Machine Learning for Everybody – Full Course

Course Contents

(0:00:00) Intro
(0:00:58) Data/Colab Intro
(0:08:45) Intro to Machine Learning
(0:12:26) Features
(0:17:23) Classification/Regression
(0:19:57) Training Model
(0:30:57) Preparing Data
(0:44:43) K-Nearest Neighbors
(0:52:42) KNN Implementation
(1:08:43) Naive Bayes
(1:17:30) Naive Bayes Implementation
(1:19:22) Logistic Regression
(1:27:56) Log Regression Implementation
(1:29:13) Support Vector Machine
(1:37:54) SVM Implementation
(1:39:44) Neural Networks
(1:47:57) Tensorflow
(1:49:50) Classification NN using Tensorflow
(2:10:12) Linear Regression
(2:34:54) Lin Regression Implementation
(2:57:44) Lin Regression using a Neuron
(3:00:15) Regression NN using Tensorflow
(3:13:13) K-Means Clustering
(3:23:46) Principal Component Analysis
(3:33:54) K-Means and PCA Implementations