Machine Learning Course Syllabus

Module 1: Introduction to Machine Learning

• What is Machine Learning (ML)

• Applications of ML in real-world scenarios

• Types of ML:

  • Supervised

  • Unsupervised

  • Reinforcement Learning

• Overview of the ML workflow

Module 2: Python & ML Tools Setup

• Python essentials for ML

• Jupyter Notebook, Google Colab

• Key Libraries:

  • NumPy

  • Pandas

  • Matplotlib

  • Seaborn

  • Scikit-Learn

Module 3: Data Preprocessing

• Handling missing values

• Encoding categorical variables:

  • Label Encoding

  • One-Hot Encoding

• Dealing with imbalanced datasets

• Data cleaning, normalization & standardization

Module 4: Exploratory Data Analysis (EDA) & Visualization

• Data visualization using Matplotlib & Seaborn

• Understanding distributions, correlations, and trends

• Identifying anomalies and outliers

Module 5: Feature Engineering

• Feature scaling:

  • Normalization vs Standardization

• Handling outliers

• Feature transformation & creation

• Feature encoding for models

Module 6: Feature Selection & Dimensionality Reduction

• Feature selection methods:

  • Filter methods

  • Wrapper methods

  • Embedded methods

• Dimensionality reduction techniques:

  • Principal Component Analysis (PCA)

  • t-Distributed Stochastic Neighbor Embedding (t-SNE)

  • Linear Discriminant Analysis (LDA)

Module 7: Supervised Learning – Regression

• Linear Regression:

  • Simple, Multiple, Polynomial

• Regularized Regression:

  • Lasso (L1), Ridge (L2), ElasticNet

• Model evaluation metrics:

  • Mean Squared Error (MSE)

  • Root Mean Squared Error (RMSE)

  • Mean Absolute Error (MAE)

  • R² Score

Module 8: Supervised Learning – Classification

• Logistic Regression

• Decision Trees

• Random Forest

• Support Vector Machines (SVM)

• Ensemble Methods:

  • Bagging

  • Boosting (AdaBoost, XGBoost)

• Evaluation metrics:

  • Accuracy

  • Precision

  • Recall

  • F1-Score

  • ROC-AUC

Module 9: Unsupervised Learning

• Clustering:

  • K-Means

  • Hierarchical Clustering

  • DBSCAN

  • Evaluation Metrics: Silhouette Score, Davies-Bouldin Index

• Association Rule Mining:

  • Apriori Algorithm

  • FP-Growth

  • Metrics: Support, Confidence, Lift

• Practical applications

Module 10: Anomaly Detection

• Isolation Forest

• One-Class SVM

• Autoencoders for anomaly detection

Module 11: Time Series Analysis & Forecasting

• Introduction to time series data

• Key concepts: Stationarity, trend, seasonality

• Forecasting models:

  • ARIMA

  • SARIMA

  • Prophet (optional)

Module 12: Model Optimization & Hyperparameter Tuning

• Cross-validation techniques

• Hyperparameter tuning:

  • GridSearchCV

  • RandomizedSearchCV

  • Bayesian Optimization

Module 13: Deep Learning Basics

• Introduction to Neural Networks

• Activation functions & optimizers

• Overview of:

  • Feedforward Neural Networks

  • Convolutional Neural Networks (CNN)

  • Recurrent Neural Networks (RNN)

• Frameworks:

  • TensorFlow

  • Keras

  • PyTorch

Module 14: Model Deployment & MLOps

• Saving and loading models

• Building APIs using:

  • Flask

  • FastAPI

  • Django

• Introduction to MLOps:

  • ML Pipelines

  • MLflow

  • CI/CD for ML Projects

Module 15: Capstone / Final Project

End-to-End ML Project covering:

• Data collection

• Data preprocessing

• Model building & evaluation

• Model deployment

Example Capstone Projects:

• Credit Card Fraud Detection

• Crop Yield Prediction