Applied Statistics in Python for Machine Learning Engineers
An Indepth Look at Statiscs for Machine Learning Engineers in the Real-World
Updated on Sep, 2023
Language - English
"This is a review for me, as many years ago, I took several statistics courses in my doctoral program. I use these regularly, but often forget or overlook the theoretical and conceptual underpinnings. I love the clear explanations and the visualizations, plus the learning of new methods in Python and Numpy, etc. This augments what I learned in the 6 month data analytics boot camp I just graduated from. The only thing that would be helpful would be having some real data (aside from the pima data) to work with, Thanks!" - Dr. Judith Calvo
"I am new to machine learning. This course explains basic machine learning terminology that other courses skip. The step-by-step explanations put the machine learning process into perspective" - James Reynolds
The machine learning engineer is the single most in-demand job on earth, according to top job board indeed.
A machine learning engineer cannot be eï¬€ective without an understanding of basic statistical concepts and statistics methods, and an eï¬€ective practitioner cannot excel without being aware of and leveraging the terminology and methods used in the sister ï¬eld 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.
Machine learning practitioners eventually realize the need to master statistics. This might start with a need to better interpret descriptive statistics or data visualizations and may progress to the need to start using sophisticated hypothesis tests. The problem is, they don’t seek out the statistical information they need. Instead, they try to read through a text book on statistics or work through the material for an undergraduate course on statistics. This traditional approach is overly complicated, slow because it covers a breadth and depth of material on statistics that is beyond the needs of the machine learning practitioner.
In this course you'll learn applied statistics for machine learning. The course will focus on the knowledge of statistics you need for your machine learning projects. You'll be able to take what you've learned and apply it to your real-world problems.
Who this course is for:
- If you want to become a machine learning engineer then this course is for you
- If you're a programmer thinking about moving to applied machine learning then this course is for you
- If you want to improve your modeling performance the this course is for you
What will you learn in this course:
- 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
What are the prerequisites for this course?
- A basic background in mathmatics
- An understanding of machine learning
- Experience using Python in machine learning
Check out the detailed breakdown of what’s inside the course
- Introduction 01:28 01:28
- Course Overview 02:21 02:21
- Download Files
An Introduction to Statistics
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