Welcome to Feature Engineering for Machine Learning, the most extensive course on feature engineering readily available online
In this course, you will discover how to craft functions and build more effective machine discovering models.
Who is this course for?
So, you’ve made your initial steps into information science, you understand the most frequently utilized prediction designs, you probably built a linear regression or a classification tree model. At this stage you’re most likely beginning to experience some challenges – you recognize that your information set is dirty, there are great deals of values missing out on, some variables include labels instead of numbers, others do not fulfill the presumptions of the designs, and on top of whatever you question whether this is properly to code things up. And to make things more complex, you can’t find many combined resources about feature engineering. Perhaps only blogs? You may start to wonder: how are things truly done in tech business?
This course will help you! This is the most comprehensive online course in variable engineering You will learn a substantial variety of engineering strategies utilized worldwide in various companies and in information science competitions, to clean and change your data and variables.
What will you find out?
I have created a wonderful collection of feature engineering techniques, based on scientific articles, white documents, data science competitions, and obviously my own experience as an information scientist.
Particularly, you will find out:
How to assign your missing out on data
How to encode your categorical variables
How to change your numerical variables so they satisfy ML design presumptions
How to transform your mathematical variables into discrete periods
How to eliminate outliers
How to manage date and time variables
How to work with different time zones
How to manage mixed variables which consist of strings and numbers
Throughout the course, you are going to find out numerous techniques for each of the pointed out jobs, and you will find out to implement these methods in an elegant, effective, and professional way, utilizing Python, NumPy, Scikit-learn, pandas and a special open-source package that I developed particularly for this course: Function- engine.
At the end of the course, you will be able to implement all your function engineering actions in a single and stylish pipeline, which will enable you to put your predictive models into production with maximum performance.
Need to know more? Continue reading …
In this course, you will at first end up being acquainted with the most extensively utilized techniques for variable engineering, followed by more advanced and customized techniques, which record info while encoding or changing your variables. You will likewise find in-depth descriptions of the different strategies, their benefits, constraints and underlying presumptions and the very best programs practices to implement them in Python.
This extensive feature engineering course consists of over 100 lectures covering about 10 hours of video, and ALL topics include hands-on Python code examples which you can use for reference and for practice, and re-use in your own tasks
In addition, the code is updated regularly to stay up to date with brand-new patterns and brand-new Python library releases.
So what are you waiting for? Register today, accept the power of function engineering and build much better artificial intelligence designs.