A Non-Programmer’s Guide to Learning Machine Learning

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Artificial intelligence might seem intimidating, but it isn’t actually as complex as you might think. Many of the tools that have been developed over the last decade or so have all helped to make artificial intelligence and machine learning more accessible to engineers with varying degrees of experience and knowledge.

Today, we’ve got to a stage where it’s now accessible even to people who have barely written a line of code in their life! Pretty exciting, right?

But if you’re completely new to the field, it can be challenging to know how to get started – fortunately, we’re about to help you overcome that first hurdle. If you are an AI denier, then be sure to first read ‘why learn Machine Learning as a non-techie’ before you move forward. A strong purpose and belief is the first step to learning anything new.

Alright, now here’s how you can get started with artificial intelligence and machine learning techniques quickly.

0. Use a free MLaaS or a no code interactive machine learning tool to experience first hand what is possible with learning machine learning: Some popular examples of no code machine learning as a service option are Microsoft Azure, BigML, Orange, and Amazon ML. Read Q2 under the FAQ section below to know more on this topic.

1. Learn Linear Algebra: Linear Algebra is the elementary unit for ML. It helps you effectively comprehend the theory behind the Machine learning algorithms and how they work. It also improves your math skills such as statistics, programming skills, which are all other skills that helps in ML.

Learning Resources:

Linear Algebra for Beginners: Open Doors to Great Careers

Linear algebra Basics

2. Learn just enough Python or any programming: Now, you can get started with any language of your interest, but we suggest Python as  it’s great for people who are new to programming. It’s easy to learn due to its simple syntax. You’ll be able to quickly implement the ML algorithms. Also,  It has a rich development ecosystem that offers a ton of libraries and frameworks in Machine Learning such as Scikit Learn, Lasagne, Numpy, Scipy, Theano, Tensorflow, etc.

Learning Resources:

Python Machine Learning

Learn Python in 7 Days

Python for Beginners 2017 [Video]

Learn Python with codecademy

Python editor for beginner programmers

3. Learn basic Probability Theory and statistics: A lot of fundamental Statistical and Probability Theories form the basis for ML. You’ve probably already learned Probability and statistics in school, it easy to dive into advanced statistics for ML. Machine learning in its currently widely used form is a way to predict odds and see patterns. Knowing statistics and probability is important as it will help you with better understanding of why any machine learning algorithm works. For example, your grounding in this area, will help to ask the right questions, choose the right set of algorithms and know what to expect as answers from your ML model on questions such as:

    • What are the odds of this person also liking this movie given their current movie watching choices ( Collaborative filtering and content-based filtering)
    • How similar is this user to that group of users who brought a bunch of stuff on my site (clustering, collaborative filtering, and classification)
    • Could this person be at risk of cancer given a certain set of traits and health indicator observations (logistic regression)
    • Should you buy that stock (decision tree)

Also, check out our interview with James D. Miller to know more about why learning stats is important in this field.

Learning resources:

Statistics for Data Science [Video]

Read the source article at Packt>Hub.