Module 1: Python Overview

• Role of Python in Machine Learning

• Data Types, Operators, Conditional Statements, Loops

• Data Structures in Python: Lists, Dictionaries, Tuples, Sets

• Functions and Modules

• File Handling: Reading and Writing Files

Module 2: Introduction to Machine Learning

• Introduction to Machine Learning and its Applications

• Types of Machine Learning: Supervised, Unsupervised

• Overview of Machine Learning Workflow

• Setting Up ML Environment (Python, Jupyter Notebooks, ML Libraries)

• Introduction to Python Libraries for Machine Learning (NumPy, Pandas, Matplotlib, Seaborn, Scikit-learn)

Module 3: Data Wrangling

• Introduction to Series & DataFrames

• Handling Missing Values and Outliers

• Data Visualization & Charts

• Categorical Data Encoding: One-Hot Encoding, Label Encoding

• Feature Scaling: Normalization vs Standardization

• Feature Selection and Dimensionality Reduction

• Data Splitting

• Projects

• Analysis of Titanic Dataset

Module 4: Introduction to ML Linear Algorithms.

• Linear Regression

• Simple and Multiple Linear Regression

• Evaluation Metrics: MSE, RMSE, R²

• Regularization techniques

• Logistic Regression

• Binary Classification

• Sigmoid Function

• Evaluation Metrics: Accuracy, Precision, Recall, F1 Score, Confusion matrix, AUC

• Projects

• House Price Prediction using Linear Regression

• Heart Disease Prediction using Logistic Regression

Module 5: Core Classification and Ensemble Algorithms

• Decision Trees

• Random Forest

• k-Nearest Neighbors (KNN)

• Support Vector Machines (SVM)

• Naive Bayes

• Projects

• Breast Cancer Prediction - Analyzing and finding the best model by comparing.

Module 6: Model Evaluation and Tuning

• Cross-Validation and Train-Test Split

• Bias-Variance Tradeoff

• Hyperparameter Tuning using Grid Search and Random Search

• Model Overfitting and Underfitting

• Imbalanced datasets

Module 7: Clustering and Unsupervised Learning

• Introduction to Clustering

• K-Means Clustering

• Hierarchical Clustering

• DBSCAN

• Dimensionality Reduction using t-SNE

• Evaluation Metrics for Unsupervised Learning

• Projects

• Customer Segmentation

Module 8: Capstone Project - Hand Digit Image Classification

• End-to-End Machine Learning Project

• Problem Definition

• Data Exploration and Preprocessing

• Model Selection, Training, and Evaluation

• Hyperparameter Tuning

• Model Deployment - Joblib, Pickle

Module 9: Generative-AI

• Fundamental concepts

• Transformer architecture

• Prompt Engineering

• RAG (Retrieval-Augmented Generation)

• Project

• Personalized Chat-Bot using LLMs & Gradio