Machine Learning Tutorials and Articles

In this repository, I'm uploading code, notebooks and articles from my personal blog : https://maelfabien.github.io/. Don't hesitate to ⭐ the repo if you enjoy my work ! New articles are being published weekly !

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NEW: I'm looking for motivated Data Scientists to help me build high environmental impact algorithms (CV essentially). Please contact me if you're interested (from my website, contact section)

Table of Content :

• CheatSheets
• Latest Articles
• Machine Learning
• Deep Learning
• Data Engineering
• Written for other blogs
• Medium Articles

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/

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
• the is a link to the code folder for each article

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 ! (mael.fabien@gmail.com)

Machine Learning Cheatsheet :

For the moment, these cheat sheets are written manually. I'd like to create a visual content later that would both dive in the maths and illustrate clearly each algorithm.

1. Supervised Learning

2. Unsupervised Learning

Projects

I have made a series of projects, all of which are available on my blog : https://maelfabien.github.io/portfolio/#

Latest articles

SP - Voice Gender Detection web application: How to extract relevant features and build a voice gender detection application using MFCC, GMMs and a provided dataset.

SP - Sound Visualization (3/3): Dive into spectrograms, chromagrams, tempograms, spectral power density and more...

SP - Sound Feature Extraction (2/3): An overview with a Python implementation of the different sound features to extract.

SP - Introduction to Voice Processing in Python (1/3): Summary of the book "Voice Computing with Python" with concepts, code and examples.

SP - Building a Voice Activity Detection web application : Voice detection can be used to start a voice assistant or in emergency cases for example. Here's how to implement it using simple methods.

CV - Implementing YoloV3 for Object Detection : Learn how to implement YoloV3 and detect objects on your images and videos.

NLP - Easy Question Answering with AllenNLP : Understand the core concepts and create a simple example of Question Answering.

NLP - Data Augmentation in NLP : Details of the implementation of “Easy Data Augmentation” paper.

NLP - Character-level LSTMs to predict gender of first names : 90% accuracy on predictiong the gender of French and US first names.

NLP - Few Shot Text Classification : Implementation of a simple paper that leverages pre-trained models for few shot text classification.

NLP - Improved Few Shot Text Classification : Improving previous results with Data Augmentation and more complex models.

RL - Introduction to Reinforcement Learning : An introduction to the basic building blocks of reinforcement learning.

RL - Markov Decision Process : Overview of Markov Decision Process and Bellman Equation.

RL - Planning by Dynamic Programming : Introduction to Dynamic Programming, including Policy and Value Iteration.

NLP - I trained a Neural Network to speak like me : Having written over 100 articles, I trained a NN to write articles just like me.

DL - How do Neural Networks learn? : Dive into feedforward process and back-propagation.

See More

Article Title	Read Time	Article	Code Folder
The linear regression model (1/2)	14mn	here	here
The linear regression model (3/2)	10mn	here	here
Basics of Statistical Hypothesis Testing	5mn	here	---
The Logistic Regression	4mn	here	here
Statistics in Matlab	4mn	here	---

Article Title	Read Time	Article	Code Folder
The Basics of Machine Learning	4mn	here	---
Bayes Classifier	1mn	here	---
Linear Discriminant Analysis	3mn	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
Anomaly Detection	3mn	here	---

Article Title	Read Time	Article	Code Folder
Introduction to Time Series	4mn	here	here
Key concepts of Time Series	4mn	here	here

Article Title	Read Time	Article	Code Folder
Markov Chains	9mn	here	here
Hidden Markov Models	6mn	here	---
Build a language recognition app from scratch	10mn	here	here

Article Title	Read Time	Article	Code Folder
Introduction to Graph Mining	5mn	here	here
Graph Analysis	4mn	here	here
Graph Algorithms	11mn	here	here
Graph Learning	8mn	here	here
Graph Embedding	4mn	here	here

Article Title	Read Time	Article	Code Folder
GridSearch vs. Randomized Search	2mn	here	---
AutoML with h2o	6mn	here	---
Bayesian Hyperparameter Optimization	7mn	here	here
Machine Learning Explainability	12mn	here	---

Article Title	Read Time	Article	Code Folder
Introduction to Data Viz	12mn	here	---
Visual Recommendation System	4mn	here	---
Interactive graphs in Python with Altair	5mn	here	here
Dynamic plots with BQ-Plot	---	---	here
An interactive tool with Altair	---	here	---
An interactive tool with D3.js	---	here	---

Article Title	Read Time	Article	Code Folder
Introduction to Online Learning	5mn	here	---
Linear Classification	1mn	here	---

Article Title	Read Time	Article	Code Folder
The Rosenbaltt's Perceptron	8mn	here	here
Multilayer Perceptron (MLP)	5mn	here	here
Prevent Overfitting of Neural Netorks	6mn	here	---
Full introduction to Neural Nets	6mn	here	---
Convolutional Neural Network	6mn	here	---
How do Neural Networks learn?	3mn	here	---
Activation functions in DL	3mn	here	here

Article Title	Read Time	Article	Code Folder
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

Article Title	Read Time	Article	Code Folder
Build an Emotion Recognition WebApp from scratch	8mn	here	here
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	---
Implementing YoloV3 for Object Detection	3mn	here	---

Article Title	Read Time	Article	Code Folder
Introduction to NLP	1mn	here	---
Text Pre-Processing	8mn	here	---
Text Embedding with BoW and Tf-Idf	5mn	here	---
Text Embedding with Word2Vec	6mn	here	---
I trained a Neural Network to speak like me	8mn	here	here
I trained a Neural Network to speak like me	8mn	here	here
Few Shot Text Classification	10mn	here	here
Improved Few Shot Text Classification	9mn	here	here
Predicting Gender of First Names	7mn	here	here
Data Augmentation in NLP	3mn	here	---
Easy Question Answering with AllenNLP	4mn	here	---

Article Title	Read Time	Article	Code Folder
Introduction to Reinforcement Learning	6mn	here	---
Markov Decision Process	7mn	here	---
Planning by Dynamic Programming	4mn	here	---

Two general articles :

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

2. Useful Bash commands (1mn read) https://maelfabien.github.io/bigdata/Terminal/

3. Making your code production ready (1mn read) https://maelfabien.github.io/bigdata/Code/

Article Title	Read Time	Article
Introduction to Hadoop	4mn	here
MapReduce	3mn	here
HDFS	2mn	here
VMs in Virtual Box	1mn	here
Hadoop with the HortonWorks Sandbox	2mn	here
Load and move files to HDFS	2mn	here
Launch a MapReduce Job	2mn	here
MapReduce Jobs in Python	3mn	here
MapReduce Job in Python locally	1mn	here

Article Title	Read Time	Article
Introduction to Spark	6mn	here
Install Spark-Scala and PySpark	1mn	here
Discover Spark-Scala	2mn	here

Article Title	Read Time	Article
A No-SQL project from scratch	8mn	here
Big (Open) Data, the GDelt project	2mn	here
Install Zeppelin locally	1mn	here
Run Zeppelin on AWS EMR	4mn	here
Work with S3 buckets	1mn	here
Launch and access AWS EC2 instances	2mn	here
Install Apache Cassandra on EC2 Cluster	2mn	here
Install Zookeeper on EC2 instances	3mn	here
Build an ETL in Scala	3mn	here
Move Scala Dataframes to Cassandra	2mn	here
Move Scala Dataframes to Cassandra	2mn	here

Article Title	Read Time	Article
AWS Cloud Concepts	2mn	here
AWS Core Services	1mn	here

Article Title	Read Time	Article
TPU Survival Guide on Colab	8mn	here
Store files on Google Cloud and Colab	1mn	here
TPU Survival Guide on Colab	8mn	here
Introduction to GCP (Week 1 Module 1)	6mn	here
Lab - Instance VM + Cloud Storage	3mn	here
Lab - BigQuery Public Datasets	1mn	here
Introduction to Recommendation Systems (Week 1 Module 2)	4mn	here
Run Spark jobs on Cloud DataProc (Week 1 Module 2)	2mn	here
Lab - Recommend products using Cloud SQL and SparkML	6mn	here
Run ML models in SQL with BigQuery ML (Week 1 Module 3)	6mn	here

Article Title	Read Time	Article
Introduction to ElasticStack	1mn	here
Getting Started with ElasticSearch and Kibana	7mn	here
Install and run Kibana locally	1mn	here
Working with DevTools in ElasticSearch	9mn	here
Working with DevTools in ElasticSearch	9mn	here

Article Title	Read Time	Article
Introduction to Graph Databases	1mn	here
A day at Neo4J GraphTour	7mn	here

Written for other blogs

1. Who's the painter? - For explorium.ai : An illustration of how data enrichment and feature engineering can improve a model.

2. Machine Learning Interpretability and Explainability (1/2) - For explorium.ai : An introduction to interpretable models with code and examples.

3. Machine Learning Interpretability and Explainability (2/2) - For explorium.ai : An introduction to explainability in Machine Learning with code and examples.

4. A guide to Face Detection - For digitalminds.io : An overview of the different techniques face Face Detection in Python (with code).

5. Modéliser des distributions avec Python (French) - For Stat4Decision: Distribution fitting web application with Streamlit.

6. Introduction au Traitement Automatique de Language Naturel (TAL) (French) - For Stat4Decision

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

4. Introduction to Graphs (Part 1): https://towardsdatascience.com/introduction-to-graphs-part-1-2de6cda8c5a5

5. Graph Algorithms (Part 2): https://towardsdatascience.com/graph-algorithms-part-2-dce0b2734a1d

6. Graph Algorithms (Part 3): https://towardsdatascience.com/learning-in-graphs-with-python-part-3-8d5513eef62d

7. I trained a neural network to speak like me: https://towardsdatascience.com/i-trained-a-network-to-speak-like-me-9552c16e2396

Stay tuned :)