It won't be wrong to say that AI is one of the hottest topics right now, tech companies are working round the clock to get best AI for their work. Companies like Google, Apple, Baidu, etc. are investing billions of dollars in their AI programs. But before we dive into maths, algorithms and programming languages, let's have some background knowledge of AI. We'll try our best to make this article simple as possible. Well, when someone talks about AI most people think about movies like Chappie, Terminator, and Lucy, etc.

It's no secret that mathematics is the foundation of machine learning, and is vital to your understanding of the underpinnings of the field. In order to succeed as a machine learning practitioner, knowledge of the applicable mathematical foundations are absolutely necessary. Where can you turn to brush up on your machine learning maths, or strengthen your understanding by extending that base? Mathematics for Machine Learning is a book currently in development by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong, with the goal of motivating people to learn mathematical concepts, and which is set to be published by Cambridge University Press. According to the authors, the goal of the text is to provide the necessary mathematical skills to subsequently read books on more advanced machine learning topics.

If you want to fully grasp machine learning,and avoid mistakes, you'll need to be familiar with math at some level. You'll find it in papers and textbooks as well as libraries/frameworks. With a targeted approach, and the right frame of mind, you can tackle the math necessary for machine learning. If you didn't get along with math in high school then don't worry, this talk will be down-to-earth and approachable, and has been designed with you in mind. This talk will cover practical mathematical concepts featured in machine learning, presented in a very accessible, visual manner.

Learn the core mathematical concepts for machine learning and learn to implement them in R and python, Learn Why Businesses Achieving AI at Scale are Disproportionately Financial Outperformers. The integration of Artificial Intelligence is growing and multiple sectors are now looking to build technologies that include AI. With self-driving cars, smart robots, to even your coffee machines, AI has become a prominent technology that cannot be overlooked. Writing algorithms for AI and Machine Learning is difficult and requires extensive programming and mathematical knowledge. While these algorithms have the potential to solve a number of difficult problems that are currently plaguing the world, designing these algorithms to solve these problems requires intricate mathematical skills and experience.

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts.