Congratulations if you are reading this. That simply means, you have understood the importance of mathematics to truly understand and learn Data Science and Machine Learning.

In this course, we will cover right from the foundations of Algebraic Equations, Linear Algebra, Calculus including Gradient using Single and Double order derivatives, Vectors, Matrices, Probability and much more.

Mathematics form the basis of almost all the Machine Learning algorithms. Without maths, there is no Machine Learning. Machine Learning uses mathematical implementation of the algorithms and without understanding the math behind it is like driving a car without knowing what kind of engine powers it.

You may have studied all these math topics during school or universities and may want to freshen it up. However, many of these topics, you may have studied in a different context without understanding why you were learning them. They may not have been taught intuitively or though you may know majority of the topics, you can not correlate them with Machine Learning.

This course of Math For Machine Learning, aims to bridge that gap. We will get you upto speed in the mathematics required for Machine Learning and Data Science. We will go through all the relevant concepts in great detail, derive various formulas and equations intuitively.

This course is divided into following sections,

• Algebra Foundations

  In this section, we will lay the very foundation of Algebraic Equations including Linear Equations and how to plot them. We will understand what are Exponents, Logs, Polynomial and quadratic equations. Almost all the Machine Learning algorithms use various functions for loss measurement or optimization. We will go through the basics of functions, how to represent them and what are continuous and non-continuous functions.

• Calculus

  It is said that without calculus and differential equations, Machine Learning would have never been possible. The Gradient Descent using derivatives is essence of minimizing errors for a Machine Learning algorithm. We will understand various terms of Rate of Change, Limits, What is Derivative, including Single, Double and Partial Derivatives. I will also explain with an example, how machine learning algorithms use calculus for optimization.

• Linear Algebra

  Linear Algebra is the mathematics of the 21st Century. Every record of data is bound by some form of algebraic equation. However, it's nearly impossible for humans to create such an equation from a dataset of thousands of records. That's where the ability of vectors and matrices to crunch those numerical equations and create meaningful insights in the form of linear equations help us. We will see, right from the foundations of Vectors, Vector Arithmetic, Matrices and various arithmetic operations on them. We will also see, how the vectors and matrices together can be used for various data transformations in Machine Learning and Data Science.

• Probability

  Probability plays an important role during classification type of machine learning problems. It is also the most important technique to understand the statistical distribution of the data. Conditional probability also helps in classification of the dependent variable or prediction of a class.

With all of that covered, you will start getting every mathematical term that is taught in any of the machine learning and data science class.

Mathematics has been my favorite subject since the childhood and you will see my passion in teaching maths as you go through the course.

I firmly believe in what Einstein said, "If you can not explain it simple enough, You have not understood it enough.". I hope I can live upto this statement.

I am super excited to see you inside the class. So hit the ENROLL button and I will see you inside the course.

You will truly enjoy Mathematics For Machine Learning....