# Mathematics for Machine Learning

## Multivariate Calculus

This course is a part of
**Mathematics for Machine Learning**, a 3-course Specialization series from Coursera.

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## &

Rating | 4.6★ based on 211 ratings |
---|---|

Length | 7 weeks |

Effort | 6 weeks of study, 2-5 hours/week |

Starts | Jan 20 (3 days ago) |

Cost | $49 |

From | Imperial College London via Coursera |

Instructors | Samuel J. Cooper, David Dye, A. Freddie Page |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Programming Mathematics |

Tags | Computer Science Algorithms Math And Logic |

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## What people are saying

According to other learners, here's what you need to know

**machine learning**
in 32 reviews

a very good explanation of the required calculus basics for machine learning.

The calculation part are done by python code, which lays a foundation for further machine learning course and shows how the mathematical concepts are used in practice.

Great stuff.Thanks A quick short introduction to multivariate calculus and few machine learning techniques, but without much detail mathematical proofs for some ideas.

Just a great course for getting you ready to understand machine learning algorithms.

Everyone should take this course before jumping into machine learning algorithms and applications.

The course is a great introduction to how one can translate pre-learned mathematical concepts into machine learning.

Very good course on basic mathematics for machine learning.

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**multivariate calculus**
in 19 reviews

Builds up logically from a soft introduction to practical applications of multivariate calculus for data analytics.

Excellent course!I studied multivariate calculus during engineering.

excellent Review course for multivariate calculus and basic optimization method used for curve fitting.

Quality course that will leave you feeling confident in multivariate calculus and analytics.

It us good course and gave me basic understanding of multivariate calculus.

This was a great course for learning multivariate calculus required for Machine Learning.

very good introductory course to Multivariate Calculus Well the course is generally good, the only problem is that David sometimes may just skip the process and lack more explanation when performing the calculation, it's easy to lose track of what he is calculating if not reviewing the video over and over again, but anyway, the whole class is worth recommendation, thank you for your teaching, professors Very practical introduction to the subject, very well paced!

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**linear algebra**
in 9 reviews

the basic concepts are explained clearly, but the step of the lecture became more fast than the course of linear algebra.

Just like the linear algebra one this course is absolutely awesome.

A bit more challenging than the Linear Algebra course.

I have leant single variable calculus and linear algebra (freshman year difficulty), and this course is challenging but doable for me!

But it would be much better if the concept in linear algebra combines more with this course.

This is way better than the linear algebra course in this specialization.

Highly recommend Following on from the Linear Algebra course, this is equally excellent.

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**imperial college**
in 5 reviews

Another fantastic course by Imperial College.

Well done and kudos to Imperial College for taking the initiative.

If there are more of neural network course that Imperial College can come up would be best.

one of the best courses I have ever had.thanks to instractors and Imperial College Londonthanks so much for that specilization it helped me alot Fantastic overview/refresher of multivariate calculus, with links to ML / neural nets throughout.

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**partial derivatives**
in 3 reviews

I no longer feel intimidated when I look at an expression involving higher order partial derivatives in multiple variables!

exercises on partial derivatives need to be focused more on various aspects of partial differentiation rather than on taking partial derivatives of some complicated functions.

Again, the main enjoyment comes from seeing techniques learnt at school (partial derivatives, Taylor series, Newton-Raphson, etc) actually being used in practice.

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## Careers

An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k

RESEARCH SCIENTIST (MACHINE LEARNING) $147k

Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

## Write a review

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## &

Rating | 4.6★ based on 211 ratings |
---|---|

Length | 7 weeks |

Effort | 6 weeks of study, 2-5 hours/week |

Starts | Jan 20 (3 days ago) |

Cost | $49 |

From | Imperial College London via Coursera |

Instructors | Samuel J. Cooper, David Dye, A. Freddie Page |

Download Videos | On all desktop and mobile devices |

Language | English |

Subjects | Programming Mathematics |

Tags | Computer Science Algorithms Math And Logic |

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