A dummy’s guide to Machine Learning

“A breakthrough in machine learning would be worth 10 Microsofts”,said Bill Gates once.

Why would he possibly say something like that? A much-hyped tech jargon or something really mindblowing? What is Machine Learning anyway? Why should I learn it? Will it pave a successful career path for me?
Whoa! So many questions arise when a newbie hears about ML and AI. There are answers to each of the spread across the internet. All you need is to sit and surf through them. But how does one clear all the doubts in one shot? With this, ‘A dummy guide to Machine Learning’. .
WTF is Machine Learning anyway?
Data Sciences, Big Data Analytics, Artificial Intelligence, Predictive Analytics, Computational Statistics… all these fancy words spin around your world? They sure would. So let me put it plain and simple. Machine Learning is about teaching computers how to learn from data to make decisions or predictions. It gives the computer to learn without being explicitly programmed. In short, you teach your computer how to think.
What are the types?
 
They are majorly of three types. The names and examples sum it all.
  • Supervised Learning– You apply rules/filters in your email inbox to directly delete or archive the spam messages from marketing channels.
  • Unsupervised Learning– Your camera automatically detects your face/smile.
  • Reinforced Learning– The self-driven cars having cameras, computer and controllers interacting with the roads and nearby surroundings/obstacles to give you a safe ride.

Why should I learn it?

Are you an Iron Man fan? Do you like Jarvis or not? Yes, you do. Wouldn’t it be cool to build one yourself? It’s a really fun and cool skillset with a huge global demand. Entry salaries start from $100k – $150k. Data scientists, software engineers, and business analysts all benefit by knowing machine learning. Big bucks, lots of fun and innovation, what else do you possibly need?
Are there any prerequisites?
You don’t need to be a pro mathematician or a veteran in programming to learn machine learning but you do need to get the basics right. For starters, a ground knowledge of these three are sufficient:
  • Python for data science
  • Statistics for data science
  • Mathematics for data science

Are there any practical examples of this theory?

 
Yes, there are plenty! In fact all around us. I’ll give you some everyday examples which you can relate to:
  • Notice the recommended products on Amazon and other e-commerce websites? They are all machine learning based recommendation systems. They learn from you- your surfing habits, purchasing behavior, history, and other traceable patterns.
  • Your iPhone opens with your thumbprint, it’s no different!
  • How can we miss out Siri if we talk about the iPhone? Same goes for the google assistant.
  • Tesla Self driven Cars and so much more.
When and where do I start?
 
Right away with us! We at Coding Ninjas constantly strive to get you the best courses and study resources to equip you with the latest and trending technology. Cognizance, a special workshop on Machine Learning will give you a 360-degree overview and hands-on working experience. Limited seats available register today!