Math for Machine Learning on JetBrains Academy
Machine learning (ML) is everywhere these days. It helps us better detect malware and insider threats by continuously analyzing data to find patterns. It increases our productivity, improves service quality, and can even entertain us by recommending the movies we are likely to enjoy. ML capabilities have pushed a lot of people to start exploring machine learning, but writing algorithms and programs for ML isn’t easy and requires significant mathematical knowledge.
Introducing Math for Machine Learning on JetBrains Academy! This track will build the basic foundation required for ML model development. It breaks down complicated mathematical concepts into easy-to-read theory followed by practical tasks.
What you’ll learn
This track will help you discover what is going on under the hood of machine learning, from linear regression to gradient descent. You will gain a better understanding of regression tasks that play an important part in machine learning. In addition, you will:
✅ Learn the essentials of sets and numerical functions.
✅ Find out how probability is related to linear regression and classification tasks.
✅ Familiarize yourself with optimization problems and figure out what derivatives have to do with them.
✅ Explore vectors and matrices and find out how they can help with linear regression problems.
✅ Discover what gradient descent is and how it can be applied to logistic regression.
The track currently contains no projects, but it provides helpful educational theory followed by lots of practical exercises of varying complexity.
There are currently two machine learning tracks on JetBrains Academy: Math for Machine Learning and Introductory Machine Learning in Python. We recommend you complete them both (in no particular order) to gain a deep understanding of machine learning and the skills it takes to be an ML expert.
The former track focuses on ML mathematical concepts while the latter is here to give you a clear understanding of the main types of ML algorithms and offer practical experience with such Python libraries as NumPy, pandas, and scikit-learn.
Upon completing the track, you’ll receive a personalized certificate. You can download it at any time from your JetBrains Academy profile. It can also be added to your resume or LinkedIn profile.
A career to pursue after track graduation
If you’re interested in starting your career as a Junior ML engineer, this track will be right up your alley! Add some knowledge of statistics, data visualization techniques, and SQL, and you can try your hand at working as a Data Analyst or Data Scientist.
We hope you’ll enjoy studying machine learning with us. If you have any questions or would like to share your feedback, feel free to leave a comment below or contact us at email@example.com.
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