Applied Statistics in Python for Machine Learning Engineers 4.2 (5 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
What you’ll learn
- You’ll learn how to apply statistical techniques to your data
- You’ll understand the role that statistics plays in applied machine learning
- You’ll learn the vernacular of statistics specific to machine learning
- You’ll be able to answer interview questions about statistics for machine learning engineering interviews
- A basic background in mathmatics
- An understanding of machine learning
- Experience using Python in machine learning
The machine learning engineer is the single most in-demand job on earth, according to top job board indeed.
My name is Mike West and I’m a machine learning engineer in the applied space. I’ve worked or consulted with over 50 companies and just finished a project with Microsoft. I’ve published over 50 courses and this is 51 on Udemy. If you’re interested in learning what the real-world is really like then you’re in good hands.
A machine learning engineer cannot be eﬀective without an understanding of basic statistical concepts and statistics methods, and an eﬀective practitioner cannot excel without being aware of and leveraging the terminology and methods used in the sister ﬁeld of statistical learning.
Developers don’t know statistics and this is a big problem. Programmers don’t need to know and use statistical methods in order to develop software. Software engineering and computer science courses generally don’t include courses on statistics, let alone advanced statistical tests.
The machine learning practitioner has a tradition of algorithms and a pragmatic focus on results and model skill above other concerns such as model interpretability. Statisticians work on much the same type of modeling problems under