The first step is to learn maths. Although it is not necessary to learn maths to become a data scientist, it is highly recommended. There are ample of libraries present like sklearn that helps to implement all machine learning algorithms . After that, you'll need to learn maths behind machine learning algorithms like regression,knn,regression trees etc. Then, you can move on to the intresting part i.e. machine learning and deep learning. On this page you'll find the instructions for maths. Refer to our other pages for ML and DL.

You'll need to study four fields of mathematics. Linear Algebra, Calculus, Statistics,Probability. As mostly data will be used in matrix form in some ml algorithms and neural networks are made up of matrices in deep learning, linear algreba is required to perform functions on those matrices. Calculus is the study of change. As of now, the best use case of calculus is in backtracking which is one of the curcial factor in deep learning. Statistics and probabilty are also significant. While statistics is used to find next data based on previous data, probability tells us what are the chances of that condition being true. Here are the link provided to learn all of these.

Many of you might be scared to learn mathematics because of the fear or the experiences you had with maths but it is not like that here. If you have dedication, you'll do just fine. It is not like you have to study the complete Linear Algebra or calculus or any other field if that matter, you only need to focus on main points that are relevant in machine learning. These Resources are available absolutely free and still their quality is best and up to the mark. All the best for what is going to be your wonderful journey.