Python setup (Anaconda, Jupyter Notebook)

Basics: Variables, loops, functions, data types

Libraries: NumPy, Pandas
Practical : Write Python scripts for data manipulation and basic operations

Data cleaning: missing values, duplicates

Feature scaling, normalization, and encoding categorical data

Train-test split
Practical : Prepare a dataset for ML modeling (e.g., Titanic or Iris dataset)

Visualization with Matplotlib & Seaborn

Plotting histograms, scatter plots, and box plots

Understanding correlations and patterns
Practical : Visualize a dataset to explore insights

Linear Regression, Polynomial Regression

Model evaluation: MSE, RMSE, R² score

Use cases of regression
Practical : Predict house prices or student scores using regression models

Logistic Regression, K-Nearest Neighbors (KNN)

Decision Trees, Random Forest

Evaluation metrics: Accuracy, Precision, Recall, F1-score
Practical : Build a classifier (e.g., iris dataset, spam detection)

Clustering: K-Means, Hierarchical Clustering

Dimensionality reduction: PCA

Real-world applications of clustering
Practical : Customer segmentation or grouping similar products

Overfitting vs Underfitting

Cross-validation techniques

Grid Search and Random Search for tuning models
Practical : Optimize models built in previous modules to improve accuracy

Basics of neural networks, perceptron, and activation functions

Forward propagation & backpropagation

Intro to deep learning libraries: TensorFlow / Keras
Practical : Build a simple neural network to classify MNIST digits

Project planning and best practices

Build a complete ML project (e.g., loan prediction, sales forecasting, or digit recognizer)

Deploy ML model using Streamlit or Flask
Final Project : Students showcase a working ML application online