# 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 GitHub stars GitHub forks GitHub watchers GitHub followers GitHub commit activity GitHub contributors PyPI - Python Version Illustration 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 Illustration 2. Unsupervised Learning Illustration
# Machine Learning Illustration | 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/) | --- |
Illustration | 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) |
Illustration | 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/#) | --- |
Illustration | 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/) | --- |
Illustration | 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) |
Illustration | 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) |
Illustration | 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) |
Illustration | 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/#) | --- |
Illustration | 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) | --- |
Illustration | 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 Illustration | 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/#) | --- |
Illustration | 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/
Illustration | 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/) |
Illustration | 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/#) |
Illustration | 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/) |
Illustration | 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/) |
Illustration | 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/) |
Illustration | 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/) |
Illustration | 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 :)