Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2020-02-24T15:29:34.953Z
Answers.LastActivityDate: 2020-02-24T18:01:24.230Z
Answers.LastEditDate: 2020-02-24T18:01:24.230Z
Answers.UserDisplayName: anurag
Answers.Body: <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>
  
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Answers.CreationDate: 2017-05-10T07:54:42.420Z
Answers.LastActivityDate: 2017-05-10T07:54:42.420Z
Answers.UserDisplayName: parth
Answers.Body: <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>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2017-05-09T11:11:36.363Z
Answers.LastActivityDate: 2019-06-02T16:25:34.160Z
Answers.LastEditDate: 2019-06-02T16:25:34.160Z
Answers.UserDisplayName: SmallChess
Body: <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>
  
Tags: <machine-learning><math>
Title: How can I start learning mathematics for machine learning?
UserDisplayName: Anko6
ThreadText: # 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
  
  ---
  
  ## 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.
  
  
  ---
  
  