This course aims to teach beginners the theory and practical application of machine learning concepts. By the end of the course, students will be able to understand supervised and unsupervised learning, linear regression, logistic regression, regularization, support vector machines, principal component analysis, decision trees, ensemble learning, boosting, stacking ensemble learning, unsupervised learning, K-Means, and hierarchical clustering. The course includes hands-on projects such as predicting house prices, stock prices, heart failure, and detecting spam/ham emails. The teaching method involves a combination of theoretical explanations and practical project work. This course is intended for beginners who are interested in learning the basics of machine learning.
Overview
Syllabus
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.
Taught by
freeCodeCamp.org
