# [Coursera] Mathematics for Machine Learning Specialization

**Mathematics for Machine Learning. Learn about the prerequisite mathematics for applications in data science and machine learning**

### What you will learn

Implement mathematical concepts using real-world data

Derive PCA from a projection perspective

Understand how orthogonal projections work

Master PCA

## About this Specialization

For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics – stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science. This specialization aims to bridge that gap, getting you up to speed in the underlying mathematics, building an intuitive understanding, and relating it to Machine Learning and Data Science.

In the first course on Linear Algebra we look at what linear algebra is and how it relates to data. Then we look through what vectors and matrices are and how to work with them.

The second course, Multivariate Calculus, builds on this to look at how to optimize fitting functions to get good fits to data. It starts from introductory calculus and then uses the matrices and vectors from the first course to look at data fitting.

The third course, Dimensionality Reduction with Principal Component Analysis, uses the mathematics from the first two courses to compress high-dimensional data. This course is of intermediate difficulty and will require Python and numpy knowledge.

At the end of this specialization you will have gained the prerequisite mathematical knowledge to continue your journey and take more advanced courses in machine learning.

## Applied Learning Project

Through the assignments of this specialisation you will use the skills you have learned to produce mini-projects with Python on interactive notebooks, an easy to learn tool which will help you apply the knowledge to real world problems. For example, using linear algebra in order to calculate the page rank of a small simulated internet, applying multivariate calculus in order to train your own neural network, performing a non-linear least squares regression to fit a model to a data set, and using principal component analysis to determine the features of the MNIST digits data set.

## Description

Mathematics for Machine Learning, Specialization, name of the training video series in the field learning the skills of the machine. The training set includes a three-course video which you watch it with the science of machine learning as well will be familiar. In this course you specifically on the knowledge of mathematics in machine learning focus you will. You in the end watch all the Meetings, Courses, mathematics, the desired science data into the best possible shape, you’ll be able.

You in this period video to a good with linear algebra will be familiar. You also see the connection between linear algebra and data you will understand. In another part of this series, you with a variety of functions and how to optimize it will be familiar. This section contains introductory accounts, and then topics such as vector and Matrix can be. In the final part also, you’ll learn of the content taught to good use. Do not forget that you’re watching this course, knowledge of machine learning to the most consistent possible start, you will.

**Items that are in the training set is given:**

- Familiarity and understanding of general knowledge of machine learning
- Learning mathematical topics for use in science, data, and machine learning
- Understand and familiar with the concept of linear algebra and to understand its relationship with the data
- Familiar with all sorts of functions, such as Matrix and vectors
- Familiarity with the accounts of the multivariate in order to train the neural network
- And…

**Size: 1.66 GB **

**https://www.coursera.org/specializations/mathematics-machine-learning.**