Week-1. Introduction to Machine Learning

Types of ML (Supervised, Unsupervised, Reinforcement), ML Lifecycle

Week-2.Setting Up the Environment

Installing Libraries (scikit-learn, TensorFlow, PyTorch), Jupyter Notebook

Week-3.Data Preprocessing

Handling Missing Values, Feature Scaling, Encoding Categorical Data

Week-4. Exploratory Data Analysis (EDA)

Data Visualization, Outlier Detection, Feature Engineering

Week-5. Linear Regression

Simple & Multiple Linear Regression, Gradient Descent, Regularization

Week-6.Polynomial & Ridge Regression

Overfitting, Lasso Regression, Model Evaluation (R², RMSE)

Week-7. Logistic Regression

Binary & Multiclass Classification, ROC Curve, AUC

Week-8. Decision Trees & Random Forest

Entropy, Gini Index, Hyperparameter Tuning

Week-9.Support Vector Machines (SVM)

Kernels, Hyperplanes, Soft Margin SVM

Week-10. Clustering Techniques

K-Means, Hierarchical Clustering, DBSCAN

Week-11.Dimensionality Reduction

PCA, t-SNE, Feature Selection Methods

Week-12. Artificial Neural Networks (ANNs)

Perceptron, Activation Functions, Backpropagation

Week-13. Convolutional Neural Networks (CNNs)

Image Classification, Transfer Learning

Week-14.Recurrent Neural Networks (RNNs)

LSTMs, GRUs, Time-Series Forecasting

Week-15.Transformer Models

BERT, GPT, Attention Mechanism

Week-16. Model Explainability (XAI)

SHAP, LIME, Interpretability Techniques

Week-17. MLOps & Model Deployment

Docker, FastAPI, Streamlit, Cloud Deployment

Week-18.Capstone Project – Part 1

Problem Statement, Data Collection, Cleaning

Week-19. Capstone Project – Part 2

Model Training, Optimization, Deployment