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machine_learning_tutorials's Introduction

Machine Learning Tutorials and Articles

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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

You would like to work on an article with me ? Or you would like me to work on a specific topic ? Feel free to reach out ! ([email protected])

Machine Learning Cheatsheet :

  1. Supervised Learning

Illustration

  1. Unsupervised Learning

Illustration

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 ---
Logistic Regression 3mn here ---
Linear Discriminant Analysis 1mn here ---
Adaboost and Boosting 7mn here here
Gradient Boosting Regression 6mn here here
Gradient Boosting Classification 3mn here ---
Large Scale Kernel Methods for SVM 9mn here here
Markov Chains 9mn here 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
AutoML with h2o 6mn here ---
Bayesian Hyperparameter Optimization 7mn 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 5mn here here
GANs on the MNIST dataset --- --- here

6. Data Visualization

Article Title Read Time Article Code Folder
Introduction to Data Viz 5mn here ---
Interactive graphs in Python with Altair 5mn here here
Dynamic plots with BQ-Plot --- --- here

7. Medium Articles

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

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

  3. Markov Chains and HMMs : https://towardsdatascience.com/markov-chains-and-hmms-ceaf2c854788

8. 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/

  21. Introduction to Hadoop (4mn) https://maelfabien.github.io/bigdata/hadoop/#

  22. MapReduce (3mn) https://maelfabien.github.io/bigdata/MapReduce/#

  23. HDFS (2mn) https://maelfabien.github.io/bigdata/HDFS/#

  24. VMs in Virtual Box (1mn) https://maelfabien.github.io/bigdata/VM/#

  25. Hadoop with the HortonWorks Sandbox (1/4) (2mn) https://maelfabien.github.io/bigdata/HortonWorks/

  26. Load and move files to HDFS (2/4) (2mn) https://maelfabien.github.io/bigdata/HDFS_2/

  27. Launch a MapReduce Job (3/4) (2mn) https://maelfabien.github.io/bigdata/MRJob/

  28. MapReduce Jobs in Python (4/4) (3mn) https://maelfabien.github.io/bigdata/MRJobP/

Stay tuned, new articles coming weekly :)

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