by Eduonix | Released July 2018 | ISBN: 9781789613209

Learn the core mathematical concepts for machine learning and learn to implement them in R and PythonAbout This VideoLinear algebra notation is used in machine learning to describe the parameters and structure of different machine learning algorithms.Multivariate Calculus – This is used to supplement the learning part of machine learning.Probability Theory – The theories are used to make assumptions about the underlying data when we are designing these deep learning or AI algorithms.In DetailArtificial 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. Show and hide more Publisher Resources Download Example Code

1. Chapter 1 : Introduction
   • Introduction 00:03:53
2. Chapter 2 : Linear Algebra
   • Scalars, Vectors, Matrices, and Tensors 00:21:15
   • Vector and Matrix Norms 00:09:35
   • Vectors, Matrices, and Tensors in Python 00:21:28
   • Special Matrices and Vectors 00:13:35
   • Eigenvalues and Eigenvectors 00:11:42
   • Norms and Eigendecomposition 00:28:21
3. Chapter 3 : Multivariate Calculus
   • Introduction to Derivatives 00:19:24
   • Basics of Integration 00:11:09
   • Gradients 00:12:05
   • Gradient Visualization 00:18:50
   • Optimization 00:18:44
4. Chapter 4 : Probability Theory
   • Intro to Probability Theory 00:11:01
   • Probability Distributions 00:10:13
   • Expectation, Variance, and Covariance 00:11:23
   • Graphing Probability Distributions in R 00:12:32
   • Covariance Matrices in R 00:09:50
5. Chapter 5 : Probability Theory
   • Special Random Variables 00:10:52
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