This course is for professionals who have heard the buzz around machine learning and want to apply machine learning to data analysis and automation. Whether finance, medicine, engineering, business or other domains, this course will introduce you to problem definition and data preparation in a machine learning project. By the end of the course, you will be able to clearly define a machine learning problem using two approaches. You will learn to survey available data resources and identify potential ML applications. You will learn to take a business need and turn it into a machine learning application. You will prepare data for effective machine learning applications. This is the first course of the Applied Machine Learning Specialization brought to you by 糖心vlog官网观看 and the Alberta Machine Intelligence Institute.
Sources of Training Data
Skills You'll Learn
Algorithms, Data Analysis, Human Learning, Applied Machine Learning, Machine Learning Algorithms, Machine Learning
Reviews
4.7 (738 ratings)
- 5 stars74.39%
- 4 stars20.18%
- 3 stars4.47%
- 2 stars0.27%
- 1 star0.67%
AS
Jul 12, 2021
This is course is truly amazing for the people who want to know about the things that need to be considered when making a QUAM or an ML system. Looking forward to knowing more about ML!
BW
Nov 13, 2020
An excellent introduction to the mechanics of ML. Not so deep that coding is required, but simultaneously not so high-level as to be abstract. A very nice intro - thanks for this!
From the lesson
Learning Data
This week is all about data. You will learn about data acquisition and understand the various sources of training data. We'll talk about how much data you need and what pitfalls might arise, including ethical issues.
Taught By
Anna Koop
Senior Scientific Advisor