Crack the Machine Learning Interview and Get Your Data Science Dream Job

This course will teach you the complete foundations of machine learning that you need to get a job (and do a great job afterward)

After taking this course you will:

  • Pass job interviews and technical quizzes
  • Avoid rookie mistakes that waste companies' time and money
  • Be prepared for real work.

How it works

Step 1

Watch the video lessons and do the exercises at your own pace.

Step 2

Ask questions and get support anytime you want.

Step 3

Take the final exam.

Step 4

Obtain your certificate!

Important stuff about this course:

  • You won't spend hours learning stuff that never comes up in a job interview
  • Total beginners are welcome; coding experience or advanced math knowledge are not required.

And also...

The course was designed by an industry expert who's been on the hiring side of the table and knows what companies are looking for.

What's included

Video lessons and written course notes (see full content below).

Coding exercises directly in your browser and  practice quizzes to test your knowledge.

Direct access to the instructor and other students through a private community.

Final exam and certificate.

Here's what you'll learn:

Module 1: Machine Learning Models

Understand the challenge behind building models through the example of modeling an epidemic.

  • The 3-step machine learning recipe
  • Overfitting, underfitting, bias, variance
  • Can computers learn "without human knowledge"?
  • 8 videos + Notes
  • 1 exercise
  • 1 quiz

Module 2: Linear Regression

Learn all about linear regression.

  • Introduction to sample dataset and task
  • How to train a linear regression
  • MSE and R-squared
  • 11 videos + Notes
  • 5-minute code assignment
  • 1 quiz
  • Optional math bonus

Module 3: Scaling and pipelines

Learn how to bring inputs in different units (e.g., phone number and age) to the same scale.

  • What does the IQ have to do with scaling?
  • Min-max and standard scaling
  • How to organize code in pipelines
  • 11 videos + Notes
  • 5-minute code assignment
  • 1 quiz
  • Optional math bonus

Module 4: Regularization

Learn a technique to limit what a model can learn and prevent overfitting.

  • L1 and L2 regularization
  • Why does L1 tend to eliminate features?
  • L1 vs L2: Which one is best?
  • 9 videos + Notes
  • 5-minute code assignment
  • 1 quiz

Module 5: Validation and testing

Learn the most important machine learning skill: how to be confident your model works

  • Splitting data in trainin/validation/test
  • Model selection
  • Cross-validation
  • 21 videos + Notes
  • 30-minute code assignment
  • 2 reading bonuses

Module 6: Common mistakes

Learn from horror stories of data scientists and how to avoid common mistakes!

  • Data leakage
  • Look-ahead bias
  • The Golden Rule to avoid validation mistakes
  • 17 videos + Notes
  • 10-minute code assignment (find the error!)
  • 1 quiz

Module 7: Classification - Part 1: logistic model

Learn how to classify digits in images with a logistic model

  • The 3-step ML recipe applied to a logistic model
  • Binary and multi-class classification
  • Decision functions and boundaries
  • 11 videos + Notes
  • 5-minute code assignment 
  • 1 quiz

Module 8: Classification - Part 1: Maximum Likelihood Estimation

Learn how to build a loss function for the classification task.

  • Likelihood function and maximum likelihood estimation
  • Numerical stability
  • Cross-entropy
  • 8 videos + Notes
  • 1 quiz

Module 9: Classification - Part 3: Gradient Descent

Learn how to train a logistic model

  • The fog analogy to understand gradient descent 
  • Numerical vs analytical gradient calculation
  • Beyond vanilla gradient descent
  • 15 videos + Notes
  • 20-minute code assignment 
  • 1 quiz

Module 10: Classification Metrics and Class Imbalance

All about binary classification with skewed datasets

  • Accuracy, precision, recall, sensitivity, specificity
  • Which metric should you use?
  • Do you really have to tackle class imbalance?
  • 14 videos + Notes
  • 1 quiz

Module 11: Neural Networks

Learn all about machine learning's most publicized model family.

  • Neural nets for classification and regression
  • Stochastic gradient descent
  • Neural nets vs deep learning

Content released in April 2022.

Module 12: Tree-Based Models

Learn the latest and most popular family of machine learning models.

  • Decision trees
  • Random forest and gradient-boosted trees
  • XGBoost and why it's so popular

Content released in April 2022.

Module 13: Non-Parametric models

Learn about models that don't follow the usual 3-step recipe.

  • K-nearest neighbor classification
  • Recommendation systems (such as recommended movies on Netflix)
  • Support vector machines with kernels

Content released in April 2022.

Module 14: Unsupervised Classification

Learn how to use machine learning to find regularities in data.

  • Clustering with K-means
  • Principal component analysis
  • Finding word-vector representations for natural language processing

Content released in April 2022.

Academic courses vs. bootcamps vs. this course

Academic course

Your teacher spends hours speaking about calculus and linear algebra, but then none of that comes up in a job interview!


You learn how to use many tools but not how they work under the hood. This black-box knowledge is what companies want to avoid the most in applicants!

This course

You gain foundational knowledge and truly understand machine learning. You learn from those who've done the job before and have hired others.

Your expertise in machine learning starts here

Here's the deal

  • Unlimited access to the course
  • Direct access to instructor
  • Video lessons, exercises, quizzes and notes
  • 100% satisfaction guarantee (no risk at all!)

Value: Start a $100,000+ career

Today's price


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100% satisfaction guaranteed

This course will be of great help if you are:

A student who wants to prepare for work in data science after graduating.

An established professional or academic who wants to switch careers to data science.

A total beginner who wants to dabble in machine learning and data science for the first time.

Emmanuel Maggiori, PhD

Founder of Computing School

Meet the Instructor

Emmanuel, PhD, is a computer scientist and AI expert. He runs his consulting business on data science and AI. He has worked with companies like Expedia, Vodafone, TUI fly, the French Space Agency and dozens of startups. He personally wrote some of Expedia‚Äôs AI-powered software to price hotel rooms, used by millions of travelers every day. During his past academic career he was one of the pioneers in using deep learning to automatically analyze the content of satellite images. He's passionate about teaching. In the past, he's given lectures on logic, theoretical computer science, machine learning, deep learning, and more. 

What students are saying


Iris, Master's student from Singapore

Nhan Nguyen Ba, Senior Software Engineer at TymeBank

Frequently asked questions

Is it self-paced?

Yes. You can watch the lessons and ask questions whenever you want. It usually takes 3 months to complete if you dedicate 4 hours per week to your training.

Does it require advanced math knowledge?

No. While some math is always helpful, the course doesn't require heavy mathematical knowledge.

Do I need to know how to code?

No. There are some coding exercises but they are mostly templates where you need to modify existing code. This isn't a coding course, which we think should be learned separately so we can focus on the foundations of machine learning. However, you will probably need to learn Python at some point to get a job in data science.

I'm a total beginner. Is that okay?

Yes. The course can be followed by beginners.

Do I need a PhD to break into data science?

No. Recruiters mostly look for people with valuable work experience in the field. The best way to stand out is to show you've created machine learning models that have been deployed in production systems, used by real users and made an impact in the business.

What if I'm not satisfied with the program?

You will be. But if for any reason you regret your purchase, you can get your money back. Just send an email within 30 days to hello@computingschool.com asking for a refund.

Would you like to try it first?

No problem! You can access the course platform for free and preview the first two modules. No credit card required.

100% Satisfaction Guarantee

This course is designed to give you much more value that you expect. But if for any reason you aren't satisfied, just send an email within 30 days of your purchase to hello@computingschool.com and you'll get a refund.

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