What is Machine Learning?

Applications of ML in Real-World Scenarios

Types of ML: Supervised, Unsupervised, Reinforcement Learning

ML vs AI vs Deep Learning

Overview of ML Workflow (Data Collection → Preprocessing → Modeling → Evaluation → Deployment)

Popular ML Libraries: Scikit-Learn, TensorFlow, PyTorch

Installing Python, Jupyter Notebook, and Anaconda

Overview of Libraries: NumPy, Pandas, Matplotlib, Seaborn

Hands-on: Loading and Visualizing a Dataset

Introduction to Google Colab for Cloud-Based ML

What is Regression?

Linear Regression: Concept & Implementation

Polynomial Regression: When to Use It?

Decision Tree & Random Forest Regression

Performance Metrics: RMSE, MAE, R² Score

Hands-on: Predicting House Prices using Regression

What is Classification?

Logistic Regression: Understanding the Sigmoid Function

Decision Trees & Random Forest: Pros & Cons

Support Vector Machines (SVM): Concept & Implementation

Performance Metrics: Accuracy, Precision, Recall, F1 Score, Confusion Matrix, ROC Curve

Hands-on: Spam Email Detection using Classification

Introduction to Clustering

K-Means Clustering: Algorithm & Practical Implementation

Hierarchical Clustering: Concept & Implementation

Anomaly Detection: Identifying Outliers in Data

Dimensionality Reduction: PCA (Principal Component Analysis)

Hands-on: Customer Segmentation Using Clustering

Train-Test Split & Cross-Validation

Bias-Variance Tradeoff

Overfitting vs Underfitting

Hyperparameter Tuning: ○ Grid Search ○ Random Search ○ Automated Hyperparameter Tuning

Saving & Loading ML Models for Future Use

Hands-on: Tuning a Classification Model for Better Performance

Choosing a Dataset for the Final Project (E.g., House Price Prediction, Customer Churn Analysis)

Data Cleaning, Feature Engineering, Model Selection

Building & Evaluating the ML Model

Introduction to Model Deployment (Flask, Streamlit, FastAPI)

Best Practices in Machine Learning Projects

Final Thoughts & Next Steps in ML Learning