Module 2: Python for Back-End Development

• Flask Web Framework
• Django Web Framework

Module 3: Databases and Back-End Integration

• SQL with Python
• NoSQL with Python

Module 4: Web APIs and RESTful Services

Module 5: User Authentication and Authorization

• AJAX and Fetch API
• WebSockets

Module 6: Front-End and Back-End Communication

• Project Setup
• Frontend-Backend Integration
• Build and Deployment

Module 7: Testing and Debugging

• Unit Testing in Python
• Front-End Testing
• End-to-End Testing

Module 8: Deployment and Cloud Services

• Web Application Deployment
• Docker
• CI/CD

Module 10: Advanced Topics

• Blogging Platform
• E-Commerce Website
• Social Media Application
• Real-Time Chat Application

Module 6: Front-End and Back-End Communication

• Microservices Architecture
• GraphQL
• Machine Learning Integration

Module 2: Mathematics & Statistics for ML

• Linear Algebra:
  1. Vectors, Matrices, and Tensors
  2. Eigenvalues and Eigenvectors
• Probability and Statistics:
  1. Probability Theory
  2. Random Variables
  3. Hypothesis Testing
• Calculus:
  1. Differentiation and Integration in ML Models
  2. Gradient Descent Optimization

Module 3: Python for AI & ML

• Python Basics:
  1. Data Structures: Lists, Tuples, Dictionaries
  2. Control Structures: Loops, Conditional Statements
• Libraries for AI & ML:
  1. NumPy, Pandas, Matplotlib, Seaborn
  2. Scikit-learn, TensorFlow, PyTorch

Module 4: Data Preprocessing & Visualization

• Data Collection and Cleaning
• Handling Missing Data
• Feature Engineering
• Data Normalization and Scaling
• Exploratory Data Analysis (EDA):
  1. Visualizations using Matplotlib and Seaborn

Module 5: Supervised Learning

• Regression Algorithms:
  1. Linear Regression
  2. Logistic Regression
  3. Polynomial Regression
• Classification Algorithms:
  1. Decision Trees
  2. Random Forest
  3. Support Vector Machines (SVM)
  4. Naive Bayes
• Model Evaluation:
  1. Confusion Matrix
  2. Precision, Recall, F1-Score
  3. ROC-AUC Curve

Module 6: Unsupervised Learning

• Clustering:
  1. K-Means
  2. Hierarchical Clustering
• Dimensionality Reduction:
  1. Principal Component Analysis (PCA)
  2. t-SNE
• Anomaly Detection

Module 7: Neural Networks & Deep Learning

• Introduction to Neural Networks:
  1. Perceptron and Multi-Layer Perceptrons (MLP)
  2. Activation Functions
• Deep Learning Frameworks:
  1. TensorFlow and Keras Basics
• Convolutional Neural Networks (CNN):
  1. Image Recognition
  2. Object Detection
• Recurrent Neural Networks (RNN):
  1. Sequence Modeling
  2. Natural Language Processing (NLP)

Module 8: Reinforcement Learning

• Basics of Reinforcement Learning
• Markov Decision Processes (MDP)
• Q-Learning and Deep Q-Learning
• Applications of Reinforcement Learning

Module 9: Natural Language Processing (NLP)

• Text Preprocessing:
  1. Tokenization, Lemmatization, Stemming
  2. Bag of Words and TF-IDF
• Sentiment Analysis
• Chatbot Development
• Introduction to Transformers and BERT

Module 10: Capstone Projects

• End-to-End ML Project:
  1. Problem Statement, Data Preprocessing, Model Building, Deployment
• Real-life Scenarios:
  1. Image Classification
  2. Predictive Analytics
  3. NLP-based Sentiment Analysis