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
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⭐️ 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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