Sunday, February 18, 2018

List of Data Sciences and Machine Learning usefull link

Credit to: Shivam Panchal
As published by Shivam at Linked @ https://www.linkedin.com/pulse/data-science-machine-learning-beginners-path-shivam-panchal/

Platforms:

  1. What Is Hadoop? Hadoop Tutorial For Beginners https://youtu.be/n3qnsVFNEIU
  2. What is Apache Spark? The big data analytics platform explained http://www.techworld.com.au/article/629920/what-apache-spark-big-data-analytics-platform-explained/
  3. Apache Spark Tutorial: ML with PySpark https://www.datacamp.com/community/tutorials/apache-spark-tutorial-machine-learning
  4. A Beginner's Guide To Apache Pig https://hortonworks.com/tutorial/beginners-guide-to-apache-pig/
  5. Realtime Event Processing in Hadoop with NiFi, Kafka and Storm https://hortonworks.com/tutorial/realtime-event-processing-in-hadoop-with-nifi-kafka-and-storm/

Math:

  1. A Deep Dive Into Linear Algebra https://www.khanacademy.org/math/linear-algebra
  2. An Introduction to Combinatorics & Graph Theory https://www.whitman.edu/mathematics/cgt_online/cgt.pdf

Tools & Framework:

  1. TensorFlow Tutorial – Deep Learning Using TensorFlow https://youtu.be/yX8KuPZCAMo
  2. A 6-part introduction to the MXNet API https://becominghuman.ai/an-introduction-to-the-mxnet-api-part-1-848febdcf8ab
  3. Keras Tutorial: The Ultimate Beginner's Guide to Deep Learning in Python https://elitedatascience.com/keras-tutorial-deep-learning-in-python

Data Visualization:

  1. Building Python Data Apps with Blaze and Bokeh https://youtu.be/1gD9LMqREDs
  2. Matplotlib Tutorial: Python Plotting https://www.datacamp.com/community/tutorials/matplotlib-tutorial-python
  3. Python Bokeh Tutorial - Creating Interactive Web Visualizations https://youtu.be/Mz1AXUE0nR4

Concepts:

  1. Simple Linear Regression https://onlinecourses.science.psu.edu/stat501/node/250
  2. Simple and Multiple Linear Regression in Python https://medium.com/towards-data-science/simple-and-multiple-linear-regression-in-python-c928425168f9
  3. Linear Regression in R https://www.tutorialspoint.com/r/r_linear_regression.htm
  4. An Introduction To Logistic Regression http://ufldl.stanford.edu/tutorial/supervised/LogisticRegression/
  5. Building A Logistic Regression in Python, Step by Step by Susan Li https://medium.com/towards-data-science/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8
  6. Supervised and Unsupervised Machine Learning Algorithms https://machinelearningmastery.com/supervised-and-unsupervised-machine-learning-algorithms/
  7. 6 Easy Steps to Learn Naive Bayes Algorithm (with codes in Python and R) https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/
  8. A Tutorial on Support Vector Machines for Pattern Recognition http://www.cs.northwestern.edu/~pardo/courses/eecs349/readings/support_vector_machines4.pdf
  9. A Complete Tutorial on Tree Based Modeling from Scratch (in R & Python) https://www.analyticsvidhya.com/blog/2016/04/complete-tutorial-tree-based-modeling-scratch-in-python/

Python:

  1. A Complete Tutorial to Learn Data Science with Python from Scratch https://www.analyticsvidhya.com/blog/2016/01/complete-tutorial-learn-data-science-python-scratch-2/
  2. NumPy Tutorial: Data analysis with Python https://www.dataquest.io/blog/numpy-tutorial-python/
  3. Scipy Tutorial: Vectors and Arrays (Linear Algebra) https://www.datacamp.com/community/tutorials/python-scipy-tutorial
  4. Python Pandas Tutorial https://www.tutorialspoint.com/python_pandas/
  5. Machine Learning with scikit learn Part 1 & 2 https://youtu.be/2kT6QOVSgSghttps://youtu.be/WLYzSas511I

CS:

  1. A Thorough Overview of Computational Logic https://www.cs.utexas.edu/users/boyer/acl.pdf

Game Theory:

  1. Game Theory - A 3 Part Introduction https://youtu.be/x8gOi7D6QeQ

Statistics:

  1. Correlation & causality https://www.khanacademy.org/math/probability/scatterplots-a1/creating-interpreting-scatterplots/v/correlation-and-causality
  2. Analysis of variance (ANOVA) https://www.khanacademy.org/math/statistics-probability/analysis-of-variance-anova-library
  3. Understanding Hypothesis Tests: Significance Levels (Alpha) and P values in Statistics https://shar.es/1PANrc
  4. Characteristics of Good Sample Surveys and Comparative Studies https://onlinecourses.science.psu.edu/stat100/node/3
  5. Descriptive and Inferential Statistics https://www.thoughtco.com/differences-in-descriptive-and-inferential-statistics-3126224
  6. Intro to Probability Theory https://youtu.be/f9XFM8YLccg
  7. Introduction to Conditional Probability & Bayes theorem for data science https://www.analyticsvidhya.com/blog/2017/03/conditional-probability-bayes-theorem/
  8. Central limit theorem https://www.khanacademy.org/math/statistics-probability/sampling-distributions-library/sample-means/v/central-limit-theorem

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