MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + .srt | Duration: 52 lectures (6 hour, 35 mins) | Size: 3.03 GB
filter methods , wrapper methods and embedded methods
What you’ll learn
Understand different methods of feature selection
Build simpler, faster and more reliable machine learning models
Reduce feature space in a dataset
Analyse and understand the selected features
Implement different methods of feature selection
Requirements
Python coding skills
Jupyter notebook installation
A Python installation
Description
Learn how to select features and build simpler, faster and more reliable machine learning models.
This is the most comprehensive, yet easy to follow, course for feature selection available online. Throughout this course you will learn a variety of techniques used worldwide for variable selection, gathered from data competition websites and white papers, blogs and forums, and from the instructor’s experience as a Data Scientist.
You will have at your fingertips, altogether in one place, multiple methods that you can apply to select features from your data set.
The lectures include an explanation of the feature selection technique, the rationale to use it, and the advantages and limitations of the procedure. It also includes full code that you can take home and apply to your own data sets.
This course is therefore suitable for complete beginners in data science looking to learn how to go about to select features from a data set, as well as for intermediate and even advanced data scientists seeking to level up their skills.
With more than 50 lectures and 6 hours of video this comprehensive course covers every aspect of variable selection. Throughout the course you will use python as your main language.
So what are you waiting for? Enrol today, learn how to select variables for machine learning, and build simpler, faster and more reliable learning models.
Who this course is for:
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
Data analysts who want to level up their skills in data science
Software engineers and academics stepping into data science
Software engineers and academics switching careers into data science
Advanced Data Scientists who want to discover alternative methods for feature selection
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