Live Course Module: Machine Learning Algorithms Course for Data Science

Total Duration: 48 Hours (8 Weeks)

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Week 1: Introduction & Fundamentals (6 hrs)

1. Introduction to Machine Learning (1 hr)

   • What is ML & its role in Data Science

   • Types of ML: Supervised, Unsupervised, Reinforcement

   • Real-world applications

2. Mathematical & Statistical Foundations (2 hrs)

   • Probability, Linear Algebra basics (vectors, matrices)

   • Descriptive vs Inferential statistics

   • Correlation, covariance, hypothesis testing

3. Data Preprocessing & Feature Engineering (3 hrs)

   • Data cleaning, handling missing values

   • Feature scaling: normalization, standardization

   • Feature selection & dimensionality reduction (PCA, LDA)

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Week 2: Supervised Learning – Regression (6 hrs)

4. Linear Regression (2 hrs)

   • Concept of regression, cost function

   • Gradient descent optimization

   • Multiple Linear Regression

5. Regularization Techniques (1 hr)

   • Ridge, Lasso, ElasticNet

6. Logistic Regression (3 hrs)

   • Classification basics, sigmoid function

   • Binary & Multiclass classification

   • Evaluation metrics (Accuracy, Precision, Recall, F1-score, ROC, AUC)

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Week 3: Supervised Learning – Tree-based Models (6 hrs)

7. Decision Trees (2 hrs)

   • Entropy, Information Gain, Gini Index

   • Overfitting & pruning

8. Random Forest (2 hrs)

   • Ensemble methods, bagging

   • Feature importance

9. Gradient Boosting & XGBoost (2 hrs)

   • Boosting concept

   • XGBoost, LightGBM, CatBoost overview

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Week 4: Supervised Learning – Advanced Models (6 hrs)

10. Support Vector Machines (2 hrs)

    • Linear vs Non-linear SVM

    • Kernels (RBF, polynomial)

    • Hyperparameter tuning

11. k-Nearest Neighbors (2 hrs)

    • Distance metrics (Euclidean, Manhattan)

    • Curse of dimensionality

12. Naïve Bayes Classifier (2 hrs)

    • Bayes’ theorem

    • Gaussian, Multinomial, Bernoulli NB

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Week 5: Unsupervised Learning (6 hrs)

13. Clustering Algorithms (3 hrs)

    • k-Means clustering & Elbow method

    • Hierarchical clustering

    • DBSCAN

14. Dimensionality Reduction (3 hrs)

    • PCA deep dive

    • t-SNE, UMAP for visualization

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Week 6: Model Evaluation & Optimization (6 hrs)

15. Model Evaluation & Validation (3 hrs)

    • Cross-validation

    • Bias-Variance tradeoff

    • Confusion matrix, ROC-AUC analysis

16. Hyperparameter Tuning (2 hrs)

    • Grid Search, Random Search

    • Bayesian optimization

17. Model Deployment Basics (1 hr)

    • Saving models with pickle/joblib

    • Intro to deployment (Flask/Streamlit basics)

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Week 7: Advanced Topics (Optional) (6 hrs)

18. Ensemble Learning (2 hrs)

    • Bagging, Boosting, Stacking

19. Time Series Forecasting (2 hrs)

    • ARIMA, SARIMA

    • ML models for time series

20. Neural Network Basics (2 hrs)

    • Perceptron, Feed-forward networks

    • Intro to Deep Learning concepts

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✅ Course Outcomes

By the end of the course, learners will:

• Understand core ML algorithms and their applications.

• Be able to preprocess, train, evaluate, and tune models.

• Gain hands-on experience using Python (scikit-learn, pandas, matplotlib, seaborn, XGBoost).

• Be industry-ready for ML projects & interviews.