Introduction

Tutorials and Code for Machine Learning taken from Udemy

Getting Started

Tutorials can be found here

To download ML code on your machine:
git clone https://github.com/SarCode/ML-Code-Tutorials-Udemy.git

To find a suitable algorithm for your dataset use this cheat-sheet

Name		Name	Last commit message	Last commit date
Latest commit History 14 Commits
Apriori_Python		Apriori_Python
Artificial_Neural_Networks		Artificial_Neural_Networks
Classification_Template		Classification_Template
Data_Preprocessing		Data_Preprocessing
Decision_Tree_Classification		Decision_Tree_Classification
Decision_Tree_Regression		Decision_Tree_Regression
Hierarchical_Clustering		Hierarchical_Clustering
K_Means		K_Means
K_Nearest_Neighbors		K_Nearest_Neighbors
Kernel_PCA		Kernel_PCA
Kernel_SVM		Kernel_SVM
LDA		LDA
Logistic_Regression		Logistic_Regression
Model_Selection		Model_Selection
Multiple_Linear_Regression		Multiple_Linear_Regression
Naive_Bayes		Naive_Bayes
Natural_Language_Processing		Natural_Language_Processing
PCA		PCA
Polynomial_Regression		Polynomial_Regression
Random_Forest_Classification		Random_Forest_Classification
Random_Forest_Regression		Random_Forest_Regression
Regression_Template		Regression_Template
SVM		SVM
SVR		SVR
Simple_Linear_Regression		Simple_Linear_Regression
Thompson_Sampling		Thompson_Sampling
UCB		UCB
XGBoost		XGBoost
README.md		README.md
ml_map.webp		ml_map.webp