Python Programming for Machine Learning learners:

Basics of Python programming to get start machine learning and deep learning. 

Note: I would suggest strongly to use Ubuntu as the operating system for machine learning enthusiasts, because most of the existing libraries are compatible with Ubuntu. 

1. Getting Started with numbers, strings and lists 
2. Defining Functions 
3. Control flow instructions.
   if
   for
   range
4. Reading and writing files

Python based third party libreries:

Installing of numpy, scipy matplotlib, pandas, ipython, sympy, nose and scikit learn.
Installing with pip:

python -m pip install --user numpy scipy matplotlib ipython jupyter pandas sympy nose

(Or)

sudo apt-get install python-numpy python-scipy python-matplotlib ipython ipython-notebook python-pandas python-sympy python-nose

 

1. Numpy: It is the fundamental package for scientific computing in Python which support multi dimentonal array operations.
   • Numpy basics.
   • Numpy for Matlab users
   • 
     
2. Scipy: It is a collection of mathematical algorithms and convenience functions built on the Numpy extension of Python.
   • Basic functions
   • Special functions (scipy.special)
   • Integration (scipy.integrate)
   • Optimization (scipy.optimize)
   • Interpolation (scipy.interpolate)
   • Fourier Transforms (scipy.fftpack)
   • Signal Processing (scipy.signal)
   • Linear Algebra (scipy.linalg)
   • Sparse Eigenvalue Problems with ARPACK
   • Compressed Sparse Graph Routines (scipy.sparse.csgraph)
   • Spatial data structures and algorithms (scipy.spatial)
   • Statistics (scipy.stats)
   • Multidimensional image processing (scipy.ndimage)
   • File IO (scipy.io)
3. Matplotlib: It is a library for making 2D plots of arrays in Python.
   Matplotlib tutorials.
4. Pandas: It is python based source data analysis / manipulation package.
5. Scikit Learn: It is opensource python based machine learning tool box.