🏁 Topics Covered in this course:

• ⌨️ (0:00:00) Introduction
• ⌨️ (0:00:58) Data/Colab Introduction
• ⌨️ (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