✅ Discover what gradient descent is and how it can be applied to logistic regression.
✅ Explore vectors and matrices and find out how they can help with linear regression problems. ✅ Familiarize yourself with optimization problems and figure out what derivatives have to do with them. ✅ Find out how probability is related to linear regression and classification tasks. ✅ Learn the essentials of sets and numerical functions. You will gain a better understanding of regression tasks that play an important part in machine learning. This track will help you discover what is going on under the hood of machine learning, from linear regression to gradient descent. It breaks down complicated mathematical concepts into easy-to-read theory followed by practical tasks. Introducing Math for Machine Learning on JetBrains Academy! This track will build the basic foundation required for ML model development. 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.
It increases our productivity, improves service quality, and can even entertain us by recommending the movies we are likely to enjoy. It helps us better detect malware and insider threats by continuously analyzing data to find patterns. Machine learning (ML) is everywhere these days.
0 kommentar(er)
