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

freeCodeCamp.org

Learn the theory and practical application of machine learning concepts in this comprehensive course for beginners.

🔗 Learning resources: https://github.com/ayush714/ML001-Project-Sources-Code-and-Learning-Materials
💻 Code: https://github.com/ayush714/ML001-Project-Sources-Code-and-Learning-Materials

✏️ Course developed by Ayush Singh. Check out his channel: https://www.youtube.com/c/neweraa

⭐️ Course Contents ⭐️
⌨️ (0:00:00) Course Introduction
⌨️ (0:04:34) Fundamentals of Machine Learning
⌨️ (0:25:22) Supervised Learning and Unsupervised Learning In Depth
⌨️ (0:35:39) Linear Regression
⌨️ (1:07:06) Logistic Regression
⌨️ (1:24:12) Project: House Price Predictor
⌨️ (1:45:16) Regularization
⌨️ (2:01:12) Support Vector Machines
⌨️ (2:29:55) Project: Stock Price Predictor
⌨️ (3:05:55) Principal Component Analysis
⌨️ (3:29:14) Learning Theory
⌨️ (3:47:38) Decision Trees
⌨️ (4:58:19) Ensemble Learning
⌨️ (5:53:28) Boosting, pt 1
⌨️ (6:11:16) Boosting, pt 2
⌨️ (6:44:10) Stacking Ensemble Learning
⌨️ (7:09:52) Unsupervised Learning, pt 1
⌨️ (7:26:58) Unsupervised Learning, pt 2
⌨️ (7:55:16) K-Means
⌨️ (8:20:21) Hierarchical Clustering
⌨️ (8:50:28) Project: Heart Failure Prediction
⌨️ (9:33:29) Project: Spam/Ham Detector

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Machine Learning Course for Beginners

Machine Learning Course for Beginners

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