Machine Learning: Introduction to Supervised and Unsupervised Learning Algorithms with Real-World Applications

How Do Computers Learn?

Machine Learning is a branch of computer science that wants to stop programming using a detailed list of commands. Instead, their aim is to implement high-level routines that teach computers how to approach new and unknown problems – these are learning algorithms.

In practice, these Algorithms empower computers to Learn and to Adapt.

We can use these algorithms to obtain insights, recognize patterns and make predictions from data, images, sounds or videos we have never seen before. Unfortunately, the true power and applications of today’s Machine Learning Algorithms remain deeply misunderstood by many people.

Through this book I want fix this confusion, I want to shed light on the most relevant Machine Learning Algorithms in use today. I will show you exactly how each algorithm works, why it works and when you should use it.

Supervised Learning Algorithms

• K-Nearest Neighbour
• Naïve Bayes
• Regressions

Unsupervised Learning Algorithms:

• Support Vector Machines
• Neural Networks
• Decision Trees