Start With Machine Learning
Since you have navigated your way to the most crucial section of my website, I would like to congratulate you in advance, as you will learn the principles of Machine Learning (ML) straightforwardly. This page will provide you with an overview of all the key points you need to bear in mind while making preparations for pursuing our ML course. It will also provide you with all the essential information regarding ML that you might spend hours finding on the internet. As we dig deeper into the course, you will slowly start to understand terms like machines, neural networks, algorithms, deep learning, big data etc.
The Objective of This Course: The course is designed to shape your thought process as a technical engineer and provide you with the theoretical knowledge about Machine Learning. You will also get the practical experience required to apply the learnt skills and techniques in daily world problems.
Down below, you will find the overview of the three steps required to become a machine learning expert in a few months.
Step 1: Understand The Prerequisites
Before heading on to answer one of the most popular questions of all time, ie. “How to learn AI fast?”, there are three prerequisites** to Machine Learning that you need to know about:
1. Statistics: Classical statistics is the backbone of machine learning. Although statistics is a sub-topic of maths, it is treated as a separate subject due to its vastness. Algorithms derived from classical statistics are required to interpret values in a data set. Therefore, to design and code various machine learning models, you should have a firm grasp of statistics.
2. Coding: It is the other indispensable part of machine learning, including managing and manipulating large amounts of data. You should have the cursory knowledge about Python, R, C++ or any other programming language compatible with machine learning APIs. If you haven’t learned a relevant programming language, you will need to if you want to make further progress in this field.
3. Mathematics and Calculus: Machine learning is all about creating algorithms that can learn from large datasets to predict an outcome. It is built on mathematical principles like linear algebra, calculus and probability. Mathematics is essential for solving any data science project. Fundamental knowledge about the subject is required to understand the concept of different algorithms and recognise which one is better and why.
**Since advance mathematics, statistics and computer programming are very vast in their respective fields; we will not provide you with the absolute basic knowledge about the subjects. However, we will teach you the overview of some particular topics in the subjects as and when required.
Step 2: Learn General Machine Learning Concepts
Since you are done with learning and excelling the prerequisites, you can now learn the Machine Learning concepts. During the course, you will learn the following:
The meaning of machine learning its categories and types in detail. Different algorithms, like Linear Regression, K-Nearest Neighbors, K-Means Clustering. Bias & Variance. The definition and difference between Artificial Neural Networks and Deep Learning. What are Decision Trees and how to draw one?
Learning all of the above, you will be familiar with the development environment in different APIs and finally taught how to build and optimise a Python language model.
Step 3: Self Motivation And The Will To Never Quit
There is no doubt that learning maths, statistics, and how to code is extremely important, but on the same side, the will to never quit and stay determined is equally essential. I’ve seen many people who want to build a career in Artificial Intelligence for many reasons: the hype around it, the high salary offerings and even the fear of termination of future jobs! However, all of these can prove to be a motivating factor; I firmly believe that a person should only learn AI if he is genuinely interested in it and is in love with automation.
If you are buckled up and are ready to go on this adventurous journey with us, please press on the button down below which would redirect you to the full Machine Learning Course.