How to start Machine Learning: A step by step guide to learn the emerging tool of AI

Machine Learning is one of the most popular career choices in modern times. But still millions of questions are in students’ minds what is exactly machine learning and how to learn it? Here we have covered all the key facts about machine learning. So let’s get started.

What is Machine Learning?

Machine Learning is the science of computers to learn and behave like a human without being explicitly programmed. It uses Artificial intelligence to enable machines and learn a task from experience without programming them specifically. So, we can say that Machines learn automatically without human hand-holding.

The process of Machine learning starts with feeding them good quality data and then trains the machines by building various machine learning models using the data & different algorithms. The choice of an algorithm should depend on what type of data do we have and what kind of task we are trying to automate.

How to start Machine learning?

An Introduction to Machine Learning - Becoming Human: Artificial ...

Here is a roadmap that you can follow on your way to start your career as a talented machine learning engineer.

Step 1: Understand the Prerequisites

As you are starting your career in this field so you need to follow some prerequisites before starting your learnings. The things that you need to know before starting machine learning includes Linear Algebra, Multivariate Calculus, Statistics, and Python. Don’t be scared if you don’t know these, you just need a basic understanding of the topics.

Step 2: Learn various Machine learning concepts

If you have followed Step 1, you can move to actually learn ML, which is actually a fun part. It will be best if we move on to the more basic concepts.

(A). Terminologies of machine learning:

Before start your learning you should have to know the given terminologies here:

  1. Model
  2. Feature
  3. Target
  4. Training
  5. Prediction

(B). Types of machine learning:

  • Supervised Learning
  • Unsupervised Learning
  • Semi-supervised Learning
  • Reinforcement Learning

(C). How to practice ML:

The most time-consuming topis in ML is data collection, integration Reinforcement Learning. So make sure to practice this because you need high-quality data. But some times there is a large amount of data that is often dirty. So most of your time will go here.

Learn various models and practice on real projects It is also important to interpret the results obtained by using different models.

(D). Resources for Learning Machine Learning:

There are various online and offline resources that are both free and paid as well that can be used to learn Machine Learning.

Step 3: Combine your Theoretical knowledge with Practical Knowledge

Once you will understand the basics of ML, you can move on to the next part which is competition. These will basically make you even more expert in MachineLearning by combining your theoretical knowledge with practical implementation.

Now you have completed all the steps and such simple challenges. You can also enhance your skills by working on more and more challenges and eventually creating more and more creative and difficult Machine Learning projects.