After reading this chapter, you will be able to understand the different terminologies used in machine learning and the process of performing machine learning activities. Also, you will be able to look at a problem statement and immediately identify which problem domain the problem belongs to; whether it is a classification or a regression problem, and such. You will find connections between seemingly disparate sets of problems. You will also find basic intuition behind some of the major algorithms used in machine learning today. Finally, I wrap up this chapter with a motivating example of identifying hand written digits using a supervised learning algorithm. This is analogous to your Hello world program.
I have created the following Twitter account for you (my dear reader) to get in touch with me. If you want to ask a question, post errata, or just have a suggestion, tag this twitter ID and I will surely get back as soon as I can.
https://twitter.com/fsharpforml
I will post contents here that will augment the content in the book.
The preceding image shows some of the areas where machine learning techniques are used extensively. In this book, you will learn about most of these usages.
Machines learn almost the same way as we humans do. We learn in three different ways.
As kids our parents taught us the alphabets and thus we can distinguish between the A's and H's. The same is true with machines. Machines are also taught the same way to recognize characters. This is known as supervised learning.
While growing up, we taught ourselves the differences between the teddy bear toy and an actual bear. This is known as unsupervised learning, because there is no supervision required in the process of the learning. The main type of unsupervised learning is called clustering; that's the art of finding groups in unlabeled datasets. Clustering has several applications, one of them being customer base segmentation.
Remember those days when you first learnt how to take the stairs? You probably fell many times before successfully taking the stairs. However, each time you fell, you learnt something useful that helped you later. So your learning got re-enforced every time you fell. This process is known as reinforcement learning. Ever saw those funky robots crawling uneven terrains like humans. That's the result of re-enforcement learning. This is a very active topic of research.
Whenever you shop online at Amazon or on other sites, the site recommends back to you other stuff that you might be interested in. This is done by a set of algorithms known as recommender systems.
Machine learning is very heavily used to determine whether suspicious credit card transactions are fraudulent or not. The technique used is popularly known as anomaly detection. Anomaly detection works on the assumption that most of the entries are proper and that the entry that is far (also called an outlier) from the other entries is probably fraudulent.
In the coming decade, machine learning is going to be very commonplace and it's about time to democratize the machine learning techniques. In the next few sections, I will give you a few examples where these different types of machine learning algorithms are used to solve several problems.