1.2: Introduction to Python and Development Setup

2.1: Introduction to Data Science Essentials

2.2: Introduction to NumPy for Numerical Computing

2.3: Advanced NumPy Operations

2.4: Introduction to Pandas for Data Manipulation

2.5: Data Cleaning and Preparation with Pandas

2.6: Data Aggregation and Grouping in Pandas

2.7: Data Visualization with Matplotlib and Seaborn

2.8: Exploratory Data Analysis (EDA) Project

3.1: Introduction to Mathematics for Machine Learning

3.2: Linear Algebra Fundamentals

3.3: Advanced Linear Algebra Concepts

3.4: Calculus for Machine Learning (Derivatives)

3.5: Calculus for Machine Learning (Integrals and Optimization)

3.6: Probability Theory and Distributions

3.7: Math-Driven Mini Project – Linear Regression from Scratch

3.8: Statistics Fundamentals

4.1: Introduction to Probability and Statistics for Machine Learning

4.2: Probability Theory and Random Variables

4.3: Probability Distributions in Machine Learning

4.4: Statistical Inference - Estimation and Confidence Intervals

4.5: Hypothesis Testing and P-Values

4.6: Types of Hypothesis Tests

4.7: Correlation and Regression Analysis

4.8: Statistical Analysis Project – Analyzing Real-World Data

5.1: Introduction to Machine Learning

5.2: Machine Learning Basics and Terminology

5.3: Introduction to Supervised Learning and Regression Models

5.4: Advanced Regression Models – Polynomial Regression and Regularization

5.5: Introduction to Classification and Logistic Regression

5.6: Model Evaluation and Cross-Validation

5.7: k-Nearest Neighbors (k-NN) Algorithm

5.8: Supervised Learning Mini Project

6.1: Introduction to Feature Engineering and Model Evaluation

6.2: Introduction to Feature Engineering

6.3: Data Scaling and Normalization

6.4: Encoding Categorical Variables

6.5: Feature Selection Techniques

6.6: Creating and Transforming Features

6.7: Model Evaluation Techniques

6.8: Cross-Validation and Hyperparameter Tuning

7.1: Introduction to Advanced Machine Learning Algorithms

7.2: Introduction to Ensemble Learning

7.3: Bagging and Random Forests

7.4: Boosting and Gradient Boosting

7.5: Introduction to XGBoost

7.6: LightGBM and CatBoost

7.7: Handling Imbalanced Data

7.8: Ensemble Learning Project – Comparing Models on a Real Dataset

8.1: Introduction to Model Tuning and Optimization

8.2: Introduction to Hyperparameter Tuning

8.3: Grid Search and Random Search

8.4: Advanced Hyperparameter Tuning with Bayesian Optimization

8.5: Regularization Techniques for Model Optimization

8.6: Cross-Validation and Model Evaluation Techniques

8.7: Automated Hyperparameter Tuning with GridSearchCV and RandomizedSearchCV

8.8: Optimization Project – Building and Tuning a Final Model

9.1: Introduction to Neural Networks and Deep Learning Fundamentals

9.2: Introduction to Deep Learning and Neural Networks

9.3: Forward Propagation and Activation Functions

9.4: Loss Functions and Backpropagation

9.5: Gradient Descent and Optimization Techniques

9.6: Building Neural Networks with TensorFlow and Keras

9.7: Building Neural Networks with PyTorch

9.8: Neural Network Project – Image Classification on CIFAR-10

10.1: Introduction to Convolutional Neural Networks (CNNs)

10.2: Introduction to Convolutional Neural Networks

10.3: Convolutional Layers and Filters

10.4: Pooling Layers and Dimensionality Reduction

10.5: Building CNN Architectures with Keras and TensorFlow

10.6: Building CNN Architectures with PyTorch

10.7: Regularization and Data Augmentation for CNNs

10.8: CNN Project – Image Classification on Fashion MNIST or CIFAR-10

11.1: Introduction to Recurrent Neural Networks (RNNs) and Sequence Modeling

11.2: Introduction to Sequence Modeling and RNNs

11.3: Understanding RNN Architecture and Backpropagation Through Time (BPTT)

11.4: Long Short-Term Memory (LSTM) Networks

11.5: Gated Recurrent Units (GRUs)

11.6: Text Preprocessing and Word Embeddings for RNNs

11.7: Sequence-to-Sequence Models and Applications

11.8: RNN Project – Text Generation or Sentiment Analysis

12.1: Introduction to Transformers and Attention Mechanisms

12.2: Introduction to Attention Mechanisms

12.3: Introduction to Transformers Architecture

12.4: Self-Attention and Multi-Head Attention in Transformers

12.5: Positional Encoding and Feed-Forward Networks

12.6: Hands-On with Pre-Trained Transformers – BERT and GPT

12.7: Advanced Transformers – BERT Variants and GPT-3

12.8: Transformer Project – Text Summarization or Translation

13.1: Introduction to Transfer Learning and Fine-Tuning

13.2: Introduction to Transfer Learning

13.3: Transfer Learning in Computer Vision

13.4: Fine-Tuning Techniques in Computer Vision

13.5: Transfer Learning in NLP

13.6: Fine-Tuning Techniques in NLP

13.7: Domain Adaptation and Transfer Learning Challenges

13.8: Transfer Learning Project – Fine-Tuning for a Custom Task

14.1: Introduction to Machine Learning Algorithms

14.2: Linear Regression Implementation in Python

14.3: Ridge and Lasso Regression Implementation in Python

14.4: Polynomial Regression Implementation in Python

14.5: Logistic Regression Implementation in Python

14.6: K-Nearest Neighbors (KNN) Implementation in Python

14.7: Support Vector Machines (SVM) Implementation in Python

14.8: Decision Trees Implementation in Python

14.9: Random Forests Implementation in Python

14.10: Gradient Boosting Implementation in Python

14.11: Naive Bayes Implementation in Python

14.12: K-Means Clustering Implementation in Python

14.13: Hierarchical Clustering Implementation in Python

14.14: DBSCAN (Density-Based Spatial Clustering of Applications w Noise) Implementation

14.15: Gaussian Mixture Models(GMM) Implementation in Python

14.16: Principal Component Analysis (PCA) Implementation in Python

14.17: t-Distributed Stochastic Neighbor Embedding (t-SNE) Implementation in Python

14.18: Autoencoders Implementation in Python

14.19: Self-Training Implementation in Python

14.20: Q-Learning Implementation in Python

14.21: Deep Q-Networks (DQN) Implementation in Python

14.22: Policy Gradient Methods Implementation in Python

14.23: One-Class SVM Implementation in Python

14.24: Isolation Forest Implementation in Python

14.25: Convolutional Neural Networks (CNNs) Implementation in Python

14.26: Recurrent Neural Networks (RNNs) Implementation in Python

14.27: Long Short-Term Memory (LSTM) Implementation in Python

14.28: Transformers Implementation in Python

15.1: What is Machine Learning?

15.2: Introduction to TensorFlow

15.3: TensorFlow vs. Other Machine Learning frameworks

15.4: Installing TensorFlow

15.5: Setting up your Development Environment

15.6: Verifying the Installation

16.1: Introduction to Tensors

16.2: Tensor Operations

16.3: Constants, Variables, and Placeholders

16.4: TensorFlow Computational Graph

16.5: Creating and Running a TensorFlow Session

16.6: Managing Graphs and Sessions

16.7: Building a Simple Feedforward Neural Network

16.8: Activation Functions

16.9: Loss Functions and Optimizers

17.1: Introduction to Keras API

17.2: Building Complex Models with Keras

17.3: Training and Evaluating Models

17.4: Introduction to CNNs

17.5: Building and Training CNNs with TensorFlow

17.6: Transfer Learning with Pre-trained CNNs

17.7: Introduction to RNNs

17.8: Building and Training RNNs with TensorFlow

17.9: Applications of RNNs: Language Modeling, Time Series Prediction

18.1: Saving and Loading Models

18.2: TensorFlow Serving for Model Deployment

18.3: TensorFlow Lite for Mobile and Embedded Devices

18.4: Introduction to Distributed Computing with TensorFlow

18.5: Introduction to Distributed Computing with TensorFlow

18.6: Scaling TensorFlow with TensorFlow Serving and Kubernetes

18.7: Introduction to TFX

18.8: Building End-to-End ML Pipelines with TFX

18.9: Model Validation, Transform, and Serving with TFX

19.1: Image Classification

19.2: Natural Language Processing

19.3: Recommender Systems

19.4: Object Detection

19.5: Building a Sentiment Analysis Model

19.6: Creating an Image Recognition System

19.7: Developing a Time Series Prediction Model

19.8: Implementing a Chatbot

20.1: Generative Adversarial Networks (GANs)

20.2: Reinforcement Learning with TensorFlow

20.3: Quantum Machine Learning with TensorFlow Quantum

20.4: Quantum Machine Learning with TensorFlow Quantum

20.5: TensorFlow Documentation and Tutorials

20.6: Online Courses and Books

20.7: TensorFlow Community and Forums

20.8: Summary of Key Concepts

20.9: Next Steps in Your TensorFlow Journey

21.1: Introduction to PyTorch

21.2: Getting Started with PyTorch

21.3: Getting Started with PyTorch

21.4: Autograd and Dynamic Computation Graphs

21.5: Building Simple Neural Networks

21.6: Loading and Preprocessing Data

21.7: Model Evaluation and Validation

21.8: Advanced Neural Network Architectures

21.9: Transfer Learning and Fine-Tuning

21.10: Handling Complex Data

21.11: Model Deployment and Production

21.12: Debugging and Troubleshooting

21.13: Distributed Training and Performance Optimization

21.14: Custom Layers and Loss Functions

21.15: Research-oriented Techniques

21.16: Integration with Other Libraries

21.17: Contributing to PyTorch and Community Engagement

22.1: Introduction to LangChain and Language Models

22.2: Project 1: Simple Text-Based Question Answering Bot

22.3: Project 2: Sentiment Analysis with LangChain

22.4: Project 3: Document Summarization Tool

22.5: Project 4: Keyword Extraction from Text

22.6: Project 5: LangChain-Powered Chatbot

23.1: Understanding AI Agents - How AI Agents Function

23.2: Introduction to AI Agents

23.3: Types of AI Agents

23.4: Technologies Behind AI Agents - Machine Learning and AI Agents

23.5: Natural Language Processing in AI Agents

23.6: AI Agents in Robotics

23.7: AI Agent Frameworks & Architectures - AI Agent Development Frameworks

23.8: Overview of AutoGPT for AI Agents

23.9: IBM Bee Framework for AI Agents

23.10: LangGraph for Stateful AI Agents

23.11: CrewAI for Collaborative AI Agents

23.12: Applications of AI Agents - AI Agents in Business Operations

23.13: AI Agents in Healthcare

23.14: AI Agents in Financial Systems

23.15: AI Agents in Entertainment

23.16: AI Agents in Smart Homes and IoT

23.17: Future Trends and Ethical Implications - The Future of AI Agents

23.18: Ethics in AI Agent Development

23.19: Legal and Regulatory Challenges for AI Agents

23.20: Broader Impact of AI Agents - Social and Economic Impacts of AI Agents

23.21: AI Agents and Human Collaboration

23.22: The Role of AI Agents in Scientific Research

23.23: AI Agents in Public Safety and National Defense

24.1: Hands-on AutoGen | IBM Bee | LangGraph | CrewAI | AutoGPT

24.2: Hands-on AutoGen

24.3: Hands-on IBM Bee Framework

24.4: Hands-on LangGraph

24.5: Hands-on CrewAI

24.6: Hands-on AutoGPT

25.1: Creating and Publishing GPTs to ChatGPT Store Part 1

25.2: Creating and Publishing GPTs to ChatGPT Store Part 2

25.3: Creating and Publishing GPTs to ChatGPT Store Part 3

26.1: Project 1: AI-Powered Text Summarizer with DeepSeek AI

26.2: Project 2: AI-Based Text Generation with DeepSeek AI

26.3: Project 3: Grammar and Spell Checker with DeepSeek AI

26.4: Project 4: Named Entity Recognition (NER) Tool with DeepSeek AI

26.5: Project 5: AI-Powered Sentiment Analysis with DeepSeek AI

26.6: Project 6: Customer Support Chatbot with DeepSeek AI

26.7: Project 7: Personal AI Assistant with DeepSeek AI

26.8: Project 8: AI Legal Assistant with DeepSeek AI

26.9: Project 9: Medical Symptom Checker with DeepSeek AI

26.10: Project 10: E-commerce Product Recommendation Bot with DeepSeek AI

26.11: Project 11: Automated Email Responder with DeepSeek AI

26.12: Project 12: AI-Powered Resume Generator with DeepSeek AI

26.13: Project 13: AI-Based Meeting Minutes Generator with DeepSeek AI

26.14: Project 14: Automated PDF Text Extractor with DeepSeek AI

26.15: Project 15: Content Writer AI with DeepSeek AI

26.16: Project 16: SQL Query Generator with DeepSeek AI

26.17: Project 17: Code Debugger AI with DeepSeek AI

26.18: Project 18: AI-Based Documentation Generator with DeepSeek AI

26.19: Project 19: AI-Powered API Tester with DeepSeek AI

26.20: Project 20: AI-Based Customer Feedback Analyzer with DeepSeek AI

26.21: Project 21: Real-Time AI News Summarizer with DeepSeek AI

26.22: Project 22: AI Financial Report Analyzerwith DeepSeek AI

26.23: Project 23: AI-Powered Job Application Screener with DeepSeek AI

26.24: Project 24: AI Research Paper Summarizer with DeepSeek AI

27.1: Introduction to MLOps Sessions

27.2: Overview of MLOps and its Importance

27.3: Evolution of Machine Learning Operations

27.4: Key Concepts in MLOps: Versioning, Automation, and Monitoring

27.5: MLOps vs. DevOps: Similarities and Differences

27.6: Hands-on: Set up a basic MLOps Project Structure (Git, Docker, Model Pipeline)

27.7: Introduction to Data Science to Production Pipeline Section

27.8: Overview of the ML Workflow: Data Preparation to Deployment

27.9: Experimentation vs. Production

27.10: Challenges in Deploying ML Models

27.11: Hands-on: Build an end-to-end pipeline for an ML model

27.12: Introduction to Infrastructure for MLOps Section

27.13: Introduction to Cloud Platforms (AWS, GCP, Azure)

27.14: Containerization with Docker

27.15: Kubernetes for Orchestrating ML Workloads

27.16: Setting up Local MLOps Environments

27.17: Hands-on: Containerize a simple ML model and deploy it locally using Kubernetes

28.1: Basic Calculator using Python

28.2: Image Classifier using Keras and TensorFlow

28.3: Simple Chatbot using predefined responses

28.4: Spam Email Detector using Scikit-learn

28.5: Handwritten Digit Recognition with MNIST dataset

28.6: Sentiment Analysis on text data using NLTK

28.7: Movie Recommendation System using cosine similarity

28.8: Predict House Prices with Linear Regression

28.9: Weather Forecasting using historical data

28.10: Basic Neural Network from scratch

28.11: Stock Price Prediction using historical data w/ simple Linear Regression

28.12: Predict Diabetes using logistic regression

28.13: Dog vs. Cat Classifier with CNN

28.14: Tic-Tac-Toe AI using Minimax Algorithm

28.15: Credit Card Fraud Detection using Scikit-learn

28.16: Iris Flower Classification using decision trees

28.17: Simple Personal Assistant using Python speech libraries

28.18: Text Summarizer using Gensim

28.19: Fake Product Review Detection using NLP techniques

28.20: Detect Emotion in Text using Natural Language Toolkit (NLTK)

28.21: Book Recommendation System using collaborative filtering

28.22: Predict Car Prices using Random Forest

28.23: Identify Fake News using Naive Bayes

28.24: Create a Resume Scanner using keyword extraction

28.25: Customer Churn Prediction using classification algorithms

28.26: Named Entity Recognition (NER) using spaCy

28.27: Predict Employee Attrition using XGBoost

28.28: Disease Prediction (e.g., Heart Disease) using ML Algorithms

28.29: Movie Rating Prediction using Collaborative Filtering

28.30: Automatic Essay Grading using BERT

29.1: What is Generative AI- AI Vs ML Vs DL Vs Generative AI

29.2: How Open AI ChatGPT or LLama3 LLM Models are Trained

29.3: Evolution OF LLM Models

29.4: All LLM Models Analysis

30.1: Complete Langchain Ecosystem

30.2: Creating Virtual Environment

31.1: Getting Started With Langchain And OpenAI

32.1: Introduction To Basic Components And Modules In Langchain

32.2: Data Ingestion With Documents Loaders

32.3: Text Splitting Techniques-Recursive Character Text Splitter

32.4: Text Splitting Technique-Character Text splitter

32.5: Text Splitting Technique-HTML Header Text Splitter

32.6: Text Splitting Technique-Recursive Json Splitter

32.7: Introduction To OpenAI Embedding

32.8: Ollama Embeddings

32.9: Huggingface Embeddings

32.10: VectorStores-FAISS

32.11: VectorStore And Retriever- ChromaDB

32.12: Going Forward

33.1: Building Important Components Of Langchain

33.2: Building GENAI Apps

33.3: Understanding Retrievers and Chains

33.4: Introduction To Ollama And Set Up

33.5: Simple Gen AI App Using Ollama

33.6: Tracking Gen AI App Using Langsmith

34.1: Getting Started With Open source Models Using Groq API

34.2: Building LLM, Prompt And Stroutput Chains With LCEL

34.3: Deploy Langserve Runnable And Chain As API

35.1: Building Chatbot With Message History Using Langchain

35.2: Working With Prompt Template And Message Chat History Using Langchain

35.3: Managing the Chat Conversation History Using Langchain

35.4: Working With VectorStore And Retriever

36.1: Building Conversational Q&A Chatbot With Message History

37.1: Introduction To The Q&A Chatbot

37.2: Creating Virtual Environment

37.3: Creating Prompt Template And Integrating Open AI API

37.4: Creating Streamlit Web App and Integrating Response With OpenAI API

37.5: Q&A Chatbot With Ollama And Open Source Models