Gen AI & Data Science Bootcamp 3.0: With Practical Job Placement Support & Virtual Internship

1.1: Course Content Overview

2.1: Project 1: Hospitality Domain Data Analysis

2.2: Project 2: Expense Tracking System

3.1: Peter Pandey’s Journey and Need to Learn Python

3.2: Python Installation - Windows

3.3: Python Installation - Linux

3.4: Python Installation - Mac

3.5: How to Download Code and Get Help If I Have a Question?

4.1: Variables

4.2: Numbers

4.3: Strings

4.4: Strings - Exercise

4.5: Quiz: Variables, Numbers and Strings

4.6: Peter shares his Coding Fear with Tony

4.7: Exercise: Variables, Numbers and Strings

4.8: Chapter Summary

5.1: Lists

5.2: Install Pycharm

5.3: If Condition

5.4: For Loop

5.5: Quiz: Lists, If Condition and For Loop

5.6: Peter Found Something Interesting to Practice

5.7: Exercise: Lists, If Condition and For Loop

5.8: Chapter Summary

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

7.1: Classes and Objects

7.2: Operator Overloading

7.3: Inheritance

7.4: Exception Handling

7.5: __main__ Function

7.6: Quiz: Classes, Exception Handling

7.7: Using Python in a Real Time Problem

7.8: Exercise: Classes and Exception Handling

7.9: Chapter Summary

8.1: Introduction and Benefits

8.2: Basic Operations

8.3: Matrix Operations

8.4: Slicing, Stacking

8.5: Quiz: NumPy

8.6: Peter Solves a Real Time Problem Using NumPy

8.7: Exercise: NumPy

8.8: Chapter Summary

9.1: Pandas Introduction and Installation

9.2: Dataframe Basics

9.3: Read, Write Excel and CSV Files

9.4: Handle NA values - Part 1

9.5: Handle NA values - Part 2

9.6: Group by

9.7: Concat and Merge

9.8: Data Visualization Using Matplotlib and Seaborn

9.9: Quiz: EDA using Python

10.1: Problem Statement, OLTP vs OLAP, ETL, Data Warehouse

10.2: Data Understanding: CSV Files

10.3: Data Understanding: Fact vs Dim table, Star vs Snowflake Schema

10.4: Data Exploration

10.5: Data Cleaning

10.6: Data Transformation

10.7: Insights Generation

11.1: Introduction

11.2: Set and Frozenset

11.3: Lists, Dict and Set Comprehensions

11.4: Quiz: Comprehensions and Sets

11.5: Peter gets Adhoc tasks on Advanced Topics

11.6: Exercise: Comprehensions and Sets

11.7: PEP8 Naming Convention

11.8: Code Debugging Using PyCharm

11.9: Chapter Summary

12.1: Working with JSON

12.2: Generators and Iterators

12.3: Decorators

12.4: Quiz: JSON, Generators and Decorators

12.5: Exercise: JSON, Generators and Decorators

12.6: Chapter Summary

13.1: What is API?

13.2: Calling APIs With requests Package

13.3: Building APIs With FastAPI

13.4: Quiz: APIs

13.5: Peter fetches data using APIs

13.6: Exercise: APIs

13.7: Chapter Summary

14.1: Logging

14.2: Automated Testing with Pytest

14.3: MySQL Setup: Windows

14.4: MySQL Setup: Linux, Mac

14.5: Working with MySQL in Python

14.6: Data Validation with Pydantic

14.7: Quiz: Logging, Pytest, Pydantic and Databases

14.8: Exercise: Pytest, Pydantic

14.9: Chapter Summary

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

16.1: Final Quiz

17.1: Python Interview Question Bank

18.1: Transition to Online Credibility

2.1: Introduction to online credibility / open-source contribution

3.1: Set Up Your LinkedIn Profile

3.2: LinkedIn Banners

4.1: Add your Python Project to GitHub

4.2: Share Your Python Skill to the World

5.1: Knowledge Check

5.2: Checkpoint

2.1: Install MySQL: Windows

2.2: Install MySQL: Linux, Mac

2.3: Import Movies Dataset in MySQL

2.4: Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)

2.5: Exercise - Retrieve Data Using Text Query (SELECT, WHERE, DISTINCT, LIKE)

2.6: Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)

2.7: Exercise - Retrieve Data Using Numeric Query (BETWEEN, IN, ORDER BY, LIMIT, OFFSET)

2.8: Summary Analytics (MIN, MAX, AVG, GROUP BY)

2.9: Exercise - Summary Analytics (MIN, MAX, AVG, GROUP BY)

2.10: HAVING Clause

2.11: Calculated Columns (IF, CASE, YEAR, CURYEAR)

2.12: Exercise - Calculated Columns (IF, CASE, YEAR, CURYEAR)

2.13: The Data God’s Blessing

2.14: Quiz

3.1: Why do We Need Multiple Tables?

3.2: SQL Joins (INNER, LEFT, RIGHT, FULL)

3.3: Exercise - SQL Joins (INNER, LEFT, RIGHT, FULL)

3.4: Cross Join

3.5: Analytics on Tables

3.6: Join More Than Two Tables

3.7: Exercise - Join More Than Two Tables

3.8: Quiz

4.1: SQL Final Quiz

5.1: Transition to Maths and Stats

2.1: Types of Data

2.2: Pie Chart and Bar Chart

2.3: Quiz

2.4: Histograms and Line Chart

2.5: Quiz

2.6: Scatter and Bubble Plot

2.7: Quiz

2.8: Univariate vs. Bivariate vs. Multivariate Analysis

2.9: Chapter Summary

3.1: Descriptive vs. Inferential Statistics

3.2: Measures of Central Tendency: Mean, Median, Mode

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

4.1: Probability Basics

4.2: Quiz

4.3: Addition and Multiplication Rule

4.4: Quiz

4.5: Conditional Probability and Bayes Theorem

4.6: Quiz

4.7: Chapter Summary

5.1: What Is a Distribution?

5.2: Skewness

5.3: Normal Distribution

5.4: Detect Outliers Using Normal Distribution

5.5: Quiz

5.6: Exercise

5.7: Z Score

5.8: Standard Normal Distribution (SND)

5.9: I Need a Favour

5.10: Quiz

5.11: Exercise

5.12: Chapter Summary

6.1: Chapter Overview

6.2: Bashneer Grower’s Bank Venture

6.3: AtliQo Credit Card Launch: Problem Statement

6.4: Project Kick-off Meeting

6.5: Project Management Using Kanban

6.6: Tasks Breakdown and JIRA Board Setup

6.7: Meeting: Tony, Peter, Natasha

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

8.1: 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

9.1: 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

10.1: Campaign Planning

10.2: Hypothesis Testing and Decision Making

10.3: Final Stakeholder Meeting

11.1: T-test and Student’s t-distribution

11.2: Case Study - Exam Score

11.3: Chi-squared Distribution

11.4: Chi-squared Test of Goodness of Fit

11.5: Chi-squared Test of Independence

12.1: Final Quiz

13.1: Transition to Online Credibility and Domain Knowledge

2.1: Update Headline, Experience, etc, Sections on LinkedIn

2.2: How to Engage Meaningfully on LinkedIn

2.3: How to approach people on LinkedIn

2.4: List of Influencers to Follow

3.1: Resources to build domain knowledge

4.1: Knowledge Check

4.2: Checkpoint

5.1: Transition to Machine Learning

2.1: Who is Peter Pandey?

2.2: Peter Pandey’s journey to learn Machine Learning

2.3: What is Machine Learning?

2.4: Classification vs Regression

2.5: Supervised vs Unsupervised Learning

3.1: Simple Linear Regression

3.2: Multiple Linear Regression

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

4.1: Introduction to Classification

4.2: Logistic Regression: Binary Classification

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

5.1: What is Ensemble Learning?

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

6.1: 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

7.1: 10 Stages of AI Project Life Cycle

7.2: Requirements and Scope of Work (SOW)

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

8.1: 3 Ways of Doing Feature Engineering

8.2: Feature Selection Using Correlation

8.3: Feature Selection Using Variance Inflation Factor (VIF)

8.4: VIF: Practical Implementation (Salary Prediction)

8.5: Quiz

8.6: Exercise

8.7: Chapter Summary

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

10.1: The Rise of AtliQ AI

10.2: Project Charter Meeting

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

11.1: Peter's Promotion: New Project

11.2: Domain Understanding: NBFC & Credit Approvals

11.3: Scope of Work & Tech Architecture

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

12.1: What is ML Ops?

12.2: Importance of ML Ops in Your Career

12.3: ML Flow: Purpose and Overview

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

13.1: Final Quiz

14.1: ML Interview Question Bank

15.1: Transition to JAP, Portfolio Website and ATS Resume

2.1: What is ATS and Why it is Important?

2.2: Build an ATS Resume using Resume Builder and Checklist

2.3: Improve the ATS Score of Your Resume

2.4: Sample Resumes

3.1: Getting Started: Creating a Portfolio Website

3.2: Add Basic Info: Intro Video

3.3: Add Basic Info: Profile Picture

3.4: Add Basic Info: About Yourself

3.5: Add Resume and Skills

3.6: Add Projects & Certifications

3.7: Generate Portfolio and Share it With Potential Recruiters

4.1: Create Your Own Posts

4.2: Advanced Hacks

4.3: Knowledge Check

4.4: Checkpoint

5.1: Transition to Deep Learning

2.1: Neural Networks: The Foundation of Deep Learning

2.2: Deep Learning vs Statistical ML: When to Use What?

2.3: Neural Network Architectures

2.4: Real World Applications of Deep Learning

2.5: Tooling: PyTorch vs Tensorflow

2.6: Tooling: GPU, TPU

2.7: Quiz

2.8: Chapter Summary

3.1: What is Neuron?

3.2: Perceptron and Multilayer Perceptron (MLP)

3.3: Neural Network Intuition: Insurance Prediction

3.4: The Purpose of Activation Function

3.5: Activation Functions: Sigmoid, ReLU, Tanh, SoftMax

3.6: Quiz

3.7: Chapter Summary

4.1: PyTorch Installation

4.2: Matrix Fundamentals

4.3: PyTorch Tensor Basics 1

4.4: PyTorch Tensor Basics 2

4.5: Derivative and Partial Derivative

4.6: Chain Rule

4.7: Autograd in PyTorch

4.8: Numpy Arrays vs PyTorch Tensors

4.9: Quiz

4.10: Exercise

4.11: Chapter Summary

5.1: Training through Backpropagation

5.2: Gradient Descent: Theoretical Foundation

5.3: Gradient Descent: PyTorch Implementation

5.4: Batch GD vs Mini Batch GD vs SGD

5.5: Mini Batch GD and SGD: PyTorch Implementation

5.6: Quiz

5.7: Exercise

5.8: Chapter Summary

6.1: Employee Bonus Prediction

6.2: Datasets and DataLoaders

6.3: Handwritten Digits Classification

6.4: Classification Report and Confusion Matrix

6.5: Cost Function: Binary Cross Entropy (a.k.a. Log Loss)

6.6: Cost Function: Cross Entropy

6.7: Quiz

6.8: Exercise

6.9: Chapter Summary

7.1: Model Optimization Overview

7.2: Exponentially Weighted Moving Average

7.3: Gradient Descent with Momentum

7.4: RMSprop

7.5: Adam

7.6: Optimizers in Action with Fashion MNIST

7.7: Quiz

7.8: Exercise

7.9: Chapter Summary

8.1: What is Regularization?

8.2: Dropout Regularization

8.3: L1, L2 Regularization

8.4: Batch Normalization

8.5: Early Stopping

8.6: Quiz

8.7: Exercise

8.8: Chapter Summary

9.1: What is Hyperparameter Tuning?

9.2: GridSearchCV, RandomSearchCV

9.3: Optuna

9.4: Quiz

9.5: Exercise

9.6: Chapter Summary

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

11.1: Sequence Models

11.2: RNN in Depth

11.3: Types of RNN

11.4: Vanishing Gradient Problem

11.5: LSTM

11.6: GRU

11.7: Quiz

11.8: Chapter Summary

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

13.1: 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

14.1: Quiz

15.1: DL Interview Question Bank

16.1: Transition to NLP

2.1: Tokenization Techniques

2.2: Tokenization in Spacy

2.3: Language Processing Pipeline in Spacy

2.4: Stemming & Lemmatization

2.5: Part of Speech (POS) Tagging

2.6: Stop Words

2.7: Named Entity Recognition (NER)

2.8: Regular Expressions (Regex)

2.9: Quiz

2.10: Exercise

2.11: Chapter Summary

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

4.1: Introduction to Hugging Face

4.2: Pipelines

4.3: BERT, DistilBERT, RoBERTa, ALBERT

4.4: Tokenizers

4.5: Model Fine Tuning

4.6: Quiz

4.7: Exercise

4.8: Chapter Summary

5.1: Final Quiz

6.1: Transition to Gen AI

2.1: What is Generative AI?

2.2: Traditional AI vs Gen AI

2.3: What are AI Agents and Agentic AI?

2.4: Gen AI vs AI Agents vs Agentic AI

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

3.1: What are Large Language Models?

3.2: How LLMs Work?

3.3: Context Window, Temperature, Top-p and Top-k

3.4: Challenges: Hallucinations, Security and Cost

3.5: What is a Vector Database?

3.6: What is RAG (Retrieval Augmented Generation)?

3.7: Quiz

3.8: Exercise

3.9: Chapter Summary

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

5.1: Chromadb: Introduction and Installation

5.2: Basic Operations in Chromadb

5.3: Add, Update, Delete, Query

5.4: Metadata Filtering

5.5: Euclidean and Cosine Distance

5.6: Quiz

5.7: Exercise

5.8: Chapter Summary

6.1: Problem Statement

6.2: RAG Based Technical Architecture

6.3: Document Loaders

6.4: Text Splitters

6.5: Store Data in Vector Store (Chroma DB)

6.6: Retrieval and Answer Generation

6.7: Streamlit UI

6.8: Learning Lessons

6.9: Exercise

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

8.1: Agency in AI

8.2: Build Your First Agent using Llama and Agno

8.3: Agent with Custom Tool

8.4: What are Reasoning Models?

8.5: Building a Reasoning Agent with Agno

8.6: Multimodal Agent

8.7: Other Frameworks: Smolagents, Google ADK

8.8: Quiz

9.1: What is Model Context Protocol (MCP)?

9.2: Build Your First MCP Server: Leave Management

9.3: Prebuilt MCP Servers

9.4: What is A2A Protocol?

9.5: Quiz

9.6: Exercise

10.1: Build Your First Multi Agent Program

10.2: When to consider Multi-Agent Systems?

10.3: Design Patterns for Multi-Agent Systems

10.4: Route Agent

10.5: Quiz

11.1: Introduction to Agentic AI Evaluation

11.2: Functional Evaluation

11.3: Hands on Functional Eval in Agno

11.4: Safety and Guardrails

11.5: Operational Metrics

11.6: Hands on Perf Eval in Agno

11.7: Quiz

12.1: Problem Statement & Tech Architecture

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

13.1: Introduction to Fine-Tuning

13.2: Low-Rank Adaptation (LoRA)

13.3: Quantization Basics

13.4: QLoRA

13.5: Fine-Tuning Llama with Unsloth

13.6: Quiz

13.7: Exercise

14.1: Ethical Challenges in Gen AI

14.2: Hallucination and Misinformation

14.3: Bias

14.4: PII and Privacy Laws

14.5: Environmental Impact

14.6: Copyrights and Intellectual Property

14.7: Quiz

14.8: Chapter Summary

15.1: Final Quiz

16.1: Gen AI Interview Question Bank

17.1: LangGraph Tutorial Bonus

18.1: Crew AI Tutorial Bonus

19.1: Introduction & Project Overview

19.2: Building a Local Agent

19.3: AWS Account Setup

19.4: AWS CLI Setup

19.5: Deploying Agent on AgentCore Runtime

19.6: Add Memory to Our Agent

19.7: Overview of Observability & Other Features

19.8: Quiz

19.9: AgentCore Interview Questions

20.1: Transition to Interview Preparation, Job Application and Virtual Internships

2.1: Introduction to the Job Application Playbook

2.1: Interview Question Bank: Technical

3.1: Transition to Virtual Internship - (Trust the process)

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.

3.1: 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!

4.1: 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

5.1: Get Your Letter Of Completion

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!

3.1: 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.

4.1: Let's Dive Right Into Week 8!

4.2: Create a Streamlit UI

4.3: Incoming Task Email 1

4.4: Have you completed the Streamlit UI?

4.5: Manage Your GitHub repository

4.6: Have you pushed your code to GitHub?

4.7: End Note

4.8: Get Your Letter

1.1: Text Classification: Support Ticket Prioritization

1.2: Image Classification: Crop Yield Detection New

1.3: RAG-Based Gen AI: ChatGPT for Private Organizational Data

1.4: Chatbot: Food Delivery Chatbot

1.5: LLM Powered Real Estate Chatbot New

1.6: Recommendation System: Book Recommendations

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

1.1: Subqueries

1.2: ANY, ALL Operators

1.3: Co-Related Subquery

1.4: Exercise: Subqueries

1.5: Common Table Expression (CTE)

1.6: CTE Benefits & Other Applications

1.7: Exercise: CTE

1.8: Peter Pandey Flexes his SQL Skills

1.9: Quiz

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

3.1: The Rise of Databases at AtliQ

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

4.1: 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

5.1: 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

6.1: 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

7.1: Final Quiz

2.1: How to use Discord

3.1: Sequence Learning with RNN: A Practical Approach to Retail Sales Forecasting NEW

3.2: Production-Ready RAG: Build, Evaluate & Scale AI Systems for Enterprises

3.3: How to Stand Out in the Job Market Through Open Source Contributions