Answers.Body: <p>I do not have a definite answer for your question, but one way to validate your ML knowledge is to attempt certain problems that are asked in ML interviews for jobs. It might be a good starting point although it will not cover all the relevant concepts. Two such resources are</p>
  <ul>
  <li>The <a href="https://huyenchip.com/ml-interviews-book/contents/part-ii.-questions.html" rel="nofollow noreferrer">questions</a> section in Chip Huyen's ML Interviews book</li>
  <li>A <a href="https://arxiv.org/abs/2201.00650" rel="nofollow noreferrer">Deep Learning Interviews</a> book  that contains many questions testing your ML and DL knowledge.</li>
  </ul>
  <p>If you are looking to solve more theory questions, you can work through the exercises of Bishop's <a href="https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf" rel="nofollow noreferrer">Pattern Recognition and Machine Learning</a> book, although I am not sure if the solutions are available online.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-28T03:36:36.380Z
Answers.LastActivityDate: 2022-07-28T03:36:36.380Z
Answers.UserDisplayName: Ganesh Tata
Answers.Body: <ol>
  <li><p>Find a high quality and well recognized course, you can pay extra for a Certificate:</p>
  <ul>
  <li><p><a href="https://ocw.mit.edu/search/?t=Artificial+Intelligence" rel="nofollow noreferrer">https://ocw.mit.edu/search/?t=Artificial+Intelligence</a></p>
  </li>
  <li><p><a href="https://www.edx.org/course/the-analytics-edge" rel="nofollow noreferrer">https://www.edx.org/course/the-analytics-edge</a></p>
  </li>
  </ul>
  </li>
  <li><p>Try answering some of these questions, network with others:</p>
  <ul>
  <li><a href="https://mathoverflow.net/search?q=views%3A5000..+answers%3A0+closed%3A0">https://mathoverflow.net/</a></li>
  </ul>
  </li>
  <li><p>Write a few papers, publish, and try to obtain an <a href="https://arxiv.org/help/endorsement" rel="nofollow noreferrer">endorsement</a>.</p>
  </li>
  </ol>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2022-07-28T22:48:43.490Z
Answers.LastActivityDate: 2022-07-28T22:48:43.490Z
Answers.UserDisplayName: Rob
Body: <p>So I've been doing ML for ~2 years in industry, I'm a BSc in applied math, finished several courses on ML/DL on coursera, read some specific topics in ML/DL books. Seem to be in the know, more or less.</p>
  <p>But the catch is: I've never <em>really</em> validated my knowledge, as in taken an exam or certification. Some time has passed since I read theory and I sort of think that I'm losing it (especially since all I did was time-series forecasting).</p>
  <p>What are the possible ways to validate that I actually understand stuff? Do I just text random ML experts &quot;hey could I tell you stuff and you say whether that's bs or not&quot;?..</p>
  
Tags: <machine-learning><deep-learning><education>
Title: How to validate my knowledge of models implementation, pros and cons and area of applicability?
UserDisplayName: Roman Nozdrin
ThreadText: # How to validate my knowledge of models implementation, pros and cons and area of applicability?
  
  (Asked by: username_0 on 2022-07-27)
  
  So I've been doing ML for ~2 years in industry, I'm a BSc in applied math, finished several courses on ML/DL on coursera, read some specific topics in ML/DL books. Seem to be in the know, more or less.
  
  But the catch is: I've never *really* validated my knowledge, as in taken an exam or certification. Some time has passed since I read theory and I sort of think that I'm losing it (especially since all I did was time-series forecasting).
  
  What are the possible ways to validate that I actually understand stuff? Do I just text random ML experts "hey could I tell you stuff and you say whether that's bs or not"?..
  
  **Tags:** machine-learning, deep-learning, education
  
  ---
  
  ## 2 answers
  
  ### Answer by username_1 on 2022-07-28. Score: 1
  
  I do not have a definite answer for your question, but one way to validate your ML knowledge is to attempt certain problems that are asked in ML interviews for jobs. It might be a good starting point although it will not cover all the relevant concepts. Two such resources are
  
  * The [questions](https://huyenchip.com/ml-interviews-book/contents/part-ii.-questions.html) section in Chip Huyen's ML Interviews book
  * A [Deep Learning Interviews](https://arxiv.org/abs/2201.00650) book that contains many questions testing your ML and DL knowledge.
  
  If you are looking to solve more theory questions, you can work through the exercises of Bishop's [Pattern Recognition and Machine Learning](https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf) book, although I am not sure if the solutions are available online.
  
  
  ---
  
  ### Answer by username_2 on 2022-07-28. Score: 0
  
  1. Find a high quality and well recognized course, you can pay extra for a Certificate:
  
     * <https://ocw.mit.edu/search/?t=Artificial+Intelligence>
     * <https://www.edx.org/course/the-analytics-edge>
  2. Try answering some of these questions, network with others:
  
     * [https://mathoverflow.net/](https://mathoverflow.net/search?q=views%3A5000..+answers%3A0+closed%3A0)
  3. Write a few papers, publish, and try to obtain an [endorsement](https://arxiv.org/help/endorsement).
  
  
  ---
  
  