Machine Learning Is Easy If You Look Deeper

 Machine learning is a subfield of computer science that evolved from the study of pattern recognition and computational learning theory in artificial intelligence. In 1959, Arthur Samuel defined machine learningas a "Field of study that gives computers the ability to learn without being explicitly programmed". 

This post aims to take a newcomer from minimal knowledge of machine learning all the way to knowledgeable practitioner in a few steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? Which complement one another? What is the best order in which to use selected resources?

You do not need to be a programmer. There are many machine learning tools that provide graphical user interfaces or command line interfaces allowing you to build models and make accurate predictions without writing a line of code.

You do not need to know any math. Just like you don’t need math to drive Microsoft Excel, you do not need a background in mathematics to drive many if not most of the machine learning tools available. Figure out what capabilities you need, pick a tool and see for yourself.

You do not need to learn a specific programming language. Pick a programming language and you will discover that there are machine learning libraries available. Some libraries have been around longer and are more mature. There are also web services APIs for machine learning as a service that support a range of different languages. You don’t even need to write code to do machine learning if you don’t want to. In the end you should choose a language that best suits your project or your background if you are doing self-study.

You do not need to be an expert at machine learning. You do not need to be a machine learning expert to use machine learning tools. In fact I recommend that you use machine learning platforms like WEKA when getting started to accelerate your learning and rapidly deliver results and build your confidence.

You do not need to be an expert in the tool. I see a lot of expert programmers that do not know how to use the editor or IDE very well. It slows them down. You can learn to drive a tool better than expert in machine learning or in the tool when you make the tool the subject of study. Few people do, and if you do it will give you a huge advantage. You could even start answering questions on expert forums on how to use the tool well because you bothered to study it when other practitioners didn’t.

There is a lot of variation in what people consider a "data scientist." This actually is a reflection of the field of machine learning, since much of what data scientists do involves using machine learning algorithms to varying degrees. Is it necessary to intimately understand kernel methods in order to efficiently create and gain insight from a support vector machine model? Of course not. Like almost anything in life, required depth of theoretical understanding is relative to practical application. Gaining an intimate understanding of machine learning algorithms is beyond the scope of this article, and generally requires substantial amounts of time investment in a more academic setting, or via intense self-study at the very least.

The good news is that you don't need to possess a PhD-level understanding of the theoretical aspects of machine learning in order to practice, in the same manner that not all programmers require a theoretical computer science education in order to be effective coders.

Andrew Ng's Coursera course often gets rave reviews for its content; my suggestion, if you have the time and interest, you could take Andrew Ng's Machine Learning course on Coursera. Machine learning tools save you time by automating aspects of a machine learning project.

There are platforms that you can use to work through a machine learning project end-to-end. There are also libraries that provide capabilities for one piece of a machine learning project.

Using the right machine learning tools is as important as using the right machine learning algorithms

We will add Deep learning as we wrapping up. 

Deep learning is everywhere! Deep learning builds on neural network research going back several decades, but recent advances dating to the past several years have dramatically increased the perceived power of, and general interest in, deep neural networks. For those interested in digging deeper into deep learning, I recommend starting with the following free online book: 

▪  Neural Networks and Deep Learning by Michael Nielsen