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1. What is Maching Learning?

Machine learning is about teaching computers how to learn from data to make decisions or predictions. For true machine learning, the computer must be able to learn to identify patterns without being explicitly programmed to.
It sits at the intersection of statistics and computer science, yet it can wear many different masks. You may also hear it labeled several other names or buzz words:
Data Science, Big Data, Artificial Intelligence, Predictive Analytics, Computational Statistics, Data Mining, Etc...
While machine learning does heavily overlap with those fields, it shouldn't be crudely lumped together with them. For example, machine learning is one tool for data science (albeit an essential one). It's also one use of infrastructure that can handle big data.
Here are some examples:

• Supervised Learning - Your email provider kindly places that sketchy email from the "Nigerian prince with $50,000 to deposit into an overseas bank account" into the spam folder.
• Unsupervised Learning - Marketing firms "kindly" use hundreds of behavior and demographic indicators to segment customers into targeted offer groups.
• Reinforcement Learning - A computer and camera within a self-driving car interact with the road and other cars to learn how to navigate a city.
  

Don't worry if some of those terms mean nothing to you. After you complete this guide, you'll be able to apply each of those techniques yourself! (Self-driving car not included.)

2. Why Learn Machine Learning?Massive Global DemandThe demand for machine learning is booming all over the world. Entry salaries start from $100k – $150k. Data scientists, software engineers, and business analysts all benefit by knowing machine learning.

Data is Power
Data is transforming everything we do. All organizations, from startups to tech giants to Fortune 500 corporations, are racing to harness their data. Big and small data will continue to reshape technology and business.
It's Fun as Hell!OK, we may be a bit biased, but ML is really damn cool. It has a unique blend of discovery, engineering, and business application that makes it one-of-a-kind. You’ll have a ton of fun with this rich and vibrant field.

3. The Self-Starter WayThe self-starter way of mastering ML is to learn by "doing *****." (not the technical term).

Traditionally, students will first spend months or even years on the theory and mathematics behind machine learning. They'll get frustrated by the arcane symbols and formulas or get discouraged by the sheer volume of textbooks and academic papers to read.

Unless you want to devote yourself to Ph.D research, that's way overkill. For most people, the self-starter approach is superior to the academic approach for 3 reasons:

• You'll have more fun. By cycling between theory, practice, and projects, you'll arrive at real results faster. This is a huge boost in morale.
• You'll build practical skills the industry demands. Businesses don't care if you can derive proofs. They care if you can turn their data into gold.
• You'll build your portfolio along the way. With hands-on projects, you'll conveniently build a portfolio you can show employers. In a nutshell, the self-starter way is faster and more practical.
  

However, it definitely puts more responsibility in your own hands to follow through. Hopefully this guide will help you stay on track! Here are the 4 steps to learning machine through self-study:

•                         1 Prerequisites                                Build a foundation of statistics, programming, and a bit of math.
                         
  
•                         2 Sponge Mode                                Immerse yourself in the essential theory behind ML.
                         
  
•                         3 Targeted Practice                                Use ML packages to practice the 9 essential topics.
                         
  
•                         4 Machine Learning Projects                                Dive deeper into interesting domains with larger projects.
                         
  
  

Source:  elitedatascience.com/learn-machine-learning