deepansh27/Machine_Learning_Tutorials

Code, exercises and tutorials of my personal blog ! 📝

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
• II. Machine Learning
• III. Deep Learning
• IV. Data Engineering
• V. 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	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

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	---

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	---

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	---

Deep Learning

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	---
Convolutional Neural Network	6mn	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

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
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
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

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. A guide to Face Detection - For digitalminds.io : 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 :)