Machine Learning Course for Beginners

Dive into a comprehensive 10-hour course on machine learning designed for beginners. Explore the theory and practical applications of key concepts, starting with fundamentals and progressing through supervised and unsupervised learning techniques. Master linear and logistic regression, support vector machines, principal component analysis, decision trees, and ensemble learning methods. Apply your knowledge to real-world projects, including house price prediction, stock price forecasting, heart failure prediction, and spam detection. Access accompanying learning resources and source code on GitHub, and benefit from in-depth explanations of complex topics like regularization, boosting, and clustering algorithms. Developed by Ayush Singh, this course provides a solid foundation for aspiring machine learning practitioners.

Course Introduction.
Fundamentals of Machine Learning.
Supervised Learning and Unsupervised Learning In Depth.
Linear Regression.
Logistic Regression.
Project: House Price Predictor.
Regularization.
Support Vector Machines.
Project: Stock Price Predictor.
Principal Component Analysis.
Learning Theory.
Decision Trees.
Ensemble Learning.
Boosting, pt 1.
Boosting, pt 2.
Stacking Ensemble Learning.
Unsupervised Learning, pt 1.
Unsupervised Learning, pt 2.
K-Means.
Hierarchical Clustering.
Project: Heart Failure Prediction.
Project: Spam/Ham Detector.