Machine Learning Tutorials and Articles

In this repository, I'll upload machine learning tutorials (with code) for most common algorithms. These tutorials might include an explanation of the algorithm, an implementation of the algorithm (from scratch or using a library), as well as an example on a publically available data set.

First of all, if you're not familiar with the key concepts of machine learrning, make sure to check this first article : https://maelfabien.github.io/machinelearning/ml_base/

All the articles and codes included in this repository were originally posted on my personal blog : https://maelfabien.github.io/

The repository is organized the following way :

• articles and tutorials are posted by category
• there is a link to the article in question with the read time specified
• artiles in Bold have a corresponding folder in this repo

Machine Learning Cheatsheet :

1. Supervised Learning

2. Unsupervised Learning

1. Computer Vision

Article Title	Read Time	Article	Code Folder
A full guide to Face, Mouth and Eyes Real Time detection	16mn	here	here
How to use OpenPose on MacOS ?	3mn	here	---
Introduction to Computer Vision	1mn	here	---
Image Filtering and Image Gradients	5mn	here	here
Advanced Filtering and Image Transformation	5mn	here	---
Image Features, Panorama, Matching	5mn	here	---

2. Natural Language Processing

Article Title	Read Time	Article	Code Folder
Text Pre-Processing	7mn	here	---
Text Embedding with BoW and Tf-Idf	6mn	here	---
Text Embedding with Word2Vec	3mn	here	---

3. Statistics

Article Title	Read Time	Article	Code Folder
The linear regression model	10mn	here	here
Multidimensional Linear Regression	3mn	here	---
Normal Regression Model	1mn	here	---
Pseudo-Least Squares	1mn	here	---
Transformations of linear models	1mn	here	---
Dealing with boolean and categorical variables	1mn	here	---
Basics of Statistical Hypothesis Testing	5mn	here	---
Generalized Least Squares	2mn	here	---
Statistics in Matlab	4mn	here	---
Introduction to Time Series	4mn	here	here
Key concepts of Time Series	4mn	here	here

4. Machine Learning

Article Title	Read Time	Article	Code Folder
The Basics of Machine Learning	4mn	here	---
Bayes Classifier	1mn	here	---
Linear Discriminant Analysis	1mn	here	---
Adaboost and Boosting	7mn	here	here
Large Scale Kernel Methods for SVM	9mn	here	here
Markov Processes	9mn	here	---
Hidden Markov Models	6mn	here	---
Introduction to Graph Mining	5mn	here	here
Graph Analysis	4mn	here	here
Graph Algorithms	11mn	here	here
Graph Learning	8mn	here	here

5. Deep Learning

Article Title	Read Time	Article	Code Folder
The Rosenbaltt's Perceptron	3mn	here	here
Multilayer Perceptron (MLP)	5mn	here	here
Regularization Techniques	1mn	here	---
Convolutional Neural Network	2mn	here	---
Inception Architecture in Keras	2mn	here	here
Build an autoencoder using Keras functional API	5mn	here	---
XCeption Architecture	3mn	here	here
GANs on the MNIST dataset	---	---	here

6. Medium Articles

1. AdaBoost : https://towardsdatascience.com/boosting-and-adaboost-clearly-explained-856e21152d3e

2. Face Detection : https://towardsdatascience.com/a-guide-to-face-detection-in-python-3eab0f6b9fc1

7. Data Engineering

1. Understanding Computer Components (6mn read) https://maelfabien.github.io/bigdata/comp_components/

2. AWS Cloud Concepts (2mn read) https://maelfabien.github.io/bigdata/cloud_concept/

3. AWS Core Services (1mn read) https://maelfabien.github.io/bigdata/core_services/

4. TPU Survival Guide on Colab (8mn read) https://maelfabien.github.io/bigdata/ColabTPU/

5. Store files on Google Cloud and Colab (1mn read) https://maelfabien.github.io/bigdata/ColabDrive/

6. Introduction to ElasticStack (1mn read) https://maelfabien.github.io/bigdata/ElasticStack/

7. Getting Started with ElasticSearch and Kibana (7mn read) https://maelfabien.github.io/bigdata/ElasticCloud/

8. Install and run Kibana locally (1mn read) https://maelfabien.github.io/bigdata/Elasticsearch/

9. Working with DevTools in ElasticSearch (9mn read) https://maelfabien.github.io/bigdata/DevTools/

10. Introduction to Graph Databases (1mn read) https://maelfabien.github.io/bigdata/Neo4J/

11. A day at Neo4J GraphTour (6mn read) https://maelfabien.github.io/bigdata/Neo4J_gt/

12. Install Zeppelin locally (1mn read) https://maelfabien.github.io/bigdata/zeppelin_local/

13. Run Zeppelin on AWS EMR (4mn read) https://maelfabien.github.io/bigdata/zeppelin_emr/

14. Work with S3 buckets (1mn read) https://maelfabien.github.io/bigdata/storage/

15. Launch and access AWS EC2 instances (2mn read) https://maelfabien.github.io/bigdata/EC2/

16. Install Apache Cassandra on EC2 Cluster (2mn read) https://maelfabien.github.io/bigdata/EC2_Cassandra/

17. Install Zookeeper on EC2 instances (3mn read) https://maelfabien.github.io/bigdata/ZK/

18. Big (Open) Data, the GDelt project (2mn read) https://maelfabien.github.io/bigdata/zeppelin-GDELT/

19. Build an ETL in Scala (3mn read) https://maelfabien.github.io/bigdata/Scala/

20. Move Scala Dataframes to Cassandra (2mn) https://maelfabien.github.io/bigdata/Scala_Cassandra/

Stay tuned, new articles coming weekly :)