My Learning Path for Machine Learning with Python

I am newbie in Machine Learning but working with data a lot as an ETL developer. I simply fall for ML when I've first introduced with it, so I have given a target date to myself to complete the below learning schedule with my day to day work :-)

Hoping I am not too harsh on my self.

Let's not do the talking but the work :

Python

For beginners, I will suggest below reference material which can help them learning python quickly

• BOOK - Python for Informatics
• [Course - Programming for Everybody by University of Michigan] (https://www.coursera.org/learn/python)

These 2 source are more than enough to learn basics of python.

Python Pandas

• [10 minutes to pandas] (http://pandas.pydata.org/pandas-docs/stable/10min.html#min)
• [10 minutes to pandas - IPYTHON Notebook] (http://www.datagenx.net/2017/01/10-minutes-with-pandas-library.html)
• [pandas turorials #1] (http://pandas.pydata.org/pandas-docs/stable/tutorials.html)
• [pandas turorials #2] (https://bitbucket.org/hrojas/learn-pandas)
• [Some tutorials on my Site] (http://www.datagenx.net/search/label/Pandas)
• [A Intro to Pandas] (http://synesthesiam.com/posts/an-introduction-to-pandas.html)
• [Python Pandas CookBook] (http://pandas.pydata.org/pandas-docs/stable/cookbook.html)

Python Scipy

• [Scipy Notes] (http://www.scipy-lectures.org/)

Python Scikit-Learn (Machine Learning)

• [scikit-learn Tutorials] (http://scikit-learn.org/stable/tutorial/)
• [PyCon 2014 Scikit-learn Tutorial] (https://github.com/atulsingh0/sklearn_pycon2014)
• [PyCon 2015 Scikit-learn Tutorial] (http://nbviewer.jupyter.org/github/jakevdp/sklearn_pycon2015/blob/master/notebooks/Index.ipynb)

Some E-Learning Courses on ML

• [Build Intelligent Applications] (https://www.coursera.org/specializations/machine-learning)
• [Gain new insights into your data] (https://www.coursera.org/specializations/data-science-python)
• [Analyze Text, Discover Patterns, Visualize Data] (https://www.coursera.org/specializations/data-mining)

Machine Learning Books

• [Python Data Science Handbook] (https://github.com/jakevdp/PythonDataScienceHandbook)
• [Elements of Statistical Learning. Hastie, Tibshirani, Friedman ] (http://www-stat.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf)
• [All of Statistics. Larry Wasserman ] (http://www.ucl.ac.uk/~rmjbale/Stat/wasserman2.pdf)
• [Machine Learning and Bayesian Reasoning. David Barber ] (http://web4.cs.ucl.ac.uk/staff/D.Barber/textbook/090310.pdf)
• [Gaussian Processes for Machine Learning. Rasmussen and Williams ] (http://www.gaussianprocess.org/gpml/chapters/RW.pdf)
• [Information Theory, Inference, and Learning Algorithms. David MacKay ] (http://www.cs.toronto.edu/~mackay/itprnn/book.pdf)
• [Introduction to Machine Learning. Smola and Vishwanathan] (http://alex.smola.org/drafts/thebook.pdf)
• [A Probabilistic Theory of Pattern Recognition. Devroye, Gyorfi, Lugosi.] (http://www.szit.bme.hu/~gyorfi/pbook.pdf)
• [Introduction to Information Retrieval. Manning, Rhagavan, Shutze] (http://nlp.stanford.edu/IR-book/pdf/irbookprint.pdf)
• [Forecasting: principles and practice. Hyndman, Athanasopoulos. (Online Book) ] (http://otexts.com/fpp/)
• Introduction to Information Retrieval (Manning et al. 2008)

Probability / Stats

• [Introduction to statistical thought. Lavine ] (https://www.math.umass.edu/~lavine/Book/book.pdf)
• [Basic Probability Theory. Robert Ash] (http://www.math.uiuc.edu/~r-ash/BPT/BPT.pdf)
• [Introduction to probability. Grinstead and Snell] (http://math.dartmouth.edu/~prob/prob/prob.pdf)
• [Principle of Uncertainty. Kadane] (http://uncertainty.stat.cmu.edu/wp-content/uploads/2011/05/principles-of-uncertainty.pdf)

Linear Algebra / Optimization

• [Linear Algebra, Theory, and Applications. Kuttler] (https://math.byu.edu/~klkuttle/Linearalgebra.pdf)
• [Linear Algebra Done Wrong. Treil] (http://www.math.brown.edu/~treil/papers/LADW/LADW.pdf)
• [Applied Numerical Computing. Vandenberghe] (http://www.seas.ucla.edu/~vandenbe/103/reader.pdf)
• [Applied Numerical Linear Algebra. James Demmel] (http://uqu.edu.sa/files2/tiny_mce/plugins/filemanager/files/4281667/hamdy/hamdy1/cgfvnv/hamdy2/h1/h2/h3/h4/h5/h6/Applied Numerical Linear .pdf)
• [Convex Optimization. Boyd and Vandenberghe] (http://www.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf)

Genetic Algorithms

• [A Field Guide to Genetic Programming. Poli, Langdon, McPhee.] (http://dces.essex.ac.uk/staff/rpoli/gp-field-guide/A_Field_Guide_to_Genetic_Programming.pdf)
• [Evolved To Win. Sipper] (http://www.lulu.com/ie/en/shop/moshe-sipper/evolved-to-win/ebook/product-18719826.html)
• [Essentials of Metaheuristics. Luke ] (http://cs.gmu.edu/~sean/book/metaheuristics/Essentials.pdf)

Some Other Books to follow

• Mastering Machine Learning with Scikit-Learning
• Building Machine Learning System with Python
• Learning scikit-learn: Machine Learning in Python
• scikit-learn Cookbook

Machine Learning Videos and Lectures

• [Machine Learning by Tom Mitchell and Maria-Florina Balcan] (http://www.cs.cmu.edu/~ninamf/courses/601sp15/lectures.shtml)

Some Other Learning Paths to follow

• 7 Steps to Mastering Machine Learning With Python