Below are links for websites, videos, and books that I found useful giving an overview of different machine learning models and steps in the data preparation and model validation process.

I will update these links as I come across more great resources!

Best Sites for Machine Learning Mastery

I often find myself using the same sites over and over again for reference when exploring machine learning and deep learning. Links to my favorite resources are below:

• Towards Data Science
• Analytics Vidhya
• Machine Learning Mastery
• Python Scikit-learn Documentation

Data Preparation

Data Preprocessing

• Preprocessing with sklearn: a complete and comprehensive guide
• Feature Scaling for Machine Learning: Understanding the Difference Between Normalization vs. Standardization
• All about Categorical Variable Encoding
• Ordinal and One-Hot Encodings for Categorical Variables

Data Splitting for Training and Model Validation 

• train_test_split Vs StratifiedShuffleSplit
• sklearn.cross_validation.StratifiedShuffleSplit
• Cross-validation: Evaluating Estimator Performance

Imputing Missing Values

• Imputation of Missing Values
• Simple Techniques for Missing Data Imputation

Problem Types and Model Selection

• Difference Between Classification and Regression in Machine Learning
• Which Machine Learning Model to Use?
• Machine Learning Algorithms: Which One to Choose for Your Problem

Regularized Regression

• An Introduction to Ridge, Lasso, and Elastic Net Regression
• Regularization: Ridge, Lasso and Elastic Net

Essential Algorithms: Tree Methods

Ensemble Methods

• Ensemble Learning to Improve Machine Learning Results
• Ensemble Methods

Decision Trees

• What is a Decision Tree?
• A Step by Step CART Decision Tree Example
• sklearn.tree.DecisionTreeClassifier
• Classification And Regression Trees for Machine Learning
• InDepth: Parameter tuning for Decision Tree
• Impurity & Judging Splits – How Decision Trees Work

Random Forest

• An Implementation and Explanation of the Random Forest in Python
• Introduction to Random forest – Simplified
• A Beginner’s Guide to Random Forest Hyperparameter Tuning
• sklearn.ensemble.RandomForestClassifier

Gradient Boosted Trees

• A Gentle Introduction to the Gradient Boosting Algorithm for Machine Learning
• Gradient Boosting Decision Tree Algorithm Explained
• Boosting algorithm: GBM
• Gradient Boosting Classifiers in Python with Scikit-Learn
• sklearn.ensemble.GradientBoostingClassifier
• Complete Machine Learning Guide to Parameter Tuning in Gradient Boosting (GBM) in Python

XGBoost

• A Gentle Introduction to XGBoost for Applied Machine Learning
• An End-to-End Guide to Understand the Math behind XGBoost
• A Beginner’s guide to XGBoost
• XGBoost Algorithm: Long May She Reign!

Support Vector Machines (SVMs)

• Support Vector Machine — Simply Explained
• Chapter 2 : SVM (Support Vector Machine) — Theory
• Understanding Support Vector Machine (SVM) algorithm from examples (along with code)
• Support Vector Machines
• sklearn.svm.SVC

Multi-Class & Multi-Label Algorithms

• Multiclass and Multilabel Algorithms
• Multiclass Classification using scikit-learn
• How to Use One-vs-Rest and One-vs-One for Multi-Class Classification
• Multiclass Classification using Random Forest on Scikit-Learn Library
• Machine Learning — Multi-Class Classification with Imbalanced Dataset

Loss Functions

• Common Loss Functions in Machine Learning
• A Detailed Guide to 7 Loss Functions for Machine Learning Algorithms with Python Code
• Common loss functions that you should know!

Model Validation & Improvement

Model Evaluation & Improving Models

• Evaluating a Classification Model
• Validating Machine Learning Models with scikit-learn
• Regression: An Explanation of Regression Metrics And What Can Go Wrong
• Metrics and Scoring: Quantifying the Quality of Predictions
• How to Use ROC Curves and Precision-Recall Curves for Classification in Python
• Understanding AUC – ROC Curve
• sklearn.metrics.classification_report
• Feature Importances with Forests of Trees
• Important three techniques to improve machine learning model performance with imbalance datasets

Clustering Models

K-Means

• The Most Comprehensive Guide to K-Means Clustering You’ll Ever Need
• K-means Cluster Analysis
• Interpret All Statistics and Graphs for Cluster K-Means

Hierarchical Clustering

• What is Hierarchical Clustering?
• What is Hierarchical Clustering? on KDNuggets
• Understanding the Concept of the Hierarchical Clustering Technique
• A Beginner’s Guide to Hierarchical Clustering and how to Perform it in Python

DBSCAN

• DBSCAN Clustering in ML – Density-Based Clustering
• Density-Based Clustering

Validating Clustering Models

• Clustering Validation Statistics: 4 Vital Things Everyone Should Know – Unsupervised Machine Learning
• Determining The Optimal Number Of Clusters: 3 Must Know Methods
• Selecting the Number of Clusters with Silhouette Analysis on K-Means Clustering
• Cluster Validation Statistics: Must Know Methods
• Unsupervised Learning: Evaluating Clusters

Recommender Engines

General

• Comprehensive Guide to Build a Recommendation Engine from Scratch (in Python)
• Building a Movie Recommendation Engine in Python using Scikit-Learn
• Large Scale Jobs Recommendation Engine using Implicit Data in PySpark

Content-Based Method

• Beginners Guide to learn about Content Based Recommender Engines

Collaborative Filtering Method

• Build a Recommendation Engine with Collaborative Filtering

Other Recommendation Engine Methods

• Recommender System — Singular Value Decomposition (SVD) & Truncated SVD
• Wide & Deep Learning for Recommender Systems
• ALS: Stock Portfolio Recommendations

Deep Learning

• Deep Learning: Feedforward Neural Network
• Deep Learning: Back Propagation
• Gentle Introduction to the Adam Optimization Algorithm for Deep Learning
• Adam — Latest Trends in Deep Learning Optimization

Time Series

General Principles

• Almost Everything You Need to Know About Time Series
• A Complete Tutorial on Time Series Modeling in R
• Seasonality in Python: Additive or Multiplicative Model?
• Is my Time Series Additive or Multiplicative?

Moving Average

• Moving Average Smoothing for Data Preparation and Time Series Forecasting in Python
• Moving Average Models

ARIMA

• ARIMA Models
• ARIMA Model – Complete Guide to Time Series Forecasting in Python
• How to Create an ARIMA Model for Time Series Forecasting in Python
• Understanding Auto Regressive Moving Average Model — ARIMA

Holt-Winter Model

• Holt-Winters Forecasting Simplified

Causal Impact/Bayesian Time Series

• Inferring Causal Impact using Bayesian Structural Time Series Models
• Inferring Causality in Time Series Data

Multi-Step Time Series

• Improving Multi-step Prediction of Learned Time Series Models
• Strategies for Multi-Step Time Series Forecasting

Prophet Models

• Prophet: Forecasting at Scale
• Journal Article: Forecasting at Scale
• Time Series Prediction using Prophet in Python
• Generate Quick and Accurate Time Series Forecasts using Facebook’s Prophet
• Using Prophet for Anomaly Detection
• Forecasting Multiple Time Series using Prophet in Parallel

Machine Learning for Time Series

• Using Decision Trees, Random Forest and Gradient Boosting for Time Series Prediction

Free Machine Learning Courses

• AWS Learning Library, Search for Machine Learning
• Microsoft Learn Machine Learning Crash Course