Normal Distribution
-
7.30: Quiz
-
7.31: Exercise
-
7.32: Z Score
-
7.33: Standard Normal Distribution (SND)
-
7.34: Quiz
-
7.35: Exercise
-
7.36: Random Sampling & Sample Bias
-
7.37: The Law of Large Numbers
-
7.38: Central Limit Theorem, Sampling Distribution
-
7.39: Case Study: Solar Panels
-
7.40: Standard Error
-
7.41: Quiz
-
7.42: Z Score Table (Z-Table)
-
7.43: Quiz
-
7.44: Confidence Interval
-
7.45: Confidence Interval: Estimate Car Miles
-
7.46: Exercise
8:
Supervised Machine Learning: Regression
29 Lectures
-
8.1: Simple Linear Regression
Free -
8.2: Multiple Linear Regression
Free -
8.3: Quiz
-
8.4: Exercise
-
8.5: Cost Function
-
8.6: Derivatives and Partial Derivatives
-
8.7: Chain Rule
-
8.8: Quiz
-
8.9: Exercise
-
8.10: Gradient Descent Theory
-
8.11: Gradient Descent: Python Implementation
-
8.12: Why MSE (and not MAE)?
-
8.13: Model Evaluation: Train, Test Split
-
8.14: Model Evaluation: Metrics
-
8.15: Peter Pandey Flexes his ML skills on LinkedIn
-
8.16: Quiz
-
8.17: Exercise
-
8.18: Data Preprocessing: One Hot Encoding
-
8.19: Quiz
-
8.20: Polynomial Regression
-
8.21: Quiz
-
8.22: Exercise
-
8.23: Overfitting and Underfitting
-
8.24: Reasons and Remedies For Overfitting / Underfitting
-
8.25: L1 and L2 Regularization
-
8.26: Bias Variance Trade Off
-
8.27: Quiz
-
8.28: Exercise
-
8.29: Chapter Summary
9:
Supervised Machine Learning: Classification
31 Lectures
-
9.1: Introduction to Classification
Free -
9.2: Logistic Regression: Binary Classification
Free -
9.3: Model Evaluation: Accuracy, Precision and Recall
-
9.4: Quiz
-
9.5: Exercise
-
9.6: Model Evaluation: F1 Score, Confusion Matrix
-
9.7: Logistic Regression: Multiclass Classification
-
9.8: Cost Function: Log Loss
-
9.9: Quiz
-
9.10: Exercise
-
9.11: Support Vector Machine (SVM)
-
9.12: Data Pre-processing: Scaling
-
9.13: Sklearn Pipeline
-
9.14: Quiz (disabled)
-
9.15: Quiz
-
9.16: Exercise
-
9.17: Naive Bayes: Theory
-
9.18: Naive Bayes: SMS Spam Classification
-
9.19: Quiz
-
9.20: Exercise
-
9.21: Decision Tree: Theory
-
9.22: Decision Tree: Salary Classification
-
9.23: I Need a Favour
-
9.24: Quiz
-
9.25: Exercise
-
9.26: Handle Class Imbalance: Theory
-
9.27: Handle Class Imbalance Using imblearn: Churn Prediction
-
9.28: Quiz
-
9.29: Exercise
-
9.30: Get inspired by Peter Pandey
-
9.31: Chapter Summary
10:
Ensemble Learning
21 Lectures
-
10.1: What is Ensemble Learning?
Free -
10.2: Majority Voting, Average and Weighted Average
-
10.3: Bagging
-
10.4: Bagging: Random Forest
-
10.5: Random Forest: Raisin Classification
-
10.6: Quiz
-
10.7: Exercise
-
10.8: Boosting: AdaBoost
-
10.9: Gradient Boosting: Regression Walk Through
-
10.10: Gradient Boosting: Regression Math
-
10.11: Gradient Boosting: Revenue Prediction
-
10.12: Quiz
-
10.13: Exercise
-
10.14: Gradient Boosting: Classification
-
10.15: XGBoost: Walk Through
-
10.16: XGBoost: California Housing Prediction
-
10.17: XGBoost: Synthetic Data Classification
-
10.18: XGBoost: Benefits
-
10.19: Quiz
-
10.20: Exercise
-
10.21: Chapter Summary
11:
Model Evaluation & Fine Tuning
16 Lectures
-
11.1: Introduction
-
11.2: Model Evaluation: ROC Curve & AUC
-
11.3: Cost Benefit Analysis Using ROC in Sklearn
-
11.4: Quiz
-
11.5: Exercise
-
11.6: K Fold Cross Validation
-
11.7: Stratified K Fold Cross Validation
-
11.8: Hyperparameter Tuning: GridsearchCV
-
11.9: Hyperparameter Tuning: RandomizedSearchCV
-
11.10: Quiz
-
11.11: Exercise
-
11.12: Model Selection Guide
-
11.13: Luck favors the LinkedIn post
-
11.14: Selecting the Right Evaluation Metric
-
11.15: Quiz
-
11.16: Chapter Summary
12:
ML Project Life Cycle
10 Lectures
-
12.1: 10 Stages of AI Project Life Cycle
Free -
12.2: Requirements and Scope of Work (SOW)
Free -
12.3: Data Collection
-
12.4: Data Cleaning & Exploratory Data Analysis
-
12.5: Feature Engineering
-
12.6: Model Selection & Training
-
12.7: Model Fine Tuning
-
12.8: Model Deployment
-
12.9: Monitoring and Feedback Using ML Ops
-
12.10: Chapter Summary
14:
Unsupervised Learning
13 Lectures
-
14.1: Introduction
-
14.2: K Means Clustering: Theory
-
14.3: K Means Clustering: Customer Segmentation
-
14.4: Hierarchical Clustering: Theory
-
14.5: Hierarchical Clustering: Customer Segmentation
-
14.6: Quiz
-
14.7: Exercise
-
14.8: DBSCAN: Theory
-
14.9: DBSCAN: Practical Implementation
-
14.10: Peter AI
-
14.11: Quiz
-
14.12: Exercise
-
14.13: Chapter Summary
15:
Project 1: Healthcare Premium Prediction (Regression)
16 Lectures
-
15.1: The Rise of AtliQ AI
Free -
15.2: Project Charter Meeting
Free -
15.3: Scope of Work, Task Planning in JIRA
-
15.4: Data Collection
-
15.5: Data Cleaning & EDA - Part 1
-
15.6: Data Cleaning & EDA - Part 2
-
15.7: Feature Engineering
-
15.8: Model Training, Fine Tunning
-
15.9: 98% Model Accuracy, Really?
-
15.10: Error Analysis
-
15.11: Model Segmentation
-
15.12: Request More Data
-
15.13: Model Retraining
-
15.14: Build App Using Streamlit
-
15.15: Deployment
-
15.16: Exercise
16:
Project 2: Credit Risk Modelling (Classification)
19 Lectures
-
16.1: Peter's Promotion: New Project
Free -
16.2: Domain Understanding: NBFC & Credit Approvals
Free -
16.3: Scope of Work & Tech Architecture
Free -
16.4: Data Collection
-
16.5: Quick Intro to Data Leakage
-
16.6: Data Cleaning
-
16.7: Exploratory Data Analysis (EDA)
-
16.8: Feature Engineering – Part 1
-
16.9: Weight of Evidence (WOE), Information Value (IV)
-
16.10: Feature Engineering – Part 2
-
16.11: Model Training & Evaluation
-
16.12: Introduction to Optuna
-
16.13: Model Fine Tuning Using Optuna
-
16.14: Intro To Rank Ordering & KS Statistic
-
16.15: Model Evaluation Using KS Statistic & Gini Coefficient
-
16.16: Streamlit App
-
16.17: Business Presentation
-
16.18: Deployment
-
16.19: Exercise
17:
ML Ops & Cloud Tools
22 Lectures
-
17.1: What is ML Ops?
Free -
17.2: Importance of ML Ops in Your Career
Free -
17.3: ML Flow: Purpose and Overview
Free -
17.4: ML Flow: Experiment Tracking
-
17.5: ML Flow: Model Registry
-
17.6: ML Flow: Centralized Server Using Dagshub
-
17.7: Quiz
-
17.8: What is API?
-
17.9: FastAPI Basics
-
17.10: Build FastAPI Server For Credit Risk Project
-
17.11: Quiz
-
17.12: Git Version Control System
-
17.13: Introduction to ML Cloud Platforms
-
17.14: AWS Sagemaker: Account Setup
-
17.15: AWS Sagemaker: Sagemaker Studio
-
17.16: AWS Sagemaker: 4 Ways to Train Model
-
17.17: AWS Sagemaker: Built In Algorithms
-
17.18: AWS Sagemaker: Script Mode
-
17.19: Quiz
-
17.20: Data Drift Detection Using PSI & CSI
-
17.21: PSI & CSI: Practical Implementation
-
17.22: Quiz
Job Assistance Portal, ATS Resume & Portfolio Website
01h:23m:48s on-demand video
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14
Lectures
01h:23m:48s on-demand video
|
14 Lectures
Deep Learning: Beginner to Advanced
13h:10m:01s on-demand video
|
85
Lectures
13h:10m:01s on-demand video
|
85 Lectures
3:
Getting Started
10 Lectures
-
3.1: Who is Peter Pandey?
Free -
3.2: Peter Pandey’s journey to learn Deep Learning?
Free -
3.3: Neural Networks: The Foundation of Deep Learning
Free -
3.4: Deep Learning vs Statistical ML: When to Use What?
-
3.5: Neural Network Architectures
-
3.6: Real World Applications of Deep Learning
-
3.7: Tooling: PyTorch vs Tensorflow
-
3.8: Tooling: GPU, TPU
-
3.9: Quiz
-
3.10: Chapter Summary
11:
Convolutional Neural Networks (CNN)
10 Lectures
-
11.1: What is CNN? Convolution, Kernels, Pooling and Beyond
-
11.2: Padding and Strides
-
11.3: CIFAR10 Image Classification using CNN
-
11.4: Data Augmentation
-
11.5: Transfer Learning
-
11.6: Pre-trained Models – ResNet, EfficientNet, MobileNet etc.
-
11.7: Caltech101 Classification Using Transfer Learning
-
11.8: Quiz
-
11.9: Exercise
-
11.10: Chapter Summary
13:
Transformers
15 Lectures
-
13.1: Introduction to Transformer Architecture
-
13.2: Word Embeddings
-
13.3: Contextual Embeddings
-
13.4: Overview of Encoder and Decoder
-
13.5: Tokenization, Positional Embeddings
-
13.6: Attention Mechanism
-
13.7: Multi Headed Attention
-
13.8: Decoder
-
13.9: How Transformers are Trained?
-
13.10: Hugging Face: BERT Basics
-
13.11: Hugging Face: Spam Classification Using BERT
-
13.12: Hugging Face: Next Word Prediction Using GPT2
-
13.13: Quiz
-
13.14: Exercise
-
13.15: Chapter Summary
14:
Project: Car Damage Detection
10 Lectures
-
14.1: AtliQ AI’s First Big Client: Vroom Cars
-
14.2: Problem Statement & SOW
-
14.3: Data Load and Transformation
-
14.4: Model Training with CNN
-
14.5: Model Training with CNN and Regularization
-
14.6: Model Training using Transfer Learning
-
14.7: Hyperparameter Tunning using Optuna
-
14.8: Model Evaluation and Export
-
14.9: Streamlit App
-
14.10: FastAPI Server
Natural Language Processing
06h:59m:43s on-demand video
|
27
Lectures
06h:59m:43s on-demand video
|
27 Lectures
3:
Text Representation
11 Lectures
-
3.1: Introduction to Text Representation
-
3.2: Label and One Hot Encoding
-
3.3: Bag of Words (BOW)
-
3.4: Bag of n-grams
-
3.5: TF-IDF
-
3.6: Word Embeddings: Theoretical Foundation
-
3.7: Word Embeddings in Spacy
-
3.8: News Classification using Spacy Word Embeddings
-
3.9: Quiz
-
3.10: Exercise
-
3.11: Chapter Summary
Gen AI to Agentic AI with Business Projects
20h:09m:47s on-demand video
|
130
Lectures
20h:09m:47s on-demand video
|
130 Lectures
3:
Introduction to Generative AI and Agentic AI
9 Lectures
-
3.1: What is Generative AI?
Free -
3.2: Traditional AI vs Gen AI
Free -
3.3: What are AI Agents and Agentic AI?
Free -
3.4: Gen AI vs AI Agents vs Agentic AI
Free -
3.5: Real-world Applications for Gen AI & Agentic AI
-
3.6: Steps to Build Gen AI and Agentic Applications
-
3.7: Quiz
-
3.8: Exercise
-
3.9: Chapter Summary
5:
Gen AI: Langchain and Prompting Essentials
11 Lectures
-
5.1: Elements of a Good Prompt
-
5.2: Zero-Shot, One-Shot, and Few-Shot Prompting
-
5.3: LangChain Installation
-
5.4: Groq and Ollama Setup
-
5.5: Calling LLM from Langchain
-
5.6: Prompt Templates & Chains
-
5.7: Output Parser
-
5.8: Build Financial Data Extraction App
-
5.9: Quiz
-
5.10: Exercise
-
5.11: Chapter Summary
8:
Gen AI: Business Project 2 - E-Commerce Chatbot
11 Lectures
-
8.1: Problem Statement
-
8.2: SOW & Technical Architecture
-
8.3: Implement FAQ Handling
-
8.4: Routing using semantic-router
-
8.5: Streamlit UI: FAQ Handling
-
8.6: SQLite Database Setup
-
8.7: Implement Product Handling: SQL Query Generation
-
8.8: Implement Product Handling: Data Comprehension
-
8.9: Streamlit UI: Product Questions Handling
-
8.10: Bonus: Web Scraping
-
8.11: Exercise
13:
Agentic AI: Business Project 3
9 Lectures
-
13.1: Problem Statement & Tech Architecture
Free -
13.2: HR Management System (HRMS) APIs
-
13.3: Seed Data for HRMS
-
13.4: MCP Tools for Employee Management
-
13.5: Google App Password Setup for Emails
-
13.6: MCP Tools for Emails
-
13.7: MCP Prompt to Onboard a New Employee
-
13.8: MCP Tools for Tickets Management
-
13.9: Exercise
19:
Enterprise Cloud Agent Development: Amazon Bedrock AgentCore
9 Lectures
-
19.1: Introduction & Project Overview
Free -
19.2: Building a Local Agent
Free -
19.3: AWS Account Setup
Free -
19.4: AWS CLI Setup
Free -
19.5: Deploying Agent on AgentCore Runtime
Free -
19.6: Add Memory to Our Agent
Free -
19.7: Overview of Observability & Other Features
Free -
19.8: Quiz
-
19.9: AgentCore Interview Questions
Free
21:
Building Stateful AI Agents with LangGraph
12 Lectures
-
21.1: Introduction
Free -
21.2: LangChain vs LangGraph
Free -
21.3: Environment Setup
Free -
21.4: Build Your First LangGraph
Free -
21.5: Graphs with Conditional Logic
Free -
21.6: Build a Simple Chatbot
Free -
21.7: AI Agents With Tools
Free -
21.8: AI Agents With Memory
Free -
21.9: Tracing With LangSmith
Free -
21.10: Human In The Loop - HITL
Free -
21.11: Quiz
-
21.12: LangGraph Interview Questions
Free
22:
Autonomous Multi-Agent Systems : CrewAI
9 Lectures
-
22.1: Introduction & Setup
Free -
22.2: Building a Simple Agent
Free -
22.3: Agent With Tools
Free -
22.4: Building The Crew
Free -
22.5: Crew With Tool Integration
Free -
22.6: Project Overview And Problem Statement
Free -
22.7: Project Step-by-Step Development
Free -
22.8: Quiz
-
22.9: Crew AI Interview Questions
Free
23:
Bonus: Transformer Architecture
13 Lectures
-
23.1: Introduction to Transformer Architecture
-
23.2: Word Embeddings
-
23.3: Contextual Embeddings
-
23.4: Overview of Encoder and Decoder
-
23.5: Tokenization, Positional Embeddings
-
23.6: Attention Mechanism
-
23.7: Multi Headed Attention
-
23.8: Decoder
-
23.9: How Transformers are Trained?
-
23.10: Hugging Face: BERT Basics
-
23.11: Hugging Face: Spam Classification Using BERT
-
23.12: Hugging Face: Next Word Prediction Using GPT2
-
23.13: Exercise
Start Applying for Jobs -DS
00h:02m:00s on-demand video
|
1
Lectures
00h:02m:00s on-demand video
|
1 Lectures
Interview Preparation / Job Assistance - DS
00h:02m:55s on-demand video
|
2
Lectures
00h:02m:55s on-demand video
|
2 Lectures
Virtual Internship
00h:09m:49s on-demand video
|
10
Lectures
00h:09m:49s on-demand video
|
10 Lectures
2:
Week1
29 Lectures
-
2.1: Welcome Note
-
2.2: Your Onboarding Letter
-
2.3: Welcome Note from Your Manager
-
2.4: Let's Dive Right Into Week 1!
-
2.5: Getting Help From Your Mentors / Seniors
-
2.6: Your First Task
-
2.7: Incoming Task Email 1
-
2.8: Have You Completed This Task?
-
2.9: Quality Check 1
-
2.10: Quality Check 2
-
2.11: Quality Check 3
-
2.12: Quality Check 4
-
2.13: Quality Check 5
-
2.14: Quality Check 6
-
2.15: Quality Check 7
-
2.16: Quality Check 8
-
2.17: Quality Check 9
-
2.18: Quality Check 10
-
2.19: Congratulations, You Have Completed the First Task of Your Internship.
-
2.20: Your Second Task
-
2.21: Incoming Task Email 2
-
2.22: Have You Completed the Assigned Task?
-
2.23: Presentation Submission (Visualization Task)
-
2.24: Congratulations! You Have Completed 2 Tasks in a Row!
-
2.25: You Need Scrum Training
-
2.26: Incoming Task Email 3
-
2.27: Have You Completed the Assigned Task?
-
2.28: Scrum Knowledge Check
-
2.29: Congratulations, You Have Completed Week 1 Successfully.
3:
Week 2
26 Lectures
-
3.1: Let's Dive Right into Week 2!
-
3.2: Can You Handle this Project?
-
3.3: Incoming Task Email 1
-
3.4: Have You Completed the Assigned Task?
-
3.5: Quality Check 1
-
3.6: Quality Check 2
-
3.7: Quality Check 3
-
3.8: Quality Check 4
-
3.9: Quality Check 5
-
3.10: Quality Check 6
-
3.11: Quality Check 7
-
3.12: Quality Check 8
-
3.13: Quality Check 9
-
3.14: Quality Check 10
-
3.15: Congratulations on Finishing This Task!
-
3.16: SQL Query Debugging
-
3.17: Incoming Task Email 2
-
3.18: Quality Check 1
-
3.19: Quality Check 2
-
3.20: Quality Check 3
-
3.21: Quality Check 4
-
3.22: Quality Check 5
-
3.23: Quality Check 6
-
3.24: Quality Check 7
-
3.25: Quality Check 8
-
3.26: Congratulations on Successfully Completing the Task!
4:
Week 3 & 4
26 Lectures
-
4.1: Let's Dive Right into the Final 2 Weeks!
-
4.2: Can You Step Up for a Client Task?
-
4.3: Incoming Task Email: Data Cleaning
-
4.4: Please Don't Share Datasets
-
4.5: Have You Completed the Data Cleaning?
-
4.6: Quality Check 1
-
4.7: Quality Check 2
-
4.8: Quality Check 3
-
4.9: Quality Check 4
-
4.10: Incoming Task Email: Feature Engineering
-
4.11: Have You Completed the Feature Engineering Task?
-
4.12: Quality Check 1
-
4.13: Quality Check 2
-
4.14: Quality Check 3
-
4.15: Quality Check 4
-
4.16: Let's Start Predictive Modeling
-
4.17: Incoming Task Email: Predictive Modeling
-
4.18: Have You Completed the Modeling Task?
-
4.19: Quality Check
-
4.20: Incoming Task Email: MLflow Deployment
-
4.21: Submission of DagsHub link | ML flow
-
4.22: Incoming Task Email: Streamlit App Development
-
4.23: Have You Completed the Streamlit App Development?
-
4.24: Can You Present to Client?
-
4.25: Presentation Submission (CodeX Project)
-
4.26: End Note
Virtual Internship 2
00h:10m:58s on-demand video
|
5
Lectures
00h:10m:58s on-demand video
|
5 Lectures
1:
Week 5
14 Lectures
-
1.1: Let's Dive Right into Week 5!
-
1.2: DL Project For Our Logistics Client from California
-
1.3: Incoming Task Email 1
-
1.4: Have You Completed the Assigned Task?
-
1.5: Congratulations You Have Finished The First Task Of Week 5!
-
1.6: Incoming Task Email 2
-
1.7: Have You Completed the Assigned Task?
-
1.8: Quality Check 1
-
1.9: Quality Check 2
-
1.10: Congratulations! You Have Completed 2 Tasks in a Row!
-
1.11: Incoming Task Email 3
-
1.12: Have You Completed This Task?
-
1.13: Quality Check
-
1.14: Congratulations, You Have Completed Week 5 Successfully.
2:
Week 6
12 Lectures
-
2.1: Let's Dive Right Into Week 6!
-
2.2: Can We Try an Alternative approach
-
2.3: Incoming Task Email 1
-
2.4: Have You Completed This Task?
-
2.5: Quality Check 1
-
2.6: Quality Check 2
-
2.7: Congratulations on Finishing This Task!
-
2.8: Incoming Task Email 2
-
2.9: Have You Built the Streamlit App for Client Demo
-
2.10: Manage Your GitHub Reporsitory
-
2.11: Have you pushed your code to GitHub?
-
2.12: Congratulations on Successfully Completing the Task!
3:
Week 7
8 Lectures
-
3.1: Let's Dive Right Into Week 7!
-
3.2: Business Context
-
3.3: Incoming Task Email 1
-
3.4: Have you completed this task?
-
3.5: Congratulations you have completed the first task of Week 7
-
3.6: Incoming Task Email 2
-
3.7: Have you completed this task?
-
3.8: Congratulations, You Have Completed Week 7 Successfully.
Get Your Certificate
00:00 on-demand video
|
0
Lectures
00:00 on-demand video
|
0 Lectures
AI for Everyone
16h:37m:39s on-demand video
|
67
Lectures
16h:37m:39s on-demand video
|
67 Lectures
3:
Demystify AI: Machine Learning
10 Lectures
-
3.1: What is Machine Learning
Free -
3.2: Classification vs Regression
Free -
3.3: Supervised vs Unsupervised Learning
Free -
3.4: ML Algorithms Overview
-
3.5: Tooling for ML
-
3.6: Introduction: Building Your Online Credibility on LinkedIn
-
3.7: Task: Register Your Voice on AI
-
3.8: Do This to Get Max Out of This Course
-
3.9: Takeaways & Jargons
-
3.10: Quiz
4:
Demystify AI: Deep Learning
8 Lectures
-
4.1: What is Deep Learning
Free -
4.2: Deep Learning vs Statistical Machine Learning: When to use What?
-
4.3: Credits Scoring to ChatGPT: Overview of Neural Network Architectures
-
4.4: Tooling for Deep Learning
-
4.5: Bank Employee To AI Engineer: Transition Story
-
4.6: Task: Get Engagement by Sharing an AI Learning Resource
-
4.7: Takeaways & Jargons
-
4.8: Quiz
5:
AI/ML Project Lifecycle
15 Lectures
-
5.1: 10 Stages of AI Project Lifecycle
Free -
5.2: Requirements and Scope of Work (SOW)
-
5.3: Data Collection
-
5.4: Data Preparation & Exploratory Data Analysis
-
5.5: Feature Engineering
-
5.6: Model Selection & Training
-
5.7: Model Evaluation Metrics (Accuracy, Prediction, Recall & F1 Score)
-
5.8: Model Evaluation Metrics: When to use which Metric?
-
5.9: Model Fine Tuning
-
5.10: Model Deployment
-
5.11: Deployment & Monitoring Using ML Ops
-
5.12: Online Credibility: Engage Meaningfully
-
5.13: Task: Post About AI/ML Project Steps
-
5.14: Takeaways & Jargons
-
5.15: Quiz
6:
Demystify AI: Gen AI, LLMs and NLP
11 Lectures
-
6.1: What is Generative AI (or Gen AI)?
-
6.2: Evolution of Gen AI Models
-
6.3: What is LLM? Analogy Based Simple Explanation
-
6.4: What is NLP?
-
6.5: Embeddings and Vector Database
-
6.6: Retrieval Augmented Generation (RAG)
-
6.7: Prompt Engineering
-
6.8: Tooling for Gen AI, LLM
-
6.9: Task: Share Your Unique Perspectives on Gen AI
-
6.10: Takeaways & Jargons
-
6.11: Quiz
7:
Data & AI
9 Lectures
-
7.1: The Role of Data in AI
Free -
7.2: Data Infrastructure in a Company
-
7.3: Data Collection: Overview
-
7.4: Data Storage and Transformation: Overview
-
7.5: Data Distribution: Privacy, Ethics & Governance
-
7.6: Online Credibility: Mental Model of Content Creation
-
7.7: Task: Emphasize the Importance of Data in AI
-
7.8: Takeaways & Jargons
-
7.9: Quiz
8:
6 Industry Case Studies
9 Lectures
-
8.1: Text Classification: Support Ticket Prioritization
-
8.2: Image Classification: Crop Yield Detection
-
8.3: RAG-Based Gen AI: ChatGPT for Private Organizational Data
-
8.4: Chatbot: Food Delivery Chatbot
-
8.5: LLM Powered Real Estate Chatbot
-
8.6: Recommendation System: Book Recommendations
-
8.7: Task: Build a Case Study on a Company that leverages AI
-
8.8: Takeaways & Jargons
-
8.9: Quiz
12:
Supplementary Learning (Industry Projects)
7 Lectures
-
12.1: Intro: How to use this supplementary learning
-
12.2: Statistical ML Project: Build a House Prediction System
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12.3: Deep Learning Project: Build a Potato Disease Classification System
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12.4: NLP Project: Build a chatbot using Dialog Flow
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12.5: Gen AI Project: Build a Q & A system in retail domain
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12.6: Gen AI Project: Build a news research tool in finance domain
-
12.7: Quiz
SQL Beginner to Advanced For Data Professionals
11h:39m:30s on-demand video
|
86
Lectures
11h:39m:30s on-demand video
|
86 Lectures
5:
SQL Basics: Data Retrieval - Single Table
15 Lectures
-
5.1: Install MySQL: Windows
Free -
5.2: Install MySQL: Linux, Mac
Free -
5.3: Import Movies Dataset in MySQL
Free -
5.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
5.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
5.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
5.10: HAVING Clause
Free -
5.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
5.13: The Data God’s Blessing
Free -
5.14: Quiz
-
5.15: Chapter Summary
8:
SQL Basics: Database Creation & Updates
18 Lectures
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8.1: Database Normalization and Data Integrity
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8.2: Entity Relationship Diagram (ERD)
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8.3: Mentor Talk: Art of Googling
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8.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
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8.5: Data Types: String (VARCHAR, CHAR, ENUM)
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8.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
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8.7: Data Types: JSON, Spatial (JSON, GEOMETRY)
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8.8: Luck Favors the LinkedIn Post
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8.9: Primary key
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8.10: Foreign Key
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8.11: Create a Database From an Entity Relationship Diagram - ERD
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8.12: Import Data From a CSV File Into a Database
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8.13: Insert Statement
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8.14: Update and Delete
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8.15: I Need a Favour
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8.16: Expect the Unexpected: The Intermission Scene
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8.17: Quiz
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8.18: Chapter Summary
9:
AtliQ Hardware & Problem Statement
9 Lectures
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9.1: The Rise of Databases at AtliQ
Free -
9.2: Relational vs No-SQL Database
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9.3: AtliQ Hardware’s Business Model
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9.4: Profit & Loss Statement
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9.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog
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9.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import
-
9.7: Simplified: What is Kanban?
-
9.8: Quiz
-
9.9: Chapter Summary
10:
SQL Advanced: Finance Analytics
10 Lectures
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10.1: Backlog Grooming Meeting: Gross Sales Report
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10.2: User-Defined SQL Functions
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10.3: Exercise: User-Defined SQL Functions
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10.4: Gross Sales Report: Monthly Product Transactions
-
10.5: Gross Sales Report: Total Sales Amount
-
10.6: Exercise: Yearly Sales Report
-
10.7: Stored Procedures: Monthly Gross Sales Report
-
10.8: Stored Procedure: Market Badge
-
10.9: Benefits of Stored Procedures
-
10.10: Quiz
11:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
11.1: Problem Statement and Pre-Invoice Discount Report
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11.2: Performance Improvement # 1
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11.3: Performance Improvement # 2
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11.4: Database Views: Introduction
-
11.5: Database Views: Post Invoice Discount, Net Sales
-
11.6: Exercise: Database Views
-
11.7: Top Markets and Customers
-
11.8: Exercise: Top Products
-
11.9: The Two Most Important Skills for the Data Analyst
-
11.10: Window Functions: OVER Clause
-
11.11: Window Functions: Using it in a Task
-
11.12: Exercise: Window Functions: OVER Clause
-
11.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.14: Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
11.15: 5 Ways SQL is Used in the Industry
-
11.16: Quiz
13:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
13.1: Supply Chain Basics : Simplified
-
13.2: Problem Statement
-
13.3: Create a Helper Table
-
13.4: Database Triggers
-
13.5: Database Events
-
13.6: Temporary Tables & Forecast Accuracy Report
-
13.7: Exercise: CTE, Temporary Tables
-
13.8: Subquery vs CTE vs Views vs Temporary Table
-
13.9: User Accounts and Privileges
-
13.10: Database Indexes: Overview
-
13.11: Database Indexes: Composite Index
-
13.12: Database Indexes: Index Types
-
13.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
13.14: Quiz
Live Webinars
40h:39m:20s on-demand video
|
29
Lectures
40h:39m:20s on-demand video
|
29 Lectures
4:
Career Development Session
5 Lectures
-
4.1: Beginners Guide to Job Seeking - Sep 23
-
4.2: 6 Free Internet Tools to Get an Interview Call - Oct 23
-
4.3: Smart Job Assistance Portal & Expert Resume Insights
-
4.4: The Secret Behind Resumes and Portfolios That Landed Jobs: Decode with your Talent Manager
-
4.5: Strategic Job Search with Google, LinkedIn and Naukri - 22nd November
6:
Expert Webinars
11 Lectures
-
6.1: Freelancing in Data Analytics & Building Your Credibility on LinkedIn - By Zain Altaf
-
6.2: The Practical Power BI Workflow and UAT Process Every Analyst Should Know - By Trilochan Tripathy
-
6.3: Transitioning from Non-Tech to Data Analytics - Journey and Tips by Shail Sahu
-
6.4: PL-300 Certification: What You Need to Know & How to Prepare - Anmol Malviya
-
6.5: How to Approach Scenario-Based Questions and Guesstimates in the Interviews - Shashank Singh
-
6.6: Tips and Tricks to Approach Data Analyst Interviews - Gaurav Agrawal
-
6.7: How I would prepare for Data Analyst Interviews If I had to start over - Munna Das
-
6.8: Key Lessons and Interview Tips from My Journey as a Data Analyst - Bharath Kumar G
-
6.9: My Life as a Data Analyst at Ford Motors- Raghavan P
-
6.10: Data Analytics Freelancing Essentials - Santhanalakshmi Ponnurasan
-
6.11: How to differentiate your work - Ashish Babaria
Live Problem-Solving Sessions
04h:40m:35s on-demand video
|
4
Lectures
04h:40m:35s on-demand video
|
4 Lectures
Personal Branding (LinkedIn & Beyond) for All Professionals
02h:06m:45s on-demand video
|
38
Lectures
02h:06m:45s on-demand video
|
38 Lectures
7:
Create Your Own Posts
9 Lectures
-
7.1: Mental Model of Content Creation
-
7.2: 6 Fundamental ways to create a post with real examples
-
7.3: Effective Template for Posting
-
7.4: 10 plug-n-play post templates
-
7.5: Treating your comments like Posts
-
7.6: I need a favor
-
7.7: Quiz
-
7.8: Activity: Create Your First Post
-
7.9: Activity: Write 3 comments like a Post
12:
Burning Questions
12 Lectures
-
12.1: I don’t get any engagements in my posts, what should I do?
-
12.2: Is spending this much time and being active on LinkedIn worth it?
-
12.3: I’m not an expert—what do I even talk about?
-
12.4: Is LinkedIn Premium required to grow on LinkedIn?
-
12.5: Do I really need a personal brand if I’m not trying to become an influencer?
-
12.6: Isn’t LinkedIn just for job seekers? I’m not looking for a new job.
-
12.7: How long does it take before I start seeing results?
-
12.8: What if my current employer doesn’t like me being active on LinkedIn?
-
12.9: Can I build a brand if I’m a freelancer/consultant and not in a full-time role?
-
12.10: How do I create content when I don’t have time?
-
12.11: Is it necessary to create content, or can I build a brand just by engaging with others?
-
12.12: What should I do if I receive negative comments or criticism on my posts?
Practice Room: Python for Gen AI & Data Science
00h:02m:00s on-demand video
|
0
Lectures
00h:02m:00s on-demand video
|
0 Lectures
Practice Room: Math & Stats for Gen AI & Data Science
00:00 on-demand video
|
0
Lectures
00:00 on-demand video
|
0 Lectures
Practice Room: Machine Learning
00:00 on-demand video
|
0
Lectures
00:00 on-demand video
|
0 Lectures
Practice Room: Deep Learning
00:00 on-demand video
|
0
Lectures
00:00 on-demand video
|
0 Lectures
Practice Room: NLP for Gen AI & Data Science
00:00 on-demand video
|
0
Lectures
00:00 on-demand video
|
0 Lectures
Practice Arena
1:
DS SQL Practice Room
-
1.1: Deduplication – Latest Record
-
1.2: Window Functions – Nth Highest Salary
-
1.3: Gaps & Islands – Login Streak
-
1.4: Cohort Retention Table Analysis
-
1.5: MoM Growth
-
1.6: Overlapping Bookings
-
1.7: Percentiles – 95th Percentile Latency
The Real Comparison
Why Choose Codebasics?
See how we stack up against overpriced bootcamps and random courses.
| Feature | Others | Codebasics |
|---|---|---|
| Learning Structure | Random videos, no path | Step-by-step curriculum |
| Doubt Support | You're on your own | Dedicated doubt solving |
| Industry Projects | Toy data sets | Real company case studies |
| Placement Help | Marketing gimmicks | Genuine job assistance |
| Teaching Methods | Complex, boring | Simplified with cinematic experiences |
| Pricing | ₹1.2L - ₹2.5L with EMI stress | ₹12k - ₹15k, no debt |
May we help you?
Frequently Asked
Questions
Q.1
Do I also get the Gen AI & DS Bootcamp?
Q.2
When are the live sessions?
Q.3
What if I miss a live session?
Q.4
What happens after the 75 days? Do I lose access?
Q.5
When do I get access?
Q.1
Do I need ML experience?
Q.2
I'm a fresher or have less than 2 years of experience. Can I join?
Q.3
Who is this bootcamp for?
Q.1
How do I get help if I'm stuck?
Q.2
Is there job assistance?
Q.1
I already own the Gen AI & DS Bootcamp. What do I pay?
Q.2
Can I purchase only the AI Engineering Bootcamp without the GenAI & Data Science Bootcamp?
Here's the thing: The AI Engineering Bootcamp is an advanced, fast-paced and highly practical program. The GenAI & Data Science Bootcamp is included so you always have a solid reference point for foundational concepts during the live sessions, think of it as your reference library throughout the journey.
We don't offer a standalone version at a reduced price as the program is intentionally designed as an integrated experience.
That said, if you are already an existing Gen AI & Data Science learner, you are eligible for a reduced price.
Q.1
What's the refund policy?
Read the full refund policy here: https://codebasics.io/refund-policy
Q.2
I used a subsidy (my existing Gen AI Bootcamp or individual course purchase). Can I refund my original purchase after enrolling in the AI Engineering Bootcamp?
Example: You own the Gen AI Bootcamp and enroll in the AI Engineering Bootcamp at a reduced price. You cannot later refund the Gen AI Bootcamp because it was used to calculate your reduced price.
Q.3
I used a subsidy and now want to refund the AI Engineering Bootcamp itself. What happens?
Q.1
What system configuration do I need for the AI Engineer Bootcamp?
Operating System: Windows 11
Processor: Intel Core i7 (10th Gen or higher) or AMD Ryzen 7 (4th Gen or higher)
(If you’re not focusing heavily on Deep Learning & GenAI, an i5 is also sufficient.)
Memory (RAM): 8GB (Minimum) / 16GB (Recommended)
Storage: 512GB SSD or HDD (SSD is strongly recommended for better speed)
GPU: NVIDIA GTX 1660 or higher (e.g., GTX 2060, 3060, or above) for Deep Learning and GPU-accelerated tasks
Note: The above configuration is more than enough for all learning and bootcamp work. You would only need stronger hardware if you plan to fine-tune small LLMs locally.
Q.1
Do I also get the Gen AI & DS Bootcamp?
Q.2
When are the live sessions?
Q.3
What if I miss a live session?
Q.4
What happens after the 75 days? Do I lose access?
Q.5
When do I get access?
Q.1
Do I need ML experience?
Q.2
I'm a fresher or have less than 2 years of experience. Can I join?
Q.3
Who is this bootcamp for?
Q.1
How do I get help if I'm stuck?
Q.2
Is there job assistance?
Q.1
I already own the Gen AI & DS Bootcamp. What do I pay?
Q.2
Can I purchase only the AI Engineering Bootcamp without the GenAI & Data Science Bootcamp?
Here's the thing: The AI Engineering Bootcamp is an advanced, fast-paced and highly practical program. The GenAI & Data Science Bootcamp is included so you always have a solid reference point for foundational concepts during the live sessions, think of it as your reference library throughout the journey.
We don't offer a standalone version at a reduced price as the program is intentionally designed as an integrated experience.
That said, if you are already an existing Gen AI & Data Science learner, you are eligible for a reduced price.
Q.1
What's the refund policy?
Read the full refund policy here: https://codebasics.io/refund-policy
Q.2
I used a subsidy (my existing Gen AI Bootcamp or individual course purchase). Can I refund my original purchase after enrolling in the AI Engineering Bootcamp?
Example: You own the Gen AI Bootcamp and enroll in the AI Engineering Bootcamp at a reduced price. You cannot later refund the Gen AI Bootcamp because it was used to calculate your reduced price.
Q.3
I used a subsidy and now want to refund the AI Engineering Bootcamp itself. What happens?
Q.1
What system configuration do I need for the AI Engineer Bootcamp?
Operating System: Windows 11
Processor: Intel Core i7 (10th Gen or higher) or AMD Ryzen 7 (4th Gen or higher)
(If you’re not focusing heavily on Deep Learning & GenAI, an i5 is also sufficient.)
Memory (RAM): 8GB (Minimum) / 16GB (Recommended)
Storage: 512GB SSD or HDD (SSD is strongly recommended for better speed)
GPU: NVIDIA GTX 1660 or higher (e.g., GTX 2060, 3060, or above) for Deep Learning and GPU-accelerated tasks
Note: The above configuration is more than enough for all learning and bootcamp work. You would only need stronger hardware if you plan to fine-tune small LLMs locally.
Software Engineer → AI Engineer in 75 Days. Live Cohort Started on 7th March. All Included.
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