Mathematics for Machine Learning and Data Science Specialization Launch

We’re thrilled to share that our newest specialization, Mathematics for Machine Learning and Data Science, is now available.

This specialization is jam-packed with foundational machine learning and data science skill-building and is appropriate for both beginners and advanced AI builders alike.


@giovanni.lignarolo. Sounds really interesting. I will definitely take this course soon!

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Hi team,

Could you please share the link to lecture notes (pdf) for each of the courses in the specialization: Mathematics for Machine Learning and Data Science Specialization?

Thanks much!


Hello @Jagadish_Kavuturu, the lecture notes are here!


Hey there,

I just started the course and noticed that there’s a broken link in the “intro to numpy” jupyter notebook.

The section with the broken link is the following:

Did you notice that the output of the data type of the `char_arr` array is `<U23`? 
This means that the string (`'Welcome to Math for ML!'`) is a 23-character (23) unicode string (`U`) on a little-endian architecture (`<`). You can learn more about data types [here](

And the broken link is

I get a 404 / not found error.


Hi @gdelgado, thanks for noticing this issue. We have already fixed it and now you can access the documentation. You may have to refresh your workspace to load the newest version of the notebook.


Mathematics is an essential tool for Machine Learning and Data Science, and it plays a crucial role in understanding, designing, and developing various algorithms and models in these fields. Here are some of the reasons why mathematics is important for Machine Learning and Data Science:

  1. Linear Algebra: Linear Algebra is used to represent and manipulate data in vector and matrix form, which are fundamental concepts in Machine Learning. It is used to solve systems of linear equations, perform matrix operations, and calculate eigenvalues and eigenvectors.
  2. Calculus: Calculus is used to optimize machine learning algorithms, such as gradient descent, which is used to find the optimal parameters for a given model. It is also used to calculate derivatives and integrals, which are essential for understanding and designing various machine learning models.
  3. Probability and Statistics: Probability and Statistics are used to analyze and model data, and they form the foundation of many machine learning algorithms, such as Naive Bayes, Decision Trees, and Random Forests. It is used to calculate probability distributions, statistical tests, and hypothesis testing.

Hello Lucas, can you tell me how to reset my notebook for week 2 back to the original or get a copy of the original file?

I’ve refreshed several times but just get a copy of the same notebook for the assignment with all of my work still there. Is there a link to files to just get a brand new week 2 work?

Thank you!

Hi @Tumi!

Sure! You can take a look here. The instructions are:

  • Open the notebook from the classroom.
  • After the notebook opens up, click File → Open
  • When your workspace opens, tick the check box before your notebook file. After it is selected, press Shutdown. The icon beside the filename should turn from green to gray.
  • Tick the checkbox again and this time choose Rename and enter any filename other than the original. For example, C4W1_Assignment.ipynb (original) → C4W1_Assignment_v2.ipynb
  • (Optional) Tick the checkbox of any other file that you want to get a fresh copy of (e.g. dataset files that you might have manipulated irreversibly). Then click Delete . You can also opt to Rename or Download each file individually in case you want to keep them before deleting.
  • Click on the Help button on the top right of the page.
  • Click the Get latest version button.
  • Click the Update Lab button. The page will refresh and you should now see the latest version of the notebook.

Thanks Lucas! I’m all set!