This specialization 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 specialization will set you up to define, train, and maintain a successful machine learning application.
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This specialization 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 specialization will set you up to define, train, and maintain a successful machine learning application.
This specialization 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 specialization will set you up to define, train, and maintain a successful machine learning application.
After completing all four courses, you will have gone through the entire process of building a machine learning project. You will be able to clearly define a machine learning problem, identify appropriate data, train a classification algorithm, improve your results, and deploy it in the real world. You will also be able to anticipate and mitigate common pitfalls in applied machine learning.
What's inside
Four courses
Introduction to Applied Machine Learning
Introduction to Applied Machine Learning
Machine Learning Algorithms: Supervised Learning Tip to Tail
Machine Learning Algorithms: Supervised Learning Tip to Tail
Data for Machine Learning
Data for Machine Learning
Optimizing Machine Learning Performance
Optimizing Machine Learning Performance
Learning objectives
- Clearly define an ml problem
- Survey available data resources and identify potential ml applications
- Prepare data for effective ml applications
- Take a business need and turn it into a machine learning application
- Clearly define an ml problem
- Survey available data resources and identify potential ml applications
- Prepare data for effective ml applications
- Take a business need and turn it into a machine learning application
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