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:
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:
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.
Module 2: Linear Regression
Learn all about linear regression.
Module 3: Scaling and pipelines
Learn how to bring inputs in different units (e.g., phone number and age) to the same scale.
Module 4: Regularization
Learn a technique to limit what a model can learn and prevent overfitting.
Module 5: Validation and testing
Learn the most important machine learning skill: how to be confident your model works
Module 6: Common mistakes
Learn from horror stories of data scientists and how to avoid common mistakes!
Module 7: Classification - Part 1: logistic model
Learn how to classify digits in images with a logistic model
Module 8: Classification - Part 1: Maximum Likelihood Estimation
Learn how to build a loss function for the classification task.
Module 9: Classification - Part 3: Gradient Descent
Learn how to train a logistic model
Module 10: Classification Metrics and Class Imbalance
All about binary classification with skewed datasets
Module 11: Neural Networks
Learn all about machine learning's most publicized model family.
Content released in April 2022.
Module 12: Tree-Based Models
Learn the latest and most popular family of machine learning models.
Content released in April 2022.
Module 13: Non-Parametric models
Learn about models that don't follow the usual 3-step recipe.
Content released in April 2022.
Module 14: Unsupervised Classification
Learn how to use machine learning to find regularities in data.
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!
Bootcamp
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
Value: Start a $100,000+ career
Today's price
$49
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’s 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
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.
No. While some math is always helpful, the course doesn't require heavy mathematical knowledge.
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.
Yes. The course can be followed by beginners.
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.
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.
