Feature Selection for Machine Learning

Feature Selection for Machine Learning

https://www.udemy.com/course/feature-selection-for-machine-learning/

上次更新 10/2020



英语 [自动]



12 个章节 • 69 个讲座 • 总时长 4 小时 28 分钟

From beginner to advanced







你将会学到的



Understand different methods of feature selection



Implement different methods of feature selection



Reduce feature space in a dataset



Build simpler, faster and more reliable machine learning models



Analyse and understand the selected features

要求



A Python installation



Jupyter notebook installation



Python coding skills



Some experience with Numpy and Pandas



Familiarity with Machine Learning algorithms



Familiarity with scikit-learn



说明



Welcome to Feature Selection for Machine Learning, the most comprehensive course on feature selection available online.







In this course, you will learn how to select the variables in your data set and build simpler, faster, more reliable and more interpretable machine learning models.















Who is this course for?







You’ve given your first steps into data science, you know the most commonly used machine learning models, you probably built a few linear regression or decision tree based models. You are familiar with data pre-processing techniques like removing missing data, transforming variables, encoding categorical variables. At this stage you’ve probably realized that many data sets contain an enormous amount of features, and some of them are identical or very similar, some of them are not predictive at all, and for some others it is harder to say.







You wonder how you can go about to find the most predictive features. Which ones are OK to keep and which ones could you do without? You also wonder how to code the methods in a professional manner. Probably you did your online search and found out that there is not much around there about feature selection. So you start to wonder: how are things really done in tech companies?







This course will help you! This is the most comprehensive online course in variable selection. You will learn a huge variety of feature selection procedures used worldwide in different organizations and in data science competitions, to select the most predictive features.















What will you learn?







I have put together a fantastic collection of feature selection techniques, based on scientific articles, data science competitions and of course my own experience as a data scientist.







Specifically, you will learn:







How to remove features with low variance







How to identify redundant features







How to select features based on statistical tests







How to select features based on changes in model performance







How to find predictive features based on importance attributed by models







How to code procedures elegantly and in a professional manner







How to leverage the power of existing Python libraries for feature selection















Throughout the course, you are going to learn multiple techniques for each of the mentioned tasks, and you will learn to implement these techniques in an elegant, efficient, and professional manner, using Python, Scikit-learn, pandas and mlxtend.















At the end of the course, you will have a variety of tools to select and compare different feature subsets and identify the ones that returns the simplest, yet most predictive machine learning model. This will allow you to minimize the time to put your predictive models into production.















This comprehensive feature selection course includes about 70 lectures spanning ~8 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 projects.















REMEMBER, the course comes with a 30-day money back guarantee, so you can sign up today with no risk. So what are you waiting for? Enroll today, embrace the power of feature selection and build simpler, faster and more reliable machine learning models.







此课程面向哪些人：



Beginner Data Scientists who want to understand how to select variables for machine learning



Intermediate Data Scientists who want to level up their experience in feature selection for machine learning



Advanced Data Scientists who want to discover alternative methods for feature selection



Software engineers and academics switching careers into data science



Software engineers and academics stepping into data science



Data analysts who want to level up their skills in data science