Now that we are done with the basics of Machine Learning, what it is, how many types , why is data oh-so important ,its time to get our hands dirty!! When it comes to programming there really is no other way to learn other than …by…coding 
We were quite new to the world Python when we started although we are quite experienced in many other technologies so we were not looking for a beginners course . Below is the “Learning Path” we created in Cennest for all our internal employees wanting to get started with ML using Python
Setting up the Environment
We can install Python directly and then install all other required libraries ( as and when needed ) using “pip” or you can use “Anaconda” which is a package management system for Python which will preinstall most frequently used libraries to begin with.
We found the instructions at https://github.com/ageron/handson-ml#python–required-libraries quite consolidated wrt going via Anaconda as well as using “pip”.( We also love the book)
Getting Started with Code
(Please note that we are experienced programmers so we were not looking for “getting started kind of courses”)
- Start with a quick introduction to python ( and panda) from https://www.learnpython.org/en/Welcome or do a quick read from https://docs.python.org/3/tutorial/
- All of us found Python pretty easy to get started and we jumped right into code next. That is when we got stumped by the very extensive use of some core python libraries i.e Numpy, pandas and Matplotlib so ensure you get a good grasp of these libraries and their usage before even reading sample code
- http://cs231n.github.io/python-numpy-tutorial/#numpyhgh
- https://docs.scipy.org/doc/numpy/user/basics.creation.html
- The Python DataScience Handbook at Microsoft AI School is an excellent reference for usage of python and its libraries especially for Data learning! We especially liked the section on MatPlotLib.
After this…Code, Code and Code to simply become better 
Next in line:- Load up and analyze your data!!
Until Next Time
Team Cennest!