Machine learning is a subset of Artificial Intelligence which employs mathematical operations to find meaningful results and predictions based on data. Machine learning works on structured data i.e. organised data stored in rows and columns (tabular form). So how can one start learning Machine Learning, and what are the prerequisites? Everything is discussed one by one.
Let’s start with the first question which is quite confusing, Which language to choose for Machine Learning?
Python for sure is the best choice. Being beginner friendly and it’s vast library support makes it the best choice. One should get a little understanding of programming concepts before jumping into ML.
In machine learning, mathematics does all the magic behind the scenes. It’s obvious to ask how much mathematics one should know? Well you don’t need a PhD in mathematics to practise machine learning. Broadly, Algebra, Calculus and Statistics are all you need. Basic understanding of these topics is required. High school/ University level mathematics is sufficient to start with. Knowing these concepts will make your understanding better and you will be able to relate different ML algorithms logically. Existing frameworks and libraries in Python programming language and other languages take care of the mathematical part. Majority of mathematical formulas are already implemented and are available for use. To know which approach will suit best for your task and to interpret the results in an easy language to non-technical stakeholders and to optimise your problem, mathematical knowledge will surely help. And once you start learning you will gradually lean towards mathematics to feed your curiosity.
As a beginner, being able to communicate technically is very important. Understanding basic terminologies in Machine learning and AI domain is a must. One should get familiarized with terms frequently showing up while learning ML. This will help you understand various resources better and you will be able to debug your code quickly.
Machine learning is not a process where you give some random data to an algorithm and voilà you solved a complex problem. Modelling (creating a machine learning model) is no doubt an important part of the whole process but there are other significantly important aspects in Data science you should also focus on. Data preprocessing is an important step which you should consider giving time. Data preprocessing is about making data in a form best suitable for the algorithm to work on. Data is the heart and soul of machine learning. Your model is as good as your data is. So preprocessing of data is a must and cannot be skipped. Preprocessing includes replacing missing data and null values, outlier detection, normalisation etc. Preprocessing of data enhances the power of ML algorithms.
Data visualization is also equally important. ML works on loads of data and to understand data, visualization is the best and easiest method. And we choose what type of algorithms to use on particular dataset based on the visualizations.
So now you have everything you need to start your ML journey. Practice makes you perfect. As soon as you learn the basics, take some dataset ( abundant on the internet ) and get your hands dirty. Wish you a happy learning journey.