Machine_Learning_Tutorials

by maelfabien

Code, exercises and tutorials of my personal blog ! 📝

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Machine Learning Tutorials and Articles

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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 !

🚀 I recently started a newsletter in which I gather some cool articles I wrote on a topic, interesting Github repositories, projects, papers and more! I’ll try to send 1 to 2 emails per month. If you want to stay in the loop, just click here : http://eepurl.com/gyYzi5

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 :


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 ! ([email protected])

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

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  1. Unsupervised Learning

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Projects

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

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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



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| 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 | --- |


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| 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 | --- |


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


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| 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 |


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| 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 |


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| 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 | --- |

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| 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 | --- |


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


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| 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 |


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| 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 |


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| 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 | --- |


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| 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 | --- |


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


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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/


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| 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 |


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


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| 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 |


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


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| 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 |


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| 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 |


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| 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 :)

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