Overview
Syllabus
⌨️ Intro
⌨️ Data/Colab Intro
⌨️ Intro to Machine Learning
⌨️ Features
⌨️ Classification/Regression
⌨️ Training Model
⌨️ Preparing Data
⌨️ K-Nearest Neighbors
⌨️ KNN Implementation
⌨️ Naive Bayes
⌨️ Naive Bayes Implementation
⌨️ Logistic Regression
⌨️ Log Regression Implementation
⌨️ Support Vector Machine
⌨️ SVM Implementation
⌨️ Neural Networks
⌨️ Tensorflow
⌨️ Classification NN using Tensorflow
⌨️ Linear Regression
⌨️ Lin Regression Implementation
⌨️ Lin Regression using a Neuron
⌨️ Regression NN using Tensorflow
⌨️ K-Means Clustering
⌨️ Principal Component Analysis
⌨️ K-Means and PCA Implementations
Taught by
freeCodeCamp.org