# Machine_Learning_Tutorials
**Repository Path**: pansi18/Machine_Learning_Tutorials
## Basic Information
- **Project Name**: Machine_Learning_Tutorials
- **Description**: Code, exercises and tutorials of my personal blog ! 📝
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No
## Statistics
- **Stars**: 0
- **Forks**: 0
- **Created**: 2021-03-17
- **Last Updated**: 2021-03-17
## Categories & Tags
**Categories**: Uncategorized
**Tags**: None
## README
# Machine Learning Tutorials and Articles
In this repository, I'm uploading code, notebooks and notebooks 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 !
## Table of Content :
- [I. CheatSheets](https://github.com/maelfabien/Machine_Learning_Tutorials#machine-learning-cheatsheet)
- [II. Machine Learning](https://github.com/maelfabien/Machine_Learning_Tutorials#machine-learning)
- [III. Deep Learning](https://github.com/maelfabien/Machine_Learning_Tutorials#deep-learning)
- [IV. Data Engineering](https://github.com/maelfabien/Machine_Learning_Tutorials#data-engineering)
- [V. Medium Articles](https://github.com/maelfabien/Machine_Learning_Tutorials#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 :
1. Supervised Learning
2. Unsupervised Learning
# Machine Learning
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| The linear regression model (1/2) | 14mn | [here](https://maelfabien.github.io/statistics/linreg/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/2-Statistics/LinearRegression) |
| The linear regression model (3/2) | 10mn | [here](https://maelfabien.github.io/statistics/linreg2/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/2-Statistics/LinearRegression) |
| Basics of Statistical Hypothesis Testing | 5mn | [here](https://maelfabien.github.io/statistics/Tests/) | --- |
| The Logistic Regression | 4mn | [here](https://maelfabien.github.io/statistics/linreg3/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/2-Statistics/LogisticRegression) |
| Statistics in Matlab | 4mn | [here](https://maelfabien.github.io/statistics/matlab/) | --- |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| The Basics of Machine Learning | 4mn | [here](https://maelfabien.github.io/machinelearning/ml_base/) | --- |
| Bayes Classifier | 1mn | [here](https://maelfabien.github.io/machinelearning/bayes/) | --- |
| Linear Discriminant Analysis | 3mn | [here](https://maelfabien.github.io/machinelearning/LDA/) | --- |
| Adaboost and Boosting | 7mn | [here](https://maelfabien.github.io/machinelearning/adaboost/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/AdaBoost) |
| Gradient Boosting Regression | 6mn | [here](https://maelfabien.github.io/machinelearning/GradientBoost/#) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/GradientBoost) |
| Gradient Boosting Classification | 3mn | [here](https://maelfabien.github.io/machinelearning/GradientBoostC/#) | --- |
| Large Scale Kernel Methods for SVM | 9mn | [here](https://maelfabien.github.io/machinelearning/largescale/#svm-classifier) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/LargeScaleKernel) |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| A full guide to Face, Mouth and Eyes Real Time detection | 16mn | [here](https://maelfabien.github.io/tutorials/face-detection/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/1_Computer%20Vision/01-FaceDetection) |
| How to use OpenPose on MacOS ? | 3mn | [here](https://maelfabien.github.io/tutorials/open-pose/) | --- |
| Introduction to Computer Vision | 1mn | [here](https://maelfabien.github.io/computervision/cv_1/) | --- |
| Image Filtering and Image Gradients | 5mn | [here](https://maelfabien.github.io/computervision/cv_2/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/1_Computer%20Vision/04-ImageFiltering) |
| Advanced Filtering and Image Transformation | 5mn | [here](https://maelfabien.github.io/computervision/cv_3/#) | --- |
| Image Features, Panorama, Matching | 5mn | [here](https://maelfabien.github.io/computervision/cv_4/#) | --- |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| Introduction to NLP | 1mn | [here](https://maelfabien.github.io/machinelearning/NLP_0/#) | --- |
| Text Pre-Processing | 8mn | [here](https://maelfabien.github.io/machinelearning/NLP_1/) | --- |
| Text Embedding with BoW and Tf-Idf | 5mn | [here](https://maelfabien.github.io/machinelearning/NLP_2/) | --- |
| Text Embedding with Word2Vec | 6mn | [here](https://maelfabien.github.io/machinelearning/NLP_3/) | --- |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| Introduction to Time Series | 4mn | [here](https://maelfabien.github.io/statistics/TimeSeries1/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/2-Statistics/TimeSeries) |
| Key concepts of Time Series | 4mn | [here](https://maelfabien.github.io/statistics/TimeSeries2/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/2-Statistics/TimeSeries) |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| Markov Chains | 9mn | [here](https://maelfabien.github.io/machinelearning/HMM_1/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/HMM) |
| Hidden Markov Models | 6mn | [here](https://maelfabien.github.io/machinelearning/HMM_2/) | --- |
| Build a language recognition app from scratch | 10mn | [here](https://maelfabien.github.io/machinelearning/HMM_3/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/HMM) |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| Introduction to Graph Mining | 5mn | [here](https://maelfabien.github.io/machinelearning/graph_1/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/GraphMining) |
| Graph Analysis | 4mn | [here](https://maelfabien.github.io/machinelearning/graph_2/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/GraphMining) |
| Graph Algorithms | 11mn | [here](https://maelfabien.github.io/machinelearning/graph_3/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/GraphMining) |
| Graph Learning | 8mn | [here](https://maelfabien.github.io/machinelearning/graph_4/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/GraphMining) |
| Graph Embedding | 4mn | [here](https://maelfabien.github.io/machinelearning/graph_5/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/GraphMining) |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| GridSearch vs. Randomized Search | 2mn | [here](https://maelfabien.github.io/machinelearning/GridRand/) | --- |
| AutoML with h2o | 6mn | [here](https://maelfabien.github.io/machinelearning/AutoML/#) | --- |
| Bayesian Hyperparameter Optimization | 7mn | [here](https://maelfabien.github.io/machinelearning/HyperOpt/#) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/3-MachineLearning/BayesianHyperOpt) |
| Machine Learning Explainability | 12mn | [here](https://maelfabien.github.io/machinelearning/Explainability/#) | --- |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| Introduction to Data Viz | 12mn | [here](https://maelfabien.github.io/machinelearning/Dataviz/) | --- |
| Visual Recommendation System | 4mn | [here](https://maelfabien.github.io/machinelearning/VizReco/#) | --- |
| Interactive graphs in Python with Altair | 5mn | [here](https://maelfabien.github.io/machinelearning/Altair/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/6-DataViz/Altair) |
| Dynamic plots with BQ-Plot | --- | --- | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/6-DataViz/BQPlot) |
| An interactive tool with Altair | --- | [here](https://maelfabien.github.io/tsne) | --- |
| An interactive tool with D3.js | --- | [here](https://maelfabien.github.io/viz) | --- |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| Introduction to Online Learning | 5mn | [here](https://maelfabien.github.io/machinelearning/Online/) | --- |
| Linear Classification | 1mn | [here](https://maelfabien.github.io/machinelearning/Online2/) | --- |
# Deep Learning
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| The Rosenbaltt's Perceptron | 8mn | [here](https://maelfabien.github.io/deeplearning/Perceptron/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/4-DeepLearning/Perceptron) |
| Multilayer Perceptron (MLP) | 5mn | [here](https://maelfabien.github.io/deeplearning/mlp/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/4-DeepLearning/MultilayerPerceptron) |
| Prevent Overfitting of Neural Netorks | 6mn | [here](https://maelfabien.github.io/deeplearning/regu/) | --- |
| Convolutional Neural Network | 6mn | [here](https://maelfabien.github.io/deeplearning/cnn/#) | --- |
| Article Title | Read Time | Article | Code Folder |
| --- | --- | --- | --- |
| Inception Architecture in Keras | 2mn | [here](https://maelfabien.github.io/deeplearning/inception/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/4-DeepLearning/Inception) |
| Build an autoencoder using Keras functional API | 5mn | [here](https://maelfabien.github.io/deeplearning/autoencoder/) | --- |
| XCeption Architecture | 5mn | [here](https://maelfabien.github.io/deeplearning/xception/) | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/4-DeepLearning/Xception) |
| GANs on the MNIST dataset | --- | --- | [here](https://github.com/maelfabien/Machine_Learning_Tutorials/tree/master/4-DeepLearning/GANs) |
# Data Engineering
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/
| Article Title | Read Time | Article |
| --- | --- | --- |
| Introduction to Hadoop | 4mn | [here](https://maelfabien.github.io/bigdata/hadoop/) |
| MapReduce | 3mn | [here](https://maelfabien.github.io/bigdata/MapReduce/#) |
| HDFS | 2mn | [here](https://maelfabien.github.io/bigdata/HDFS/#) |
| VMs in Virtual Box | 1mn | [here](https://maelfabien.github.io/bigdata/VM/#) |
| Hadoop with the HortonWorks Sandbox | 2mn | [here](https://maelfabien.github.io/bigdata/HortonWorks/) |
| Load and move files to HDFS | 2mn | [here](https://maelfabien.github.io/bigdata/HDFS_2/) |
| Launch a MapReduce Job | 2mn | [here](https://maelfabien.github.io/bigdata/MRJob/) |
| MapReduce Jobs in Python | 3mn | [here](https://maelfabien.github.io/bigdata/MRJobP/) |
| MapReduce Job in Python locally | 1mn | [here](https://maelfabien.github.io/bigdata/MRH/) |
| Article Title | Read Time | Article |
| --- | --- | --- |
| Introduction to Spark | 6mn | [here](https://maelfabien.github.io/bigdata/spark1/) |
| Install Spark-Scala and PySpark | 1mn | [here](https://maelfabien.github.io/bigdata/spark2/) |
| Discover Spark-Scala | 2mn | [here](https://maelfabien.github.io/bigdata/spark3/#) |
| Article Title | Read Time | Article |
| --- | --- | --- |
| Big (Open) Data, the GDelt project | 2mn | [here](https://maelfabien.github.io/bigdata/zeppelin-GDELT/) |
| Install Zeppelin locally | 1mn | [here](https://maelfabien.github.io/bigdata/zeppelin_local/) |
| Run Zeppelin on AWS EMR | 4mn | [here](https://maelfabien.github.io/bigdata/zeppelin_emr/) |
| Work with S3 buckets | 1mn | [here](https://maelfabien.github.io/bigdata/storage/) |
| Launch and access AWS EC2 instances | 2mn | [here](https://maelfabien.github.io/bigdata/EC2/) |
| Install Apache Cassandra on EC2 Cluster | 2mn | [here](https://maelfabien.github.io/bigdata/EC2_Cassandra/) |
| Install Zookeeper on EC2 instances | 3mn | [here](https://maelfabien.github.io/bigdata/ZK/) |
| Build an ETL in Scala | 3mn | [here](https://maelfabien.github.io/bigdata/Scala/) |
| Move Scala Dataframes to Cassandra | 2mn | [here](https://maelfabien.github.io/bigdata/Scala_Cassandra/) |
| Move Scala Dataframes to Cassandra | 2mn | [here](https://maelfabien.github.io/bigdata/Scala_Cassandra/) |
| Article Title | Read Time | Article |
| --- | --- | --- |
| AWS Cloud Concepts | 2mn | [here](https://maelfabien.github.io/bigdata/cloud_concept/) |
| AWS Core Services | 1mn | [here](https://maelfabien.github.io/bigdata/core_services/) |
| Article Title | Read Time | Article |
| --- | --- | --- |
| TPU Survival Guide on Colab | 8mn | [here](https://maelfabien.github.io/bigdata/ColabTPU/) |
| Store files on Google Cloud and Colab | 1mn | [here](https://maelfabien.github.io/bigdata/ColabDrive/) |
| Article Title | Read Time | Article |
| --- | --- | --- |
| Introduction to ElasticStack | 1mn | [here](https://maelfabien.github.io/bigdata/ElasticStack/) |
| Getting Started with ElasticSearch and Kibana | 7mn | [here](https://maelfabien.github.io/bigdata/ElasticCloud/) |
| Install and run Kibana locally | 1mn | [here](https://maelfabien.github.io/bigdata/Elasticsearch/) |
| Working with DevTools in ElasticSearch | 9mn | [here](https://maelfabien.github.io/bigdata/DevTools/) |
| Working with DevTools in ElasticSearch | 9mn | [here](https://maelfabien.github.io/bigdata/DevTools/) |
| Article Title | Read Time | Article |
| --- | --- | --- |
| Introduction to Graph Databases | 1mn | [here](https://maelfabien.github.io/bigdata/Neo4J/) |
| A day at Neo4J GraphTour | 7mn | [here](https://maelfabien.github.io/bigdata/Neo4J_gt/) |
# Written for other blogs
1. [Who's the painter? - For explorium.ai](https://www.explorium.ai/blog/whos-the-painter/) : An illustration of how data enrichment and feature engineering can improve a model.
2. [Machine Learning Interpretability and Explainability (1/2) - For explorium.ai](https://www.explorium.ai/blog/interpretability-and-explainability-part-1/) : An introduction to interpretable models with code and examples.
3. [A guide to Face Detection - For digitalminds.io](https://www.digitalminds.io/blog/a_guide_to_face_detection_in_python) : An overview of the different techniques face Face Detection in Python (with code).
# 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
####
Stay tuned :)