This is the 2nd part of machine learning full course. Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data.
It is seen as a part of artificial intelligence. #MachineLearning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.
The following topics about machine learning have been discussed in this course.

⭐️ Table of Contents ⭐️
⌨️ (0:00:00) Classification with bias function
⌨️ (0:10:24) Probabilistic discriminative models
⌨️ (0:39:12) Logistic regression
⌨️ (1:30:11) Neural Networks
⌨️ (3:01:39) Neural Networks backpropagation
⌨️ (3:31:11) Unsupervised learning
⌨️ (3:43:57) K-means clustering
⌨️ (4:26:00) Gaussian mixture models
⌨️ (4:51:33) Principal component analysis
⌨️ (6:31:43) Kernelizing linear models
⌨️ (7:08:05) Support vector machine
⌨️ (8:19:54) Gaussian distribution
⌨️ (9:36:50) Bootstrapping and feature bagging
⌨️ (10:13:08)Decision trees


⭐️ Credit ⭐️
Course Author by: Erik Bekkers
Website:    / @erikbekkers6398  
License: This work is licensed under a Creative Commons Attribution 4.0 International License

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