How To Get Started with AI after your MBA — Part 2

The previous post on the series of articles on how to get into AI especially after your MBA briefly introduced the topic and a list of resources to keep one updated on the latest developments in the field.

This is next part of the adventure where I will try to chalk out an actual plan of getting started with learning AI without seemingly getting lost.

Okay by now I hope you are in the mood to get a little deep into the field of AI (maybe even start learning to code up your own programs). So to start with, before actually getting to start reading about AI algorithms you need to have 2 things sorted:

  1. You have an interest and understanding of Tech Space (startups, companies, various technologies, tools and business models around them). I hope my previous post would have helped you getting started with this
  2. A good understanding of programming to kick off this adventure

Point 1 is something you have to work on by your self. Point 2 is something we can talk about.

AI without programming is like Physics without Mathematics. So you need to know how to code, at least on a basic level.

In case you have absolutely no idea about coding, try learning Python from Codecademy. Get a hang of what programming is, what are classes, lists, arrays, functions and other data structures. There is no short cut around this, you have to do this.

Perhaps if you are done with Codecademy then you can start practising a few programming problems on Codechef. Remember the goal here is not to master Python or any of those fancy frameworks like Django or something, the goal is to understand how code works.

On the other hand if you are already familiar with basic programming from your school or engineering days you can directly jump to this one source right away: DataCamp’s Python Course. Why this — because it’s one of the most simple online tutorial you can get to get a hang of how Python is used in AI and Machine Learning. Particularly this course is best for getting an understanding of various libraries like Numpy and others which are the foundation of AI.

Great, so far so good. Now we are at a stage when you know how to code your little algos, use a bunch of libraries and talk about technologies.

What’s next.

All the above mentioned online resources come with their own in-browser IDE so you don’t have to worry about installing Python and all. But from here on get Python on your system. There are many ways around that but the best one especially for AI and Machine Learning is to install Anaconda. What exactly is that? It’s just a Python distribution which is nothing but all your libraries installed at once which you need for your AI codes. The other way can be to install Python separately along with a separate text editor like Sublime and using your system’s console. Well that kind of a setup is good for a hard core programmer but we can use Conda/Anaconda which provides everything right in one place.

Pro Tip: Do read about what IDEs are, what is a text editor, what are different types of editors, what is Git, what is BitBucket and what is GitHub. Get an understanding of all these to get a feel of how a developer machine really works.

A lot of work in AI is around Data Collection and Cleaning. Often this is one part which people misses out because sadly everyone talking about AI will talk about fancy Neural Networks and stuffs but not about gory details of cleaning up the CSV files.

One of the best course you can do for this is Udemy’s Machine Learning for Trading. Don’t do the full course but stick to sessions where they teach and talk about Numpy arrays, Pandas and data files. Do some PoCs and you can get a good understanding of how to treat data in Python.

Great, by now you must be good with basic programming and maybe some of the advanced stuffs like including libraries and playing around with their in-built functions and methods.

The next part of this series will specifically deal with in-depth details of various AI resources, books, courses and how to actually start learning the most awaited stuff.

Stay Tuned! and if you find this helpful, leave a clap!

Source: Deep Learning on Medium