**Mathematical Foundation for AI and Machine Learning**

English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 4h 15m | 1.81 GB

Learn the core mathematical concepts for machine learning and learn to implement them in R and Python

Artificial Intelligence has gained importance in the last decade with a lot depending on the development and integration of AI in our daily lives. The progress that AI has already made is astounding with innovations like self-driving cars, medical diagnosis and even beating humans at strategy games like Go and Chess. 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 plays an important role as it builds the foundation for programming for these two streams. And in this course, we’ve covered exactly that. We designed a complete course to help you master the mathematical foundation required for writing programs and algorithms for AI and ML.

The course has been designed in collaboration with industry experts to help you breakdown the difficult mathematical concepts known to man into easier to understand concepts.The course covers three main mathematical theories: Linear Algebra, Multivariate Calculus and Probability Theory.

What You Will Learn

- Refresh the mathematical concepts for AI and Machine Learning
- Learn to implement algorithms in Python
- Understand the how the concepts extend for real-world ML problems

**Table of Contents**

**Introduction**

1 Introduction

**Linear Algebra**

2 Scalars, Vectors, Matrices, and Tensors

3 Vector and Matrix Norms

4 Vectors, Matrices, and Tensors in Python

5 Special Matrices and Vectors

6 Eigenvalues and Eigenvectors

7 Norms and Eigendecomposition

**Multivariate Calculus**

8 Introduction to Derivatives

9 Basics of Integration

10 Gradients

11 Gradient Visualization

12 Optimization

**Probability Theory**

13 Intro to Probability Theory

14 Probability Distributions

15 Expectation, Variance, and Covariance

16 Graphing Probability Distributions in R

17 Covariance Matrices in R

**Probability Theory**

18 Special Random Variables

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