<p>Some of the fundamental mathematical concepts required in ML field are as follows:</p>

<ul>
<li>Linear Algebra </li>
<li>Analytic Geometry </li>
<li>Matrix Decompositions </li>
<li>Vector Calculus </li>
<li>Probability and Distribution </li>
<li>Continuous Optimization</li>
</ul>

<p>A very recent book availble at <a href="https://mml-book.github.io/" rel="nofollow noreferrer">Mathematics for Machine Learning</a> covers all these aspects and more.</p>

CC BY-SA 4.0
2020-02-24T15:29:34.953Z
2020-02-24T18:01:24.230Z
2020-02-24T18:01:24.230Z
anurag
<p>If you are interested to deepen your statistical concepts before diving into machine learning, i would recommend <strong>Introduction to Statistics: Descriptive Statistics</strong> course in <strong>edX</strong> </p>

<p>where you'll learn</p>

<ul>
<li>The fundamental concepts and methods of statistics</li>
<li>How to intepret graphical and numerical summaries of data</li>
<li>Understand the reasoning behind the calculations, the assumptions under which    they are valid, and the correct interpretation of results</li>
</ul>

<p>The link for course is <a href="https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x" rel="nofollow noreferrer">edX</a></p>

<p>This will definitely clarify your stat background with added benefit of certification.</p>

CC BY-SA 3.0
2017-05-10T07:54:42.420Z
2017-05-10T07:54:42.420Z
parth
<p>You should begin from Dr Andrew Ng machine learning course on Coursera. It's probably the most popular course for newcomers in machine learning. It's a free course.</p>

<p>You should also grab "Elements of Statistical Learning" ebook PDF. It's a free book.</p>

<p>You may want to focus on:</p>

<ol>
<li>Regression</li>
<li>Cross validation</li>
<li>Bias-variance tradeoff</li>
<li>Decision surface</li>
<li>Gradient descent</li>
</ol>

<p>And more...</p>

CC BY-SA 4.0
2017-05-09T11:11:36.363Z
2019-06-02T16:25:34.160Z
2019-06-02T16:25:34.160Z
SmallChess
<p>I am an Android programmer. Now, I would like to learn machine learning. I know it requires a mathematical background, like statistics, probability, calculus and linear algebra. However, I am a bit lost. Where should I start from? Can someone provide me a road map for how to learn the mathematical background required for machine learning?</p>

<machine-learning><math>
How can I start learning mathematics for machine learning?
Anko6
# How can I start learning mathematics for machine learning?

(Asked by: username_0 on 2017-05-09)

I am an Android programmer. Now, I would like to learn machine learning. I know it requires a mathematical background, like statistics, probability, calculus and linear algebra. However, I am a bit lost. Where should I start from? Can someone provide me a road map for how to learn the mathematical background required for machine learning?

**Tags:** machine-learning, math


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## 3 answers

### Answer by username_3 on 2017-05-09. Score: 5 | Accepted answer

You should begin from Dr Andrew Ng machine learning course on Coursera. It's probably the most popular course for newcomers in machine learning. It's a free course.

You should also grab "Elements of Statistical Learning" ebook PDF. It's a free book.

You may want to focus on:

1. Regression
2. Cross validation
3. Bias-variance tradeoff
4. Decision surface
5. Gradient descent

And more...


---

### Answer by username_1 on 2020-02-24. Score: 2

Some of the fundamental mathematical concepts required in ML field are as follows:

* Linear Algebra
* Analytic Geometry
* Matrix Decompositions
* Vector Calculus
* Probability and Distribution
* Continuous Optimization

A very recent book availble at [Mathematics for Machine Learning](https://mml-book.github.io/) covers all these aspects and more.


---

### Answer by username_2 on 2017-05-10. Score: 1

If you are interested to deepen your statistical concepts before diving into machine learning, i would recommend **Introduction to Statistics: Descriptive Statistics** course in **edX**

where you'll learn

* The fundamental concepts and methods of statistics
* How to intepret graphical and numerical summaries of data
* Understand the reasoning behind the calculations, the assumptions under which they are valid, and the correct interpretation of results

The link for course is [edX](https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x)

This will definitely clarify your stat background with added benefit of certification.


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