Mathematics for Machine Learning: Multivariate Calculus
This course provides a basic understanding of multivariate calculus, which is essential to construct several standard machine learning approaches. They will start at the beginning with a review of the "rise over run" formulation of a slope before moving on to the formal definition of a function's gradient. Then they begin to put together a collection of tools that make calculus easier and faster. Next, they will learn how to compute vectors that point upwards on multidimensional surfaces, and they will use an interactive game to put what they have learned into practice.
They will look at how calculus may be used to create approximations to functions, as well as quantifying how precise such approximations should be. They also spend some time discussing where calculus is used in neural network training before showing you how it is used in linear regression models. The Multivariate Calculus is designed to provide you a basic grasp of calculus as well as the vocabulary you'll need to hunt up ideas on your own if you get stuck. Without getting into too much depth, maybe you'll have enough confidence to take some more specialized machine learning classes in the future.
- Flexible deadlines: Reset deadlines based on your availability.
- Shareable certificate: Get a Certificate when you complete
- 100% online: Start now and learn at times that suit you.
- Course 2 of 3 in the: Mathematics for Machine Learning Specialization
- Beginner level
- Approx. 6 p.m. to finish
- Subtitles: Arabic, French, Portuguese (European), Greek, Italian, Vietnamese, German, Russian, English, Spanish
SKILLS YOU WILL GAIN
- Linear Regression
- Vector Calculus
- Multivariate Calculus
- Gradient Down
Rating: 4.7/5
Enroll here: coursera.org/learn/multivariate-calculus-machine-learning
