Sunday, February 18, 2018

List of Data Sciences and Machine Learning usefull link

Credit to: Shivam Panchal
As published by Shivam at Linked @


  1. What Is Hadoop? Hadoop Tutorial For Beginners
  2. What is Apache Spark? The big data analytics platform explained
  3. Apache Spark Tutorial: ML with PySpark
  4. A Beginner's Guide To Apache Pig
  5. Realtime Event Processing in Hadoop with NiFi, Kafka and Storm


  1. A Deep Dive Into Linear Algebra
  2. An Introduction to Combinatorics & Graph Theory

Tools & Framework:

  1. TensorFlow Tutorial – Deep Learning Using TensorFlow
  2. A 6-part introduction to the MXNet API
  3. Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python

Data Visualization:

  1. Building Python Data Apps with Blaze and Bokeh
  2. Matplotlib Tutorial: Python Plotting
  3. Python Bokeh Tutorial - Creating Interactive Web Visualizations


  1. Simple Linear Regression
  2. Simple and Multiple Linear Regression in Python
  3. Linear Regression in R
  4. An Introduction To Logistic Regression
  5. Building A Logistic Regression in Python, Step by Step by Susan Li
  6. Supervised and Unsupervised Machine Learning Algorithms
  7. 6 Easy Steps to Learn Naive Bayes Algorithm (with codes in Python and R)
  8. A Tutorial on Support Vector Machines for Pattern Recognition
  9. A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python)


  1. A Complete Tutorial to Learn Data Science with Python from Scratch
  2. NumPy Tutorial: Data analysis with Python
  3. Scipy Tutorial: Vectors and Arrays (Linear Algebra)
  4. Python Pandas Tutorial
  5. Machine Learning with scikit learn Part 1 & 2


  1. A Thorough Overview of Computational Logic

Game Theory:

  1. Game Theory - A 3 Part Introduction


  1. Correlation & causality
  2. Analysis of variance (ANOVA)
  3. Understanding Hypothesis Tests: Significance Levels (Alpha) and P values in Statistics
  4. Characteristics of Good Sample Surveys and Comparative Studies
  5. Descriptive and Inferential Statistics
  6. Intro to Probability Theory
  7. Introduction to Conditional Probability & Bayes theorem for data science
  8. Central limit theorem


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