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

Projects Covered

● Analysis of Titanic Dataset

● House Price Prediction using Linear Regression

● Heart Disease Prediction using Logistic Regression

● Breast Cancer Prediction

● Customer Segmentation

● Hand Digit Image Classification