Mathematics & Statistics Foundations | Machine Learning & Ai

Learn the core mathematical concepts, Probability & Statistics for Data Science, Data Analytics, Machine & Deep Learning

What you'll learn

Understand and implement Regression, Classification, and Clustering algorithms

Learn Linear Algebra, Calculus for Machine and Deep Learning

Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning

Refresh the mathematical concepts for AI and Machine Learning

Requirements

Some basic concepts of linear algebra and calculus

Familiarity with secondary school-level mathematics will make the class easier to follow along with.

Description

The future for AI is extremely promising and it isn’t far from when we have our own robotic companions. This has pushed a lot of developers to start writing codes and start developing for AI and ML programs. However, learning to write algorithms for AI and ML isn’t easy and requires extensive programming and mathematical knowledge. Mathematics forms the core of data science and machine learning. Thus, to be the best data scientist you can be, you must have a working understanding of the most relevant math. Getting started in data science is easy thanks to high-level libraries like Scikit-learn and Keras. However, understanding the math behind the algorithms in these libraries opens an infinite number of possibilities up to you. From identifying modeling issues to inventing new and more powerful solutions, understanding the math behind it all can dramatically increase the impact you can make over the course of your career. This is a highly comprehensive Mathematics, Statistics, and Probability course, you learn everything from Set theory, Combinatorics, Probability, statistics, and linear algebra to Calculus with tons of challenges and solutions for Business Analytics, Data Science, Data Analytics, and Machine Learning. Mathematics, Probability & Statistics are the bedrock of modern science such as machine learning, predictive risk management, inferential statistics, and business decisions. 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.

Overview

Section 1: Introduction

Lecture 1 Introduction to Machine Learning with Python

Section 2: Importing

Lecture 2 Machine Learning Introduction

Lecture 3 Analytics in Machine Learning

Lecture 4 Big Data Machine Learning

Lecture 5 Emerging Trends Machine Learning

Lecture 6 Data Mining

Lecture 7 Data Mining Continues

Lecture 8 Supervised and Unsupervised

Section 3: Basics of Statistics Sampling

Lecture 9 Sampling Method in Machine Learning

Lecture 10 Technical Terminology

Lecture 11 Error of Observation and Non Observation

Lecture 12 Systematic Sampling

Lecture 13 Cluster Sampling

Section 4: Basics of Statistics Data types and Visualization

Lecture 14 Statistics Data Types

Lecture 15 Qualitative Data and Visualization

Section 5: Basics of Statistics Probability

Lecture 16 Machine Learning

Lecture 17 Relative Frequency Probability

Lecture 18 Joint Probability

Lecture 19 Conditional Probability

Lecture 20 Concept of Independence

Lecture 21 Total Probability

Section 6: Basics of Statistics Random Variables

Lecture 22 Random Variable

Lecture 23 Probability Distribution

Lecture 24 Cumulative Probability Distribution

Section 7: Basics of Statistics Distributions

Lecture 25 Bernoulli Distribution

Lecture 26 Gaussian Distribution

Lecture 27 Geometric Distribution

Lecture 28 Continuous and Normal Distribution

Section 8: Matrix Algebra

Lecture 29 Mathematical Expression and Computation

Lecture 30 Transpose of Matrix

Lecture 31 Properties of Matrix

Lecture 32 Determinants

Section 9: Hypothesis Testing

Lecture 33 Error Types

Lecture 34 Critical Value Approach

Lecture 35 Right and Left Sided Critical Approach

Lecture 36 P-Value Approach

Lecture 37 P-Value Approach Continues

Lecture 38 Hypothesis Testing

Lecture 39 Left Tail Test

Lecture 40 Two Tail Test

Lecture 41 Confidence Interval

Lecture 42 Example of Confidence Interval

Section 10: Hypothesis Tests-Types

Lecture 43 Normal and Non Normal Distribution

Lecture 44 Normality Test

Lecture 45 Normality Test Continues

Lecture 46 Determining the Transformation

Lecture 47 T-Test

Lecture 48 T-Test Continue

Lecture 49 More on T-Test

Lecture 50 Test of Independence

Lecture 51 Example of Test of Independence

Lecture 52 Goodness of Fit Test

Lecture 53 Example of Goodness of Fit Test

Section 11: Regression

Lecture 54 Co-Variance

Lecture 55 Co-Variance Continues

Beginners who want to learn Data Science and Machine Learning,Practitioners and experts who want to get a refresher of the maths for machine learning