Gen AI & Data Science Bootcamp: With Practical Job Placement Support & Virtual Internship
Industry Projects-Based Bootcamp Created by Hiring Managers in Data / AI Field
Break into tech with Gen AI & Data Science Bootcamp designed to get you hired. Learn directly from Dhaval Patel and industry pros. With real-world projects, build a portfolio that stands out, and get job-ready with virtual internships and placement support. Emerge ready for roles like Data Scientist, Machine Learning Engineer, NLP Engineer, or AI Engineer. Benefit from dedicated guidance and lifetime content validity.
40+
Real Scenarios
12+
Business Projects
2
Virtual Internships
Free
Portfolio Website
Lifetime
Content Validity
2000+
Enrolled Learners
Join our Bootcamp
Learn In-demand Skills
Get Hired by Top Companies
Get AI Toolkit For Professionals at a 25% subsidy along with your Bootcamp. Click to Benefit
No Questions Asked Refund Policy
Get your money within 30 days of enrollment
Added Benefit
If you have already taken our paid Gen AI & DS course(s), you will pay only the difference (This Bootcamp Price - Money You Paid for Gen AI & DS Course(s) Price)
40+
Real Scenarios
12+
Business Projects
2
Virtual Internships
Free
Portfolio Website
Lifetime
Content Validity
2000+
Enrolled Learners
Why this is the most effective Gen AI & DS Bootcamp?
-
Automated Resume Builder and Portfolio Website to showcase your work
-
Unlimited daily doubt clearance support via private Discord community
-
Real-time industry-based projects in F & B, Banking, Healthcare, Finance, Hospitality, Automotive, Real Estate & Logistics
-
Datasets that mimics real time industry scenarios
-
Practical job assistance (Resume & Interview Preparation + Interview and Job Application Playbook + Interview Leads + Building Online Credibility)
-
Highly engaging content with the cinematic experience, real business practice problems, business meetings, etc.
-
Easy explanation of complex topics by Dhaval Patel, a famous Youtuber (1M+ subs) teacher & active data industry expert
-
Soft / Core skills / tasks co-created by Hemanand Vadivel, an industry leader with 8+ years of experience in International Markets
Key Reasons
Why Should You Enroll in This Gen AI & DS Bootcamp?
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Industry project-based Bootcamp designed by hiring experts
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We don't make false job guarantees, but our learners get hired!
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Interview guidance to help you land your dream job
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30 Days Refund Policy - No Questions Asked
Hear It From
Our Happy Learners
Our content is rated 4.9/5 from 22818+ Learners
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Machine Learning
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SQL
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Python
One of the finest course, I have come across!!
The content is sequentially crafted and covers from the fundamentals of Machine learning to advanced level coding. This has cleared both the theoretical and code level doubts and has helped developing math intuition behind any challenges. Codebasics provide the best courses that are both cost effective and contents are well engaging with the simplest explanation.
I have an overall experience of 3.8 years in Data Science,engineering and visualisation using the tools like Dataiku, MarkLogic and Qlik sense. But this course has taught me Python alongwith Data science with a better approach. My love for mathematics has helped me to deep dive into nitty gritty aspects in each algorithm. After a career gap of 7 months, I hope to land a better job where I can explore, develop my skills and contribute to greater challenges in the field of AI/ML. Research in this domain is my preference but I am open to learn and contribute.
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This course provides a comprehensive introduction to the Python programming language. The course is well structured, starting with the basics and gradually building up to more advanced concepts. The lessons are taught through clear and concise video tutorials, accompanied by interactive coding exercises that reinforce the concepts covered. The course covers topics such as data types, functions, object-oriented programming, and more. The instructor is knowledgeable and passionate about Python, and the course is well-paced, making it easy to follow along and absorb the material. Overall, the Code Basics Python course is an excellent resource for anyone looking to learn Python, from beginners to those with some programming experience.
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Landed a Job
Being from a non-tech background and I know only economics and statistics, I feel happy because these industry experts made our life so easy. From the bottom of my heart, I congratulate Mr Dhaval Patel for introducing this fantastic 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
I just finished this AI course and it was amazing! I was really nervous at first because AI seemed so hard, but Dhaval sir made everything easy to understand. He breaks down difficult topics into simple, clear lessons, using practical examples that make learning fun and straightforward. His passion for teaching and deep knowledge of AI shine through in every class. Thanks to his incredible support and teaching style, I completed the course, which I never thought I could do. I highly recommend this course to anyone interested in AI. Dhaval sir, you made this journey unforgettable and truly exciting. Thank you!
AI for Everyone
You Can
Work On Real World Projects
That
Hiring Managers Like
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Healthcare Premium Prediction
Develop a high-accuracy predictive model to estimate healthcare premiums based on factors such as age, smoking habits, BMI, and medical history. Integrate the model into a Streamlit application for client use in making predictions.
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Credit Risk Modelling: NBFC Domain
Build a Streamlit app and classification model to calculate scores for loan applications in the NBFC domain. Categorize applicants into credit ratings (poor to excellent) and identify potential defaulters, following a CIBIL-like system.
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Expense Tracking System: Finance Domain
Build a interactive web application using Streamlit which takes the expense amount and category as input. It will then perform analytics and provide insights on expense breakdown by categories and months.
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AtliQo Bank - Credit Card Launch Campaign
Assist AtliQo Bank in launching a credit card campaign by performing data cleaning, EDA, hypothesis testing, and A/B testing to analyze effectiveness, uncover insights, and recommend actions for campaign success.
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Beverage Price Range Prediction
Develop a predictive model that will assist in setting a price range that avoids the risks of overselling or underselling the product based on the survey data, ensuring a successful market launch.
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Car Damage Detection using CNN
Build a deep learning model for car damage detection using CNNs, transfer learning, and hyperparameter tuning. Deploy the model with Streamlit and FastAPI, enabling automated and efficient damage assessments for real-world applications.
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Real Estate Assistant Using RAG
Develop a generative AI solution for the real estate domain using Retrieval-Augmented Generation (RAG). Implement efficient data retrieval and answer generation, integrating the system with a user-friendly Streamlit interface for seamless interactions.
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E-Commerce Website Chatbot
Build a chatbot for an e-commerce platform to handle FAQs, product queries, and assist customers with purchase decisions. Integrated with SQLite database for dynamic responses and deployed via a user-friendly Streamlit UI for seamless interactions.
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Stale Fruit Detection: Deep Learning
Build a CNN-based deep learning model to detect stale fruit in cold storage warehouses. Project includes data augmentation, model training, fine-tuning, and building a Streamlit app where clients can drag and drop images for real-time detection.
/uploads/bundle/real_world_project/rag_project.webp)
RAG Based System for Healthcare Professionals
Develop a RAG based Q&A system to query PubMed articles and provide relevant answers using an LLM. The system stores articles in Chromadb, generating context-based responses via an interactive Streamlit UI.
Overview
What you'll learn in
this Gen AI & DS Bootcamp
Welcome to The Gen AI and Data Science Bootcamp Experience
01h:13m:35s on-demand video
|
20 Lectures
Welcome to the Bootcamp Experience
14 Lectures
-
Career Benefits of Learning AI and Data Science in 2025
Free -
How do I know If the Data Scientist / AI Engineer role is suitable for me?
Free -
There are so many Bootcamps out there, why this Bootcamp?
Free -
What kind of Job Assistance Do You Provide?
Free -
How Do I Get Doubt-Clearance Support?
Free -
Unlock Discord Channels
-
Quality over Quantity
Free -
How Much Time Do I Need to Complete The Bootcamp?
Free -
How Many Business Projects Will I Complete in This Bootcamp?
Free -
What are the Soft Skills I Will Acquire By the End of Bootcamp?
Free -
Pricing, EMI and Refund
Free -
Bootcamp Syllabus Overview
Free -
How Will I Be Informed About the Monthly Live Webinars?
Free -
System Requirements
Free
Python: Beginner to Advanced For Data Professionals
16h:57m:31s on-demand video
|
108 Lectures
Project 2: Expense Tracking System
11 Lectures
-
Problem Statement & Tech Architecture
-
Database CRUD Operations
-
Automated Tests Setup for CRUD
-
Expense Management: Backend (FastAPI)
-
Expense Management: Logging
-
Streamlit Introduction
-
Expense Management: Frontend (Streamlit)
-
Analytics: Backend (FastAPI)
-
Analytics: Frontend (Streamlit)
-
README and Requirements.txt
-
Exercise
Online Credibility
00h:24m:12s on-demand video
|
4 Lectures
SQL for Data Science
02h:02m:08s on-demand video
|
86 Lectures
SQL Basics: Data Retrieval - Single Table
14 Lectures
-
Install MySQL: Windows
Free -
Install MySQL: Linux, Mac
Free -
Import Movies Dataset in MySQL
Free -
Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
HAVING Clause
Free -
Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
The Data God’s Blessing
Free -
Quiz
Math and Statistics For AI, Data Science
12h:49m:38s on-demand video
|
98 Lectures
Measures Of Central Tendency and Dispersion
18 Lectures
-
Descriptive vs. Inferential Statistics
Free -
Measures of Central Tendency: Mean, Median, Mode
Free -
Percentile
-
Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
Quiz
-
Exercise
-
Measures of Dispersion: Range, IQR
-
Box or Whisker Plot
-
Outlier Treatment Using IQR and Box Plot
-
Quiz
-
Exercise
-
Measures of Dispersion: Variance and Standard Deviation
-
Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
Correlation
-
Correlation vs Causation
-
Quiz
-
Exercise
-
Chapter Summary
Phase 1: Find Target Market
21 Lectures
-
Data Validation Of Acquired Data
-
Data Understanding, MySQL Setup
-
Data Import in Jupyter Notebook
-
Data Cleaning: Handle NULL Values (Annual Income)
-
Data Cleaning: Treat Outliers (Annual Income)
-
Data Visualization: Annual Income
-
Exercise: Treat Outliers in Age Column
-
Exercise Solution: Treat Outliers in Age Column
-
Data Visualization: Age, Gender, Location
-
Peter’s Nightmare
-
Data Cleaning: Credit Score Table - Part 1
-
Data Cleaning: Credit Score Table - Part 2
-
Correlation among Credit Profile Variables
-
Exercise: Handle NULL Values in Transactions Table
-
Exercise Solution: Handle NULL Values in Transactions Table
-
Peter’s Confusion: IQR or Std Dev?
-
Data Cleaning: Treat Outliers using IQR (Transaction Amount)
-
Data Visualization: Transactions Table
-
Finalize the Target Group
-
Phase 1 Feedback Meeting With Stakeholders
-
Get Ready For Phase 2
Hypothesis Testing
19 Lectures
-
Null vs Alternate Hypothesis
-
Z Test, Rejection Region
-
Housing Inflation Test: Rejection Region
-
Quiz
-
Exercise
-
p-Value
-
Housing Inflation Test: p-Value
-
Quiz
-
Exercise
-
One-Tailed vs Two-Tailed Test
-
Type 1 and Type 2 Errors
-
Quiz
-
Statistical Power & Effect Size
-
A/B Testing
-
A/B Testing Using Z Test
-
A/B Testing: Drug Trial
-
Quiz
-
Exercise
-
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
Supervised Machine Learning: Regression
29 Lectures
-
Simple Linear Regression
Free -
Multiple Linear Regression
Free -
Quiz
-
Exercise
-
Cost Function
-
Derivatives and Partial Derivatives
-
Chain Rule
-
Quiz
-
Exercise
-
Gradient Descent Theory
-
Gradient Descent: Python Implementation
-
Why MSE (and not MAE)?
-
Model Evaluation: Train, Test Split
-
Model Evaluation: Metrics
-
Peter Pandey Flexes his ML skills on LinkedIn
-
Quiz
-
Exercise
-
Data Preprocessing: One Hot Encoding
-
Quiz
-
Polynomial Regression
-
Quiz
-
Exercise
-
Overfitting and Underfitting
-
Reasons and Remedies For Overfitting / Underfitting
-
L1 and L2 Regularization
-
Bias Variance Trade Off
-
Quiz
-
Exercise
-
Chapter Summary
Supervised Machine Learning: Classification
31 Lectures
-
Introduction to Classification
Free -
Logistic Regression: Binary Classification
Free -
Model Evaluation: Accuracy, Precision and Recall
-
Quiz
-
Exercise
-
Model Evaluation: F1 Score, Confusion Matrix
-
Logistic Regression: Multiclass Classification
-
Cost Function: Log Loss
-
Quiz
-
Exercise
-
Support Vector Machine (SVM)
-
Data Pre-processing: Scaling
-
Sklearn Pipeline
-
Quiz (disabled)
-
Quiz
-
Exercise
-
Naive Bayes: Theory
-
Naive Bayes: SMS Spam Classification
-
Quiz
-
Exercise
-
Decision Tree: Theory
-
Decision Tree: Salary Classification
-
I Need a Favour
-
Quiz
-
Exercise
-
Handle Class Imbalance: Theory
-
Handle Class Imbalance Using imblearn: Churn Prediction
-
Quiz
-
Exercise
-
Get inspired by Peter Pandey
-
Chapter Summary
Ensemble Learning
21 Lectures
-
What is Ensemble Learning?
Free -
Majority Voting, Average and Weighted Average
-
Bagging
-
Bagging: Random Forest
-
Random Forest: Raisin Classification
-
Quiz
-
Exercise
-
Boosting: AdaBoost
-
Gradient Boosting: Regression Walk Through
-
Gradient Boosting: Regression Math
-
Gradient Boosting: Revenue Prediction
-
Quiz
-
Exercise
-
Gradient Boosting: Classification
-
XGBoost: Walk Through
-
XGBoost: California Housing Prediction
-
XGBoost: Synthetic Data Classification
-
XGBoost: Benefits
-
Quiz
-
Exercise
-
Chapter Summary
Model Evaluation & Fine Tuning
16 Lectures
-
Introduction
-
Model Evaluation: ROC Curve & AUC
-
Cost Benefit Analysis Using ROC in Sklearn
-
Quiz
-
Exercise
-
K Fold Cross Validation
-
Stratified K Fold Cross Validation
-
Hyperparameter Tuning: GridsearchCV
-
Hyperparameter Tuning: RandomizedSearchCV
-
Quiz
-
Exercise
-
Model Selection Guide
-
Luck favors the LinkedIn post
-
Selecting the Right Evaluation Metric
-
Quiz
-
Chapter Summary
Project 1: Healthcare Premium Prediction (Regression)
16 Lectures
-
The Rise of AtliQ AI
Free -
Project Charter Meeting
Free -
Scope of Work, Task Planning in JIRA
-
Data Collection
-
Data Cleaning & EDA - Part 1
-
Data Cleaning & EDA - Part 2
-
Feature Engineering
-
Model Training, Fine Tunning
-
98% Model Accuracy, Really?
-
Error Analysis
-
Model Segmentation
-
Request More Data
-
Model Retraining
-
Build App Using Streamlit
-
Deployment
-
Exercise
Project 2: Credit Risk Modelling (Classification)
19 Lectures
-
Peter's Promotion: New Project
Free -
Domain Understanding: NBFC & Credit Approvals
Free -
Scope of Work & Tech Architecture
Free -
Data Collection
-
Quick Intro to Data Leakage
-
Data Cleaning
-
Exploratory Data Analysis (EDA)
-
Feature Engineering – Part 1
-
Weight of Evidence (WOE), Information Value (IV)
-
Feature Engineering – Part 2
-
Model Training & Evaluation
-
Introduction to Optuna
-
Model Fine Tuning Using Optuna
-
Intro To Rank Ordering & KS Statistic
-
Model Evaluation Using KS Statistic & Gini Coefficient
-
Streamlit App
-
Business Presentation
-
Deployment
-
Exercise
ML Ops & Cloud Tools
22 Lectures
-
What is ML Ops?
Free -
Importance of ML Ops in Your Career
Free -
ML Flow: Purpose and Overview
Free -
ML Flow: Experiment Tracking
-
ML Flow: Model Registry
-
ML Flow: Centralized Server Using Dagshub
-
Quiz
-
What is API?
-
FastAPI Basics
-
Build FastAPI Server For Credit Risk Project
-
Quiz
-
Git Version Control System
-
Introduction to ML Cloud Platforms
-
AWS Sagemaker: Account Setup
-
AWS Sagemaker: Sagemaker Studio
-
AWS Sagemaker: 4 Ways to Train Model
-
AWS Sagemaker: Built In Algorithms
-
AWS Sagemaker: Script Mode
-
Quiz
-
Data Drift Detection Using PSI & CSI
-
PSI & CSI: Practical Implementation
-
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
Transformers
15 Lectures
-
Introduction to Transformer Architecture
-
Word Embeddings
-
Contextual Embeddings
-
Overview of Encoder and Decoder
-
Tokenization, Positional Embeddings
-
Attention Mechanism
-
Multi Headed Attention
-
Decoder
-
How Transformers are Trained?
-
Hugging Face: BERT Basics
-
Hugging Face: Spam Classification Using BERT
-
Hugging Face: Next Word Prediction Using GPT2
-
Quiz
-
Exercise
-
Chapter Summary
Natural Language Processing
06h:59m:43s on-demand video
|
27 Lectures
Gen AI to Agentic AI with Business Projects
15h:37m:44s on-demand video
|
105 Lectures
Gen AI: Business Project 2 - E-Commerce Chatbot
11 Lectures
-
Problem Statement
-
SOW & Technical Architecture
-
Implement FAQ Handling
-
Routing using semantic-router
-
Streamlit UI: FAQ Handling
-
SQLite Database Setup
-
Implement Product Handling: SQL Query Generation
-
Implement Product Handling: Data Comprehension
-
Streamlit UI: Product Questions Handling
-
Bonus: Web Scraping
-
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
Week1
29 Lectures
-
Welcome Note
-
Your Onboarding Letter
-
Welcome Note from Your Manager
-
Let's Dive Right Into Week 1!
-
Getting Help From Your Mentors / Seniors
-
Your First Task
-
Incoming Task Email 1
-
Have You Completed This Task?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Quality Check 5
-
Quality Check 6
-
Quality Check 7
-
Quality Check 8
-
Quality Check 9
-
Quality Check 10
-
Congratulations, You Have Completed the First Task of Your Internship.
-
Your Second Task
-
Incoming Task Email 2
-
Have You Completed the Assigned Task?
-
Presentation Submission (Visualization Task)
-
Congratulations! You Have Completed 2 Tasks in a Row!
-
You Need Scrum Training
-
Incoming Task Email 3
-
Have You Completed the Assigned Task?
-
Scrum Knowledge Check
-
Congratulations, You Have Completed Week 1 Successfully.
Week 2
26 Lectures
-
Let's Dive Right into Week 2!
-
Can You Handle this Project?
-
Incoming Task Email 1
-
Have You Completed the Assigned Task?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Quality Check 5
-
Quality Check 6
-
Quality Check 7
-
Quality Check 8
-
Quality Check 9
-
Quality Check 10
-
Congratulations on Finishing This Task!
-
SQL Query Debugging
-
Incoming Task Email 2
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Quality Check 5
-
Quality Check 6
-
Quality Check 7
-
Quality Check 8
-
Congratulations on Successfully Completing the Task!
Week 3 & 4
26 Lectures
-
Let's Dive Right into the Final 2 Weeks!
-
Can You Step Up for a Client Task?
-
Incoming Task Email: Data Cleaning
-
Please Don't Share Datasets
-
Have You Completed the Data Cleaning?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Incoming Task Email: Feature Engineering
-
Have You Completed the Feature Engineering Task?
-
Quality Check 1
-
Quality Check 2
-
Quality Check 3
-
Quality Check 4
-
Let's Start Predictive Modeling
-
Incoming Task Email: Predictive Modeling
-
Have You Completed the Modeling Task?
-
Quality Check
-
Incoming Task Email: MLflow Deployment
-
Submission of DagsHub link | ML flow
-
Incoming Task Email: Streamlit App Development
-
Have You Completed the Streamlit App Development?
-
Can You Present to Client?
-
Presentation Submission (CodeX Project)
-
End Note
Virtual Internship 2
00h:10m:58s on-demand video
|
5 Lectures
Week 5
14 Lectures
-
Let's Dive Right into Week 5!
-
DL Project For Our Logistics Client from California
-
Incoming Task Email 1
-
Have You Completed the Assigned Task?
-
Congratulations You Have Finished The First Task Of Week 5!
-
Incoming Task Email 2
-
Have You Completed the Assigned Task?
-
Quality Check 1
-
Quality Check 2
-
Congratulations! You Have Completed 2 Tasks in a Row!
-
Incoming Task Email 3
-
Have You Completed This Task?
-
Quality Check
-
Congratulations, You Have Completed Week 5 Successfully.
Week 6
12 Lectures
-
Let's Dive Right Into Week 6!
-
Can We Try an Alternative approach
-
Incoming Task Email 1
-
Have You Completed This Task?
-
Quality Check 1
-
Quality Check 2
-
Congratulations on Finishing This Task!
-
Incoming Task Email 2
-
Have You Built the Streamlit App for Client Demo
-
Manage Your GitHub Reporsitory
-
Have you pushed your code to GitHub?
-
Congratulations on Successfully Completing the Task!
Get Your Certificate
00:00 on-demand video
|
0 Lectures
AI for Everyone
16h:37m:39s on-demand video
|
67 Lectures
SQL Beginner to Advanced For Data Professionals
11h:15m:16s on-demand video
|
84 Lectures
SQL Basics: Database Creation & Updates
17 Lectures
-
Database Normalization and Data Integrity
-
Entity Relationship Diagram (ERD)
-
Mentor Talk: Art of Googling
-
Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
-
Data Types: String (VARCHAR, CHAR, ENUM)
-
Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
-
Data Types: JSON, Spatial (JSON, GEOMETRY)
-
Luck Favors the LinkedIn Post
-
Primary key
-
Foreign Key
-
Create a Database From an Entity Relationship Diagram - ERD
-
Import Data From a CSV File Into a Database
-
Insert Statement
-
Update and Delete
-
I Need a Favour
-
Expect the Unexpected: The Intermission Scene
-
Quiz
SQL Advanced: Finance Analytics
10 Lectures
-
Backlog Grooming Meeting: Gross Sales Report
-
User-Defined SQL Functions
-
Exercise: User-Defined SQL Functions
-
Gross Sales Report: Monthly Product Transactions
-
Gross Sales Report: Total Sales Amount
-
Exercise: Yearly Sales Report
-
Stored Procedures: Monthly Gross Sales Report
-
Stored Procedure: Market Badge
-
Benefits of Stored Procedures
-
Quiz
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
Problem Statement and Pre-Invoice Discount Report
-
Performance Improvement # 1
-
Performance Improvement # 2
-
Database Views: Introduction
-
Database Views: Post Invoice Discount, Net Sales
-
Exercise: Database Views
-
Top Markets and Customers
-
Exercise: Top Products
-
The Two Most Important Skills for the Data Analyst
-
Window Functions: OVER Clause
-
Window Functions: Using it in a Task
-
Exercise: Window Functions: OVER Clause
-
Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
5 Ways SQL is Used in the Industry
-
Quiz
SQL Advanced: Supply Chain Analytics
14 Lectures
-
Supply Chain Basics : Simplified
-
Problem Statement
-
Create a Helper Table
-
Database Triggers
-
Database Events
-
Temporary Tables & Forecast Accuracy Report
-
Exercise: CTE, Temporary Tables
-
Subquery vs CTE vs Views vs Temporary Table
-
User Accounts and Privileges
-
Database Indexes: Overview
-
Database Indexes: Composite Index
-
Database Indexes: Index Types
-
Peter Pandey's Order: I Have Completed the Course - Now What?
-
Quiz
Live Webinar
30h:18m:35s on-demand video
|
22 Lectures
Welcome to The Gen AI and Data Science Bootcamp Experience
01h:13m:35s on-demand video
|
20 Lectures
1:
Welcome to the Bootcamp Experience
14 Lectures
-
1.1: Career Benefits of Learning AI and Data Science in 2025
Free -
1.2: How do I know If the Data Scientist / AI Engineer role is suitable for me?
Free -
1.3: There are so many Bootcamps out there, why this Bootcamp?
Free -
1.4: What kind of Job Assistance Do You Provide?
Free -
1.5: How Do I Get Doubt-Clearance Support?
Free -
1.6: Unlock Discord Channels
-
1.7: Quality over Quantity
Free -
1.8: How Much Time Do I Need to Complete The Bootcamp?
Free -
1.9: How Many Business Projects Will I Complete in This Bootcamp?
Free -
1.10: What are the Soft Skills I Will Acquire By the End of Bootcamp?
Free -
1.11: Pricing, EMI and Refund
Free -
1.12: Bootcamp Syllabus Overview
Free -
1.13: How Will I Be Informed About the Monthly Live Webinars?
Free -
1.14: System Requirements
Free
Python: Beginner to Advanced For Data Professionals
16h:57m:31s on-demand video
|
108 Lectures
6:
Python Basics: Functions, Dictionaries, Tuples and File Handling
9 Lectures
-
6.1: Functions
-
6.2: Dictionary and Tuples
-
6.3: Modules and Pip
-
6.4: File Handling
-
6.5: Quiz: Functions, Dictionaries, Tuples and File Handling
-
6.6: Peter’s Request to Tony
-
6.7: Exercise: Functions, Dictionaries, Tuples and File Handling
-
6.8: Two Deadly Viruses Infecting Learners
-
6.9: Chapter Summary
15:
Project 2: Expense Tracking System
11 Lectures
-
15.1: Problem Statement & Tech Architecture
-
15.2: Database CRUD Operations
-
15.3: Automated Tests Setup for CRUD
-
15.4: Expense Management: Backend (FastAPI)
-
15.5: Expense Management: Logging
-
15.6: Streamlit Introduction
-
15.7: Expense Management: Frontend (Streamlit)
-
15.8: Analytics: Backend (FastAPI)
-
15.9: Analytics: Frontend (Streamlit)
-
15.10: README and Requirements.txt
-
15.11: Exercise
Online Credibility
00h:24m:12s on-demand video
|
4 Lectures
SQL for Data Science
02h:02m:08s on-demand video
|
86 Lectures
2:
SQL Basics: Data Retrieval - Single Table
14 Lectures
-
2.1: Install MySQL: Windows
Free -
2.2: Install MySQL: Linux, Mac
Free -
2.3: Import Movies Dataset in MySQL
Free -
2.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
2.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)
Free -
2.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
2.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)
Free -
2.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
2.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)
Free -
2.10: HAVING Clause
Free -
2.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
2.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)
Free -
2.13: The Data God’s Blessing
Free -
2.14: Quiz
Math and Statistics For AI, Data Science
12h:49m:38s on-demand video
|
98 Lectures
3:
Measures Of Central Tendency and Dispersion
18 Lectures
-
3.1: Descriptive vs. Inferential Statistics
Free -
3.2: Measures of Central Tendency: Mean, Median, Mode
Free -
3.3: Percentile
-
3.4: Analysis: Shoe Sales (Using Mean, Median, Percentile)
-
3.5: Quiz
-
3.6: Exercise
-
3.7: Measures of Dispersion: Range, IQR
-
3.8: Box or Whisker Plot
-
3.9: Outlier Treatment Using IQR and Box Plot
-
3.10: Quiz
-
3.11: Exercise
-
3.12: Measures of Dispersion: Variance and Standard Deviation
-
3.13: Analysis: Stock Returns Volatility (Using Variance and Std Dev)
-
3.14: Correlation
-
3.15: Correlation vs Causation
-
3.16: Quiz
-
3.17: Exercise
-
3.18: Chapter Summary
7:
Phase 1: Find Target Market
21 Lectures
-
7.1: Data Validation Of Acquired Data
-
7.2: Data Understanding, MySQL Setup
-
7.3: Data Import in Jupyter Notebook
-
7.4: Data Cleaning: Handle NULL Values (Annual Income)
-
7.5: Data Cleaning: Treat Outliers (Annual Income)
-
7.6: Data Visualization: Annual Income
-
7.7: Exercise: Treat Outliers in Age Column
-
7.8: Exercise Solution: Treat Outliers in Age Column
-
7.9: Data Visualization: Age, Gender, Location
-
7.10: Peter’s Nightmare
-
7.11: Data Cleaning: Credit Score Table - Part 1
-
7.12: Data Cleaning: Credit Score Table - Part 2
-
7.13: Correlation among Credit Profile Variables
-
7.14: Exercise: Handle NULL Values in Transactions Table
-
7.15: Exercise Solution: Handle NULL Values in Transactions Table
-
7.16: Peter’s Confusion: IQR or Std Dev?
-
7.17: Data Cleaning: Treat Outliers using IQR (Transaction Amount)
-
7.18: Data Visualization: Transactions Table
-
7.19: Finalize the Target Group
-
7.20: Phase 1 Feedback Meeting With Stakeholders
-
7.21: Get Ready For Phase 2
8:
Central Limit Theorem
12 Lectures
-
8.1: Random Sampling & Sample Bias
-
8.2: The Law of Large Numbers
-
8.3: Central Limit Theorem, Sampling Distribution
-
8.4: Case Study: Solar Panels
-
8.5: Standard Error
-
8.6: Quiz
-
8.7: Z Score Table (Z-Table)
-
8.8: Quiz
-
8.9: Confidence Interval
-
8.10: Confidence Interval: Estimate Car Miles
-
8.11: Exercise
-
8.12: Chapter Summary
9:
Hypothesis Testing
19 Lectures
-
9.1: Null vs Alternate Hypothesis
-
9.2: Z Test, Rejection Region
-
9.3: Housing Inflation Test: Rejection Region
-
9.4: Quiz
-
9.5: Exercise
-
9.6: p-Value
-
9.7: Housing Inflation Test: p-Value
-
9.8: Quiz
-
9.9: Exercise
-
9.10: One-Tailed vs Two-Tailed Test
-
9.11: Type 1 and Type 2 Errors
-
9.12: Quiz
-
9.13: Statistical Power & Effect Size
-
9.14: A/B Testing
-
9.15: A/B Testing Using Z Test
-
9.16: A/B Testing: Drug Trial
-
9.17: Quiz
-
9.18: Exercise
-
9.19: 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
1:
Welcome to Machine Learning Experience
2 Lectures
-
1.1: AI Family Tree
Free -
1.2: Course Overview
Free
3:
Supervised Machine Learning: Regression
29 Lectures
-
3.1: Simple Linear Regression
Free -
3.2: Multiple Linear Regression
Free -
3.3: Quiz
-
3.4: Exercise
-
3.5: Cost Function
-
3.6: Derivatives and Partial Derivatives
-
3.7: Chain Rule
-
3.8: Quiz
-
3.9: Exercise
-
3.10: Gradient Descent Theory
-
3.11: Gradient Descent: Python Implementation
-
3.12: Why MSE (and not MAE)?
-
3.13: Model Evaluation: Train, Test Split
-
3.14: Model Evaluation: Metrics
-
3.15: Peter Pandey Flexes his ML skills on LinkedIn
-
3.16: Quiz
-
3.17: Exercise
-
3.18: Data Preprocessing: One Hot Encoding
-
3.19: Quiz
-
3.20: Polynomial Regression
-
3.21: Quiz
-
3.22: Exercise
-
3.23: Overfitting and Underfitting
-
3.24: Reasons and Remedies For Overfitting / Underfitting
-
3.25: L1 and L2 Regularization
-
3.26: Bias Variance Trade Off
-
3.27: Quiz
-
3.28: Exercise
-
3.29: Chapter Summary
4:
Supervised Machine Learning: Classification
31 Lectures
-
4.1: Introduction to Classification
Free -
4.2: Logistic Regression: Binary Classification
Free -
4.3: Model Evaluation: Accuracy, Precision and Recall
-
4.4: Quiz
-
4.5: Exercise
-
4.6: Model Evaluation: F1 Score, Confusion Matrix
-
4.7: Logistic Regression: Multiclass Classification
-
4.8: Cost Function: Log Loss
-
4.9: Quiz
-
4.10: Exercise
-
4.11: Support Vector Machine (SVM)
-
4.12: Data Pre-processing: Scaling
-
4.13: Sklearn Pipeline
-
4.14: Quiz (disabled)
-
4.15: Quiz
-
4.16: Exercise
-
4.17: Naive Bayes: Theory
-
4.18: Naive Bayes: SMS Spam Classification
-
4.19: Quiz
-
4.20: Exercise
-
4.21: Decision Tree: Theory
-
4.22: Decision Tree: Salary Classification
-
4.23: I Need a Favour
-
4.24: Quiz
-
4.25: Exercise
-
4.26: Handle Class Imbalance: Theory
-
4.27: Handle Class Imbalance Using imblearn: Churn Prediction
-
4.28: Quiz
-
4.29: Exercise
-
4.30: Get inspired by Peter Pandey
-
4.31: Chapter Summary
5:
Ensemble Learning
21 Lectures
-
5.1: What is Ensemble Learning?
Free -
5.2: Majority Voting, Average and Weighted Average
-
5.3: Bagging
-
5.4: Bagging: Random Forest
-
5.5: Random Forest: Raisin Classification
-
5.6: Quiz
-
5.7: Exercise
-
5.8: Boosting: AdaBoost
-
5.9: Gradient Boosting: Regression Walk Through
-
5.10: Gradient Boosting: Regression Math
-
5.11: Gradient Boosting: Revenue Prediction
-
5.12: Quiz
-
5.13: Exercise
-
5.14: Gradient Boosting: Classification
-
5.15: XGBoost: Walk Through
-
5.16: XGBoost: California Housing Prediction
-
5.17: XGBoost: Synthetic Data Classification
-
5.18: XGBoost: Benefits
-
5.19: Quiz
-
5.20: Exercise
-
5.21: Chapter Summary
6:
Model Evaluation & Fine Tuning
16 Lectures
-
6.1: Introduction
-
6.2: Model Evaluation: ROC Curve & AUC
-
6.3: Cost Benefit Analysis Using ROC in Sklearn
-
6.4: Quiz
-
6.5: Exercise
-
6.6: K Fold Cross Validation
-
6.7: Stratified K Fold Cross Validation
-
6.8: Hyperparameter Tuning: GridsearchCV
-
6.9: Hyperparameter Tuning: RandomizedSearchCV
-
6.10: Quiz
-
6.11: Exercise
-
6.12: Model Selection Guide
-
6.13: Luck favors the LinkedIn post
-
6.14: Selecting the Right Evaluation Metric
-
6.15: Quiz
-
6.16: Chapter Summary
7:
ML Project Life Cycle
10 Lectures
-
7.1: 10 Stages of AI Project Life Cycle
Free -
7.2: Requirements and Scope of Work (SOW)
Free -
7.3: Data Collection
-
7.4: Data Cleaning & Exploratory Data Analysis
-
7.5: Feature Engineering
-
7.6: Model Selection & Training
-
7.7: Model Fine Tuning
-
7.8: Model Deployment
-
7.9: Monitoring and Feedback Using ML Ops
-
7.10: Chapter Summary
9:
Unsupervised Learning
13 Lectures
-
9.1: Introduction
-
9.2: K Means Clustering: Theory
-
9.3: K Means Clustering: Customer Segmentation
-
9.4: Hierarchical Clustering: Theory
-
9.5: Hierarchical Clustering: Customer Segmentation
-
9.6: Quiz
-
9.7: Exercise
-
9.8: DBSCAN: Theory
-
9.9: DBSCAN: Practical Implementation
-
9.10: Peter AI
-
9.11: Quiz
-
9.12: Exercise
-
9.13: Chapter Summary
10:
Project 1: Healthcare Premium Prediction (Regression)
16 Lectures
-
10.1: The Rise of AtliQ AI
Free -
10.2: Project Charter Meeting
Free -
10.3: Scope of Work, Task Planning in JIRA
-
10.4: Data Collection
-
10.5: Data Cleaning & EDA - Part 1
-
10.6: Data Cleaning & EDA - Part 2
-
10.7: Feature Engineering
-
10.8: Model Training, Fine Tunning
-
10.9: 98% Model Accuracy, Really?
-
10.10: Error Analysis
-
10.11: Model Segmentation
-
10.12: Request More Data
-
10.13: Model Retraining
-
10.14: Build App Using Streamlit
-
10.15: Deployment
-
10.16: Exercise
11:
Project 2: Credit Risk Modelling (Classification)
19 Lectures
-
11.1: Peter's Promotion: New Project
Free -
11.2: Domain Understanding: NBFC & Credit Approvals
Free -
11.3: Scope of Work & Tech Architecture
Free -
11.4: Data Collection
-
11.5: Quick Intro to Data Leakage
-
11.6: Data Cleaning
-
11.7: Exploratory Data Analysis (EDA)
-
11.8: Feature Engineering – Part 1
-
11.9: Weight of Evidence (WOE), Information Value (IV)
-
11.10: Feature Engineering – Part 2
-
11.11: Model Training & Evaluation
-
11.12: Introduction to Optuna
-
11.13: Model Fine Tuning Using Optuna
-
11.14: Intro To Rank Ordering & KS Statistic
-
11.15: Model Evaluation Using KS Statistic & Gini Coefficient
-
11.16: Streamlit App
-
11.17: Business Presentation
-
11.18: Deployment
-
11.19: Exercise
12:
ML Ops & Cloud Tools
22 Lectures
-
12.1: What is ML Ops?
Free -
12.2: Importance of ML Ops in Your Career
Free -
12.3: ML Flow: Purpose and Overview
Free -
12.4: ML Flow: Experiment Tracking
-
12.5: ML Flow: Model Registry
-
12.6: ML Flow: Centralized Server Using Dagshub
-
12.7: Quiz
-
12.8: What is API?
-
12.9: FastAPI Basics
-
12.10: Build FastAPI Server For Credit Risk Project
-
12.11: Quiz
-
12.12: Git Version Control System
-
12.13: Introduction to ML Cloud Platforms
-
12.14: AWS Sagemaker: Account Setup
-
12.15: AWS Sagemaker: Sagemaker Studio
-
12.16: AWS Sagemaker: 4 Ways to Train Model
-
12.17: AWS Sagemaker: Built In Algorithms
-
12.18: AWS Sagemaker: Script Mode
-
12.19: Quiz
-
12.20: Data Drift Detection Using PSI & CSI
-
12.21: PSI & CSI: Practical Implementation
-
12.22: 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
10:
Convolutional Neural Networks (CNN)
10 Lectures
-
10.1: What is CNN? Convolution, Kernels, Pooling and Beyond
-
10.2: Padding and Strides
-
10.3: CIFAR10 Image Classification using CNN
-
10.4: Data Augmentation
-
10.5: Transfer Learning
-
10.6: Pre-trained Models – ResNet, EfficientNet, MobileNet etc.
-
10.7: Caltech101 Classification Using Transfer Learning
-
10.8: Quiz
-
10.9: Exercise
-
10.10: Chapter Summary
12:
Transformers
15 Lectures
-
12.1: Introduction to Transformer Architecture
-
12.2: Word Embeddings
-
12.3: Contextual Embeddings
-
12.4: Overview of Encoder and Decoder
-
12.5: Tokenization, Positional Embeddings
-
12.6: Attention Mechanism
-
12.7: Multi Headed Attention
-
12.8: Decoder
-
12.9: How Transformers are Trained?
-
12.10: Hugging Face: BERT Basics
-
12.11: Hugging Face: Spam Classification Using BERT
-
12.12: Hugging Face: Next Word Prediction Using GPT2
-
12.13: Quiz
-
12.14: Exercise
-
12.15: Chapter Summary
13:
Project: Car Damage Detection
10 Lectures
-
13.1: AtliQ AI’s First Big Client: Vroom Cars
-
13.2: Problem Statement & SOW
-
13.3: Data Load and Transformation
-
13.4: Model Training with CNN
-
13.5: Model Training with CNN and Regularization
-
13.6: Model Training using Transfer Learning
-
13.7: Hyperparameter Tunning using Optuna
-
13.8: Model Evaluation and Export
-
13.9: Streamlit App
-
13.10: 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
15h:37m:44s on-demand video
|
105 Lectures
2:
Introduction to Generative AI and Agentic AI
9 Lectures
-
2.1: What is Generative AI?
Free -
2.2: Traditional AI vs Gen AI
Free -
2.3: What are AI Agents and Agentic AI?
Free -
2.4: Gen AI vs AI Agents vs Agentic AI
Free -
2.5: Real-world Applications for Gen AI & Agentic AI
-
2.6: Steps to Build Gen AI and Agentic Applications
-
2.7: Quiz
-
2.8: Exercise
-
2.9: Chapter Summary
4:
Gen AI: Langchain and Prompting Essentials
11 Lectures
-
4.1: Elements of a Good Prompt
-
4.2: Zero-Shot, One-Shot, and Few-Shot Prompting
-
4.3: LangChain Installation
-
4.4: Groq and Ollama Setup
-
4.5: Calling LLM from Langchain
-
4.6: Prompt Templates & Chains
-
4.7: Output Parser
-
4.8: Build Financial Data Extraction App
-
4.9: Quiz
-
4.10: Exercise
-
4.11: Chapter Summary
7:
Gen AI: Business Project 2 - E-Commerce Chatbot
11 Lectures
-
7.1: Problem Statement
-
7.2: SOW & Technical Architecture
-
7.3: Implement FAQ Handling
-
7.4: Routing using semantic-router
-
7.5: Streamlit UI: FAQ Handling
-
7.6: SQLite Database Setup
-
7.7: Implement Product Handling: SQL Query Generation
-
7.8: Implement Product Handling: Data Comprehension
-
7.9: Streamlit UI: Product Questions Handling
-
7.10: Bonus: Web Scraping
-
7.11: Exercise
12:
Agentic AI: Business Project 3
9 Lectures
-
12.1: Problem Statement & Tech Architecture
Free -
12.2: HR Management System (HRMS) APIs
-
12.3: Seed Data for HRMS
-
12.4: MCP Tools for Employee Management
-
12.5: Google App Password Setup for Emails
-
12.6: MCP Tools for Emails
-
12.7: MCP Prompt to Onboard a New Employee
-
12.8: MCP Tools for Tickets Management
-
12.9: 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
2:
Supplementary Learning (Industry Projects)
5 Lectures
-
2.1: Intro: How to use this supplementary learning
-
2.2: Statistical ML Project: Build a House Prediction System
-
2.3: Deep Learning Project: Build a Potato Disease Classification System
-
2.4: NLP Project: Build a chatbot using Dialog Flow
-
2.5: Gen AI Project: Build a news research tool in finance domain
SQL Beginner to Advanced For Data Professionals
11h:15m:16s on-demand video
|
84 Lectures
2:
SQL Basics: Database Creation & Updates
17 Lectures
-
2.1: Database Normalization and Data Integrity
-
2.2: Entity Relationship Diagram (ERD)
-
2.3: Mentor Talk: Art of Googling
-
2.4: Data Types: Numeric (INT, DECIMAL, FLOAT, DOUBLE)
-
2.5: Data Types: String (VARCHAR, CHAR, ENUM)
-
2.6: Data Types: Date, Time (DATETIME, DATE, TIME, YEAR, TIMESTAMP)
-
2.7: Data Types: JSON, Spatial (JSON, GEOMETRY)
-
2.8: Luck Favors the LinkedIn Post
-
2.9: Primary key
-
2.10: Foreign Key
-
2.11: Create a Database From an Entity Relationship Diagram - ERD
-
2.12: Import Data From a CSV File Into a Database
-
2.13: Insert Statement
-
2.14: Update and Delete
-
2.15: I Need a Favour
-
2.16: Expect the Unexpected: The Intermission Scene
-
2.17: Quiz
3:
AtliQ Hardware & Problem Statement
8 Lectures
-
3.1: The Rise of Databases at AtliQ
Free -
3.2: Relational vs No-SQL Database
-
3.3: AtliQ Hardware’s Business Model
-
3.4: Profit & Loss Statement
-
3.5: ETL, Data Warehouse, OLAP vs OLTP, Data Catalog
-
3.6: Fact vs Dimension Table, Star vs Snowflake Schema, Data Import
-
3.7: Simplified: What is Kanban?
-
3.8: Quiz
4:
SQL Advanced: Finance Analytics
10 Lectures
-
4.1: Backlog Grooming Meeting: Gross Sales Report
-
4.2: User-Defined SQL Functions
-
4.3: Exercise: User-Defined SQL Functions
-
4.4: Gross Sales Report: Monthly Product Transactions
-
4.5: Gross Sales Report: Total Sales Amount
-
4.6: Exercise: Yearly Sales Report
-
4.7: Stored Procedures: Monthly Gross Sales Report
-
4.8: Stored Procedure: Market Badge
-
4.9: Benefits of Stored Procedures
-
4.10: Quiz
5:
SQL Advanced: Top Customers, Products, Markets
16 Lectures
-
5.1: Problem Statement and Pre-Invoice Discount Report
-
5.2: Performance Improvement # 1
-
5.3: Performance Improvement # 2
-
5.4: Database Views: Introduction
-
5.5: Database Views: Post Invoice Discount, Net Sales
-
5.6: Exercise: Database Views
-
5.7: Top Markets and Customers
-
5.8: Exercise: Top Products
-
5.9: The Two Most Important Skills for the Data Analyst
-
5.10: Window Functions: OVER Clause
-
5.11: Window Functions: Using it in a Task
-
5.12: Exercise: Window Functions: OVER Clause
-
5.13: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
5.14: Exercise: Window Functions: ROW_NUMBER, RANK, DENSE_RANK
-
5.15: 5 Ways SQL is Used in the Industry
-
5.16: Quiz
6:
SQL Advanced: Supply Chain Analytics
14 Lectures
-
6.1: Supply Chain Basics : Simplified
-
6.2: Problem Statement
-
6.3: Create a Helper Table
-
6.4: Database Triggers
-
6.5: Database Events
-
6.6: Temporary Tables & Forecast Accuracy Report
-
6.7: Exercise: CTE, Temporary Tables
-
6.8: Subquery vs CTE vs Views vs Temporary Table
-
6.9: User Accounts and Privileges
-
6.10: Database Indexes: Overview
-
6.11: Database Indexes: Composite Index
-
6.12: Database Indexes: Index Types
-
6.13: Peter Pandey's Order: I Have Completed the Course - Now What?
-
6.14: Quiz
Live Webinar
30h:18m:35s on-demand video
|
22 Lectures
Data Science
Market Opportunity
While the market is tough, you need to build the exact skills to launch or accelerate your career in data science / AI field with quality teaching. However, just building skills is not enough. You need to know how to present your skills in order to stand out from the crowd.
We help you in each and every step to achieve that:
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Building Portfolio Website
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Applying with strategies
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Acing the Job Interview
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Building Online Credibility
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Having right Business projects
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Networking both offline & online
₹12L/Year
Average Data Scientist Salary
Based on data from glassdoor.com
11M+ Jobs
Global Data Science Jobs by 2026
Source: ETHRWorld.com
36% GROWTH
Projected Demand for Data Scientists
Source: U.S. Bureau of Labor Statistics
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12+ Business Projects to add to your Resume
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Access to ATS Resume Builder
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Build Free Portfolio Website
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Unlimited Chat Support
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Live Monthly Webinars
AI Toolkit For Professionals Course
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8+ Real Business Use Cases & Automations
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Get the complete bundle to learn skills practiced by industry experts. Save US$7 with a 25% subsidy.
May we help you?
Frequently Asked
Questions
Q.1
Are the lectures going to be LIVE?
To keep the bootcamp affordable, we have kept the lectures as self-paced videos that you can watch at your convenience. However, there will be monthly webinars and discord community support, through which you can interact with instructors and mentors.
Q.2
Do you provide any Virtual Internship?
Yes, the bootcamp includes two virtual internships designed to give you real-world experience, spanning over 8 weeks.
Q.3
Can I expect to work on real-world datasets during the course?
The datasets used in this bootcamp are crafted from scratch to mimic the real business world, consolidating our years of experience. The course includes projects and case studies based on real-world datasets across multiple domains, such as healthcare, finance, banking, logistics, etc.
Q.4
Does this bootcamp provide lifetime access to the content?
Yes! You’ll have lifetime access to the course content, along with free upgrades to the course material. However, any features or additional modules introduced later may not be part of this access.
Q.5
Will I get a recording of Zoom monthly webinars if I can't attend the live session?
Yes, Monthly webinar recordings will be provided. As a boot camper, you will always have access to the latest monthly webinar along with the recordings from the previous 2 months.
Q.1
What if I have an unrelated degree but want to pursue data science?
A computer science degree or an IT degree is not required. We have many success stories of folks from non-tech backgrounds successfully cracking into a data science career.
However, a strong interest in coding and a willingness to learn Mathematics and Statistics are important pre-requisites. If you're unsure, take our Suitability Test to check if this is the right fit for you.
Suitability Test: https://codebasics.io/survey/find-your-match-ds
Q.2
What prior knowledge or skills do I need before enrolling in this bootcamp?
The only thing you need as a prerequisite is the passion and interest in learning AI and Data Science. Actual prerequisites such as Python, Math, and Statistics basics are all covered in this course.
To maximize the benefits of the bootcamp, we also encourage learners to network on LinkedIn by sharing their projects and achievements. This is a great way to showcase your skills and expand your professional network.
Q.3
How much coding/programming do I need to know to become a Data Scientist?
The first module in our bootcamp is the Python module where we teach everything from basics. You don't need to be an expert coder, but proficiency in data manipulation, visualization, and basic algorithms is good to have.
Q.4
Can I enroll in the bootcamp if I have no background in mathematics or statistics?
Yes. In this bootcamp, we have a module on Math and statistics. This module designed for absolute beginners, simplifies learning Math and Statistics through project-based learning with a real dataset, focusing on practical data science applications.
Q.5
I am confused about whether to pursue my career as a data scientist.
Sure, take this survey https://codebasics.io/survey/find-your-match-ds This survey tells you how much your natural interest matches the data scientist role. It also provides recommendations on the learning path.
Q.1
What kind of support is available if I have questions during the bootcamp?
We have created every lecture of this bootcamp with the motive to explain everything in an easy-to-understand manner. While working on these lectures you could make mistakes in steps or have some doubts. You need to commit yourself to hold patience, make efforts & figure out the solution by googling in order to become truly job ready.
For any questions, that Google cannot answer or if you hit a wall - we got you covered!
You can join our active discord community, which is a dedicated platform to discuss & clear your doubts with fellow learners & mentors.
Q.2
Is there a community platform where I can interact with other students?
Yes, you’ll be part of an active Discord community where you can interact, and learn from one another.
Q.3
Do you provide any job assistance?
Yes, the bootcamp includes ATS resume building, portfolio website creation,building online credibility, job application tips, and interview preparation.
Our goal is to help you confidently step into the job market with the right tools and guidance.
Q.4
Will this data science bootcamp guarantee me a job?
We do not believe in making impractical guarantees. Our promise is to prepare you with the most relevant skills, timeless principles, and practical knowledge to help you secure a job.
Q.1
What certificates will I receive from the bootcamp?
You will receive:
Certificates for Tools: Individual certificates for Python, SQL, Math & Statistics, Machine Learning, Deep Learning, NLP, and Generative AI.
Bootcamp Certificate: A certificate upon completing the entire bootcamp.
Internship Letters: Letter of completion for Virtual Internship.
Q.2
How do I get the certificate?
You need to complete at least 70% of the course and score 70% or more on the final quiz. After completing the quiz, simply click “Complete and Get Certificate” to download your certificate.
Q.1
Do you have an EMI (Instalment) option?
Our website doesn’t currently support an EMI option, but you can still pay in smaller installments by purchasing the Bootcamp courses individually. Here's how:
Step 1: Buy the Python course and complete it at your own pace.
Step 2: Once you’ve completed Python, buy the Math and Statistics course.
Step 3: Finally, buy the Bootcamp when you're ready.
Note: If you'd like to break your payments into even smaller amounts, you can also buy other Bootcamp courses like Machine Learning or Deep Learning individually before purchasing the full Bootcamp.
How It Aligns With Your Learning ⤵️
• The Bootcamp includes the same content as the individual courses you've purchased, so you won’t miss anything.
• Your progress in the individual courses will automatically transfer to the Bootcamp.
• The Bootcamp price will automatically be reduced by the amount you’ve already paid for these individual courses.
This way, you can pay in smaller amounts without missing any part of the learning journey!
Q.2
I have already purchased other Codebasics courses like SQL and Python. Will I have to pay the full amount to enroll in this bootcamp?
No, you won’t have to pay the full amount to enroll in the Data Analytics Bootcamp if you’ve already purchased courses that are part of the Bootcamp. Here's how it works:
• The Data Analytics Bootcamp includes the following individual courses: Python, SQL, Math & Statistics, Machine Learning (ML), and Deep Learning (DL).
• If you have purchased any of these courses individually, the amount you’ve already paid will be deducted from the Bootcamp price.
• For example, if you’ve previously purchased the Python and SQL courses, their combined price will be subtracted from the total Bootcamp price.
This ensures you only pay for the additional content in the Bootcamp and not for the courses you already own. Your learning and progress will remain seamless, as the individual courses are integrated into the Bootcamp.
This way, you get to benefit fully from the Bootcamp while only paying for the courses you haven’t purchased yet!
Q.3
I’m already enrolled in the Codebasics Data Analytics Bootcamp. Will I have to pay the full amount to enroll in this bootcamp?
No, since the SQL and Python courses in the Data Analytics Bootcamp are also part of this bootcamp, the price you’ve paid for those courses will be deducted from this bootcamp price.
Q.4
I have already purchased Data Science Foundation Bootcamp? Will i have to pay full amount to enroll in the Data Science Ultimate Bootcamp?
Not at all! We deeply value your commitment to learning with us. The amount you’ve already paid for the Foundation Bootcamp will be deducted from the fee for the Data Science Ultimate Bootcamp, so you only invest in the additional content and features.
Q.1
What if I don’t like this bootcamp? Is there a refund policy?
Yes, we offer a no-questions-asked refund policy. No hidden conditions—if you request a refund within 30 days of enrollment, you will receive a 100% refund.
Q.2
How can I request a refund?
Simply email us at [email protected] with your name, course name, course purchase date, and the registered email ID you used on the Codebasics website. We will initiate the refund after verifying your details and eligibility according to our refund policy.
Q.3
How can I trust Codebasics?
Till now more than 50000 + learners have benefitted from the quality of our paid courses. You can check the review section and also we have attached their LinkedIn profiles so that you can connect with them and ask directly.
You can check here: https://codebasics.io/testimonials
Q.1
Are the lectures going to be LIVE?
To keep the bootcamp affordable, we have kept the lectures as self-paced videos that you can watch at your convenience. However, there will be monthly webinars and discord community support, through which you can interact with instructors and mentors.
Q.2
Do you provide any Virtual Internship?
Yes, the bootcamp includes two virtual internships designed to give you real-world experience, spanning over 8 weeks.
Q.3
Can I expect to work on real-world datasets during the course?
The datasets used in this bootcamp are crafted from scratch to mimic the real business world, consolidating our years of experience. The course includes projects and case studies based on real-world datasets across multiple domains, such as healthcare, finance, banking, logistics, etc.
Q.4
Does this bootcamp provide lifetime access to the content?
Yes! You’ll have lifetime access to the course content, along with free upgrades to the course material. However, any features or additional modules introduced later may not be part of this access.
Q.5
Will I get a recording of Zoom monthly webinars if I can't attend the live session?
Yes, Monthly webinar recordings will be provided. As a boot camper, you will always have access to the latest monthly webinar along with the recordings from the previous 2 months.
Q.1
What if I have an unrelated degree but want to pursue data science?
A computer science degree or an IT degree is not required. We have many success stories of folks from non-tech backgrounds successfully cracking into a data science career.
However, a strong interest in coding and a willingness to learn Mathematics and Statistics are important pre-requisites. If you're unsure, take our Suitability Test to check if this is the right fit for you.
Suitability Test: https://codebasics.io/survey/find-your-match-ds
Q.2
What prior knowledge or skills do I need before enrolling in this bootcamp?
The only thing you need as a prerequisite is the passion and interest in learning AI and Data Science. Actual prerequisites such as Python, Math, and Statistics basics are all covered in this course.
To maximize the benefits of the bootcamp, we also encourage learners to network on LinkedIn by sharing their projects and achievements. This is a great way to showcase your skills and expand your professional network.
Q.3
How much coding/programming do I need to know to become a Data Scientist?
The first module in our bootcamp is the Python module where we teach everything from basics. You don't need to be an expert coder, but proficiency in data manipulation, visualization, and basic algorithms is good to have.
Q.4
Can I enroll in the bootcamp if I have no background in mathematics or statistics?
Yes. In this bootcamp, we have a module on Math and statistics. This module designed for absolute beginners, simplifies learning Math and Statistics through project-based learning with a real dataset, focusing on practical data science applications.
Q.5
I am confused about whether to pursue my career as a data scientist.
Sure, take this survey https://codebasics.io/survey/find-your-match-ds This survey tells you how much your natural interest matches the data scientist role. It also provides recommendations on the learning path.
Q.1
What kind of support is available if I have questions during the bootcamp?
We have created every lecture of this bootcamp with the motive to explain everything in an easy-to-understand manner. While working on these lectures you could make mistakes in steps or have some doubts. You need to commit yourself to hold patience, make efforts & figure out the solution by googling in order to become truly job ready.
For any questions, that Google cannot answer or if you hit a wall - we got you covered!
You can join our active discord community, which is a dedicated platform to discuss & clear your doubts with fellow learners & mentors.
Q.2
Is there a community platform where I can interact with other students?
Yes, you’ll be part of an active Discord community where you can interact, and learn from one another.
Q.3
Do you provide any job assistance?
Yes, the bootcamp includes ATS resume building, portfolio website creation,building online credibility, job application tips, and interview preparation.
Our goal is to help you confidently step into the job market with the right tools and guidance.
Q.4
Will this data science bootcamp guarantee me a job?
We do not believe in making impractical guarantees. Our promise is to prepare you with the most relevant skills, timeless principles, and practical knowledge to help you secure a job.
Q.1
What certificates will I receive from the bootcamp?
You will receive:
Certificates for Tools: Individual certificates for Python, SQL, Math & Statistics, Machine Learning, Deep Learning, NLP, and Generative AI.
Bootcamp Certificate: A certificate upon completing the entire bootcamp.
Internship Letters: Letter of completion for Virtual Internship.
Q.2
How do I get the certificate?
You need to complete at least 70% of the course and score 70% or more on the final quiz. After completing the quiz, simply click “Complete and Get Certificate” to download your certificate.
Q.1
Do you have an EMI (Instalment) option?
Our website doesn’t currently support an EMI option, but you can still pay in smaller installments by purchasing the Bootcamp courses individually. Here's how:
Step 1: Buy the Python course and complete it at your own pace.
Step 2: Once you’ve completed Python, buy the Math and Statistics course.
Step 3: Finally, buy the Bootcamp when you're ready.
Note: If you'd like to break your payments into even smaller amounts, you can also buy other Bootcamp courses like Machine Learning or Deep Learning individually before purchasing the full Bootcamp.
How It Aligns With Your Learning ⤵️
• The Bootcamp includes the same content as the individual courses you've purchased, so you won’t miss anything.
• Your progress in the individual courses will automatically transfer to the Bootcamp.
• The Bootcamp price will automatically be reduced by the amount you’ve already paid for these individual courses.
This way, you can pay in smaller amounts without missing any part of the learning journey!
Q.2
I have already purchased other Codebasics courses like SQL and Python. Will I have to pay the full amount to enroll in this bootcamp?
No, you won’t have to pay the full amount to enroll in the Data Analytics Bootcamp if you’ve already purchased courses that are part of the Bootcamp. Here's how it works:
• The Data Analytics Bootcamp includes the following individual courses: Python, SQL, Math & Statistics, Machine Learning (ML), and Deep Learning (DL).
• If you have purchased any of these courses individually, the amount you’ve already paid will be deducted from the Bootcamp price.
• For example, if you’ve previously purchased the Python and SQL courses, their combined price will be subtracted from the total Bootcamp price.
This ensures you only pay for the additional content in the Bootcamp and not for the courses you already own. Your learning and progress will remain seamless, as the individual courses are integrated into the Bootcamp.
This way, you get to benefit fully from the Bootcamp while only paying for the courses you haven’t purchased yet!
Q.3
I’m already enrolled in the Codebasics Data Analytics Bootcamp. Will I have to pay the full amount to enroll in this bootcamp?
No, since the SQL and Python courses in the Data Analytics Bootcamp are also part of this bootcamp, the price you’ve paid for those courses will be deducted from this bootcamp price.
Q.4
I have already purchased Data Science Foundation Bootcamp? Will i have to pay full amount to enroll in the Data Science Ultimate Bootcamp?
Not at all! We deeply value your commitment to learning with us. The amount you’ve already paid for the Foundation Bootcamp will be deducted from the fee for the Data Science Ultimate Bootcamp, so you only invest in the additional content and features.
Q.1
What if I don’t like this bootcamp? Is there a refund policy?
Yes, we offer a no-questions-asked refund policy. No hidden conditions—if you request a refund within 30 days of enrollment, you will receive a 100% refund.
Q.2
How can I request a refund?
Simply email us at [email protected] with your name, course name, course purchase date, and the registered email ID you used on the Codebasics website. We will initiate the refund after verifying your details and eligibility according to our refund policy.
Q.3
How can I trust Codebasics?
Till now more than 50000 + learners have benefitted from the quality of our paid courses. You can check the review section and also we have attached their LinkedIn profiles so that you can connect with them and ask directly.
You can check here: https://codebasics.io/testimonials
The only Bootcamp you need to upskill or become job-ready in today’s competitive market.
Learning knows no limits. Here’s to your journey of seamless learning. Pick your preferred course from the list of paid & free resources.
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