Learn the core mathematical concepts for machine learning and learn to implement them in R and Python
Key Features
- - Linear 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.
Book Description
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.
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
Who this book is for
Any one who wants to refresh or learn the mathematical tools required for AI and machine learning will find this course very useful
