用于机器学习的数据

课程概况

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to:

Understand the critical elements of data in the learning, training and operation phases
Understand biases and sources of data
Implement techniques to improve the generality of your model
Explain the consequences of overfitting and identify mitigation measures
Implement appropriate test and validation measures.
Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering.
Explore the impact of the algorithm parameters on model strength

To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode).

This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

课程大纲

What Does Good Data look like?

We all know that data is important for machine learning success, but what does it really look like? What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation.

Preparing your Data for Machine Learning Success

Now that you have your data sources identified, you need to bring it all together. This week describes what you need to prepare data overall.

Feature Engineering for MORE Fun & Profit

Data is particular to a problem. This week we'll discuss how to turn generic data into successful fuel for specific machine learning projects.

Bad Data

There are so many ways data can go wrong! This week discussed some of the pitfalls in data identification and processing.