Machine Learning

Machine Learning Course Outline

Module 1: Introduction to Machine Learning

• Definition and scope

• Types of Machine Learning: Supervised, Unsupervised, Reinforcement Learning

• Applications in real-world scenarios

• Overview of ML pipeline

Tools: Python, Jupyter Notebook

Module 2: Python for Machine Learning

• Numpy, Pandas, Matplotlib, Seaborn

• Scikit-learn basics

• Data cleaning and preprocessing

• Exploratory Data Analysis (EDA)

Module 3: Supervised Learning - Regression

• Linear Regression

• Polynomial Regression

• Ridge and Lasso Regression

• Model evaluation metrics: MSE, RMSE, R²

Lab: Predicting housing prices

Module 4: Supervised Learning - Classification

• Logistic Regression

• k-Nearest Neighbors (k-NN)

• Support Vector Machines (SVM)

• Decision Trees and Random Forests

• Evaluation: Confusion Matrix, Precision, Recall, F1 Score, ROC-AUC

Lab: Spam detection or diabetes prediction

Module 5: Unsupervised Learning

• Clustering: K-Means, Hierarchical Clustering, DBSCAN

• Dimensionality Reduction: PCA, t-SNE

• Anomaly Detection

Lab: Customer segmentation

Module 6: Model Validation & Selection

• Cross-validation

• Bias-Variance tradeoff

• Hyperparameter tuning (GridSearchCV, RandomizedSearchCV)

• Underfitting vs Overfitting

Module 7: Neural Networks & Deep Learning (Intro)

• Basics of neural networks

• Perceptron, Activation Functions

• Forward and Backpropagation

• Introduction to TensorFlow/Keras or PyTorch

Lab: Digit recognition with MNIST

Module 8: Ensemble Learning

• Bagging and Boosting

• Random Forests, AdaBoost, Gradient Boosting, XGBoost

• Stacking models

Lab: Titanic survival prediction

Module 9: Natural Language Processing (NLP)

• Text preprocessing: Tokenization, Lemmatization, Stopwords

• Bag of Words, TF-IDF

• Naive Bayes classifier

• Sentiment Analysis

Module 10: Time Series Forecasting

• Understanding time series data

• ARIMA models

• Facebook Prophet

• LSTM (optional, advanced)

Lab: Stock price prediction

Module 11: Reinforcement Learning (Optional Advanced Topic)

• Markov Decision Processes (MDP)

• Q-Learning

• Deep Q-Networks (DQN)

Module 12: Capstone Project

• Choose a real-world dataset

• Apply complete ML pipeline

• Present results (with visualization and performance metrics)