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Machine Learning

README.md

Introduction to Machine Learning for NPTEL Course

This folders contains my comprehensive notes and resources on Machine Learning (ML), covering fundamental algorithms with in-depth explanations and practical applications which are essential for the NPTEL Intoduction to Machine Learning course.

Objective

The goal of this section is to build a strong foundation in Supervised Machine Learning and Unsupervised Machine Learning.

It bridges theory, math intuition, and hands-on implementation to help understand how classical ML models learn from data.

Contact

If you want to get the complete notes in a single PDF file, do message me on LinkedIn - Ben Gregory John

Contents

  1. Linear Regression: Widely most used regression algorithm for predicting continuous values. Includes concepts like cost function, gradient descent and more.

  2. Shrinkage Methods: Techniques like Ridge and Lasso regression to prevent overfitting and improve model generalization.

  3. Dimensionality Reduction: Methods like Principal Component Analysis (PCA) to reduce feature space while retaining important information.

  4. Bias-Variance Tradeoff: Understanding the balance between bias and variance to optimize model performance.

  5. Cross-Validation: Techniques to evaluate model performance and ensure robustness.

  6. Linear Classification: The core of linear classification algorithms including logistic regression and linear discriminant analysis.

  7. Logistic Regression: A fundamental algorithm for binary classification tasks.

  8. Linear Discriminant Analysis (LDA): A classification method that finds a linear combination of features to separate classes.

  9. Support Vector Machines (SVM): Powerful classification algorithms that find the optimal hyperplane to separate classes.

  10. Bayesian Estimation: Techniques like Maximum Likelihood Estimation (MLE) and Maximum A Posteriori (MAP) for parameter estimation.

  11. Decision Trees: Algorithms for classification and regression tasks using tree-like models of decisions.

  12. Ensemble Methods: Techniques like Random Forests, AdaBoost, and Gradient Boosting to improve model performance by combining multiple models.

  13. Bayesian Networks and Naive Bayes Classifier: Probabilistic graphical models and classification algorithms based on Bayes' theorem.

  14. Clustering Methods: Unsupervised learning techniques like K-Means, Hierarchical Clustering and DBSCAN for grouping similar data points.

  15. BIRCH and CURE Clustering Algorithms: Advanced clustering algorithms for large datasets.

Notes

Topic Notebook Description
Linear Regression PDF Comprehensive notes on linear regression, regularization techniques, and practical implementations.
Ridge and Lasso Regression PDF Detailed exploration of Ridge and Lasso regression methods.
Principal Component Analysis (PCA) PDF In-depth notes on PCA for dimensionality reduction.
Logistic Regression and LDA PDF Comprehensive notes on logistic regression and linear discriminant analysis.
Support Vector Machines (SVM) PDF Detailed notes on SVM algorithms for classification tasks.
Bayesian Estimation PDF Detailed notes on Bayesian estimation techniques in machine learning. Includes Maximum Likelihood and Maximum A Posteriori estimation methods.
Decision Trees for Classifiction and Regression PDF Detailed notes on decision tree algorithms for classification and regression tasks.
Classification Evaluation Metrics PDF Comprehensive notes on various evaluation metrics used in classification tasks, including accuracy, precision, recall, F1-score, and ROC-AUC.
Ensemble Methods PDF Comprehensive notes on ensemble learning techniques such as Random Forests, AdaBoost, and Gradient Boosting.
Bayesian Networks and Naive Bayes Classifier PDF Detailed notes on Bayesian networks and the Naive Bayes classification algorithm.
Clustering Methods PDF Comprehensive notes on clustering algorithms including K-Means, Hierarchical Clustering, and DBSCAN.
BIRCH and CURE Clustering Algorithms PDF Detailed notes on BIRCH and CURE clustering algorithms for large datasets.
Gaussian Mixture Models (GMM) PDF Comprehensive notes on Gaussian Mixture Models for clustering and density estimation.