Feature Engineering for Machine Learning
Feature Engineering for Machine Learning, available at $89.99, has an average rating of 4.61, with 202 lectures, based on 3285 reviews, and has 23712 subscribers.
You will learn about Learn multiple techniques for missing data imputation. Transform categorical variables into numbers while capturing meaningful information. Learn how to deal with infrequent, rare, and unseen categories. Learn how to work with skewed variables. Convert numerical variables into discrete ones. Remove outliers from your variables. Extract useful features from dates and time variables. Learn techniques used in organizations worldwide and in data competitions. Increase your repertoire of techniques to preprocess data and build more powerful machine learning models. This course is ideal for individuals who are Data scientists who want to learn how to preprocess datasets in order to build machine learning models. or Data scientists who want to learn more techniques for feature engineering for machine learning. or Data scientists who want to improve their coding skills and programming practices for feature engineering. or Software engineers, mathematicians and academics switching careers into data science. or Data scientists interested in experimenting with various feature engineering techniques on data competitions or Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering. It is particularly useful for Data scientists who want to learn how to preprocess datasets in order to build machine learning models. or Data scientists who want to learn more techniques for feature engineering for machine learning. or Data scientists who want to improve their coding skills and programming practices for feature engineering. or Software engineers, mathematicians and academics switching careers into data science. or Data scientists interested in experimenting with various feature engineering techniques on data competitions or Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering.
Enroll now: Feature Engineering for Machine Learning
Summary
Title: Feature Engineering for Machine Learning
Price: $89.99
Average Rating: 4.61
Number of Lectures: 202
Number of Published Lectures: 202
Number of Curriculum Items: 202
Number of Published Curriculum Objects: 202
Original Price: $199.99
Quality Status: approved
Status: Live
What You Will Learn
- Learn multiple techniques for missing data imputation.
- Transform categorical variables into numbers while capturing meaningful information.
- Learn how to deal with infrequent, rare, and unseen categories.
- Learn how to work with skewed variables.
- Convert numerical variables into discrete ones.
- Remove outliers from your variables.
- Extract useful features from dates and time variables.
- Learn techniques used in organizations worldwide and in data competitions.
- Increase your repertoire of techniques to preprocess data and build more powerful machine learning models.
Who Should Attend
- Data scientists who want to learn how to preprocess datasets in order to build machine learning models.
- Data scientists who want to learn more techniques for feature engineering for machine learning.
- Data scientists who want to improve their coding skills and programming practices for feature engineering.
- Software engineers, mathematicians and academics switching careers into data science.
- Data scientists interested in experimenting with various feature engineering techniques on data competitions
- Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering.
Target Audiences
- Data scientists who want to learn how to preprocess datasets in order to build machine learning models.
- Data scientists who want to learn more techniques for feature engineering for machine learning.
- Data scientists who want to improve their coding skills and programming practices for feature engineering.
- Software engineers, mathematicians and academics switching careers into data science.
- Data scientists interested in experimenting with various feature engineering techniques on data competitions
- Software engineers who want to learn how to use Scikit-learn and other open-source packages for feature engineering.
Welcome to Feature Engineering for Machine Learning, the most comprehensive course on feature engineering available online. In this course, you will learn about variable imputation, variable encoding, feature transformation, discretization, and how to create new features from your data.
Master Feature Engineering and Feature Extraction.
In this course, you will learn multiple feature engineering methods that will allow you to transform your data and leave it ready to train machine learning models. Specifically, you will learn:
-
How to impute missing data
-
How to encode categorical variables
-
How to transform numerical variables and change their distribution
-
How to perform discretization
-
How to remove outliers
-
How to extract features from date and time
-
How to create new features from existing ones
Create useful Features with Math, Statistics and Domain Knowledge
Feature engineering is the process of transforming existing features or creating new variables for use in machine learning. Raw data is not suitable to train machine learning algorithms. Instead, data scientists devote a lot of time to data preprocessing. This course teaches you everything you need to know to leave your data ready to train your models.
While most online courses will teach you the very basics of feature engineering, like imputing variables with the mean or transforming categorical variables using one hot encoding, this course will teach you that, and much, much more.
In this course, you will first learn the most popular and widely used techniques for variable engineering, like mean and median imputation, one-hot encoding, transformation with logarithm, and discretization. Then, you will discover more advanced methods that capture information while encoding or transforming your variables to improve the performance of machine learning models.
You will learn methods like the weight of evidence, used in finance, and how to create monotonic relationships between variables and targets to boost the performance of linear models. You will also learn how to create features from date and time variables and how to handle categorical variables with a lot of categories.
The methods that you will learn were described in scientific articles, are used in data science competitions, and are commonly utilized in organizations. And what’s more, they can be easily implemented by utilizing Python’s open-source libraries!
Throughout the lectures, you’ll find detailed explanations of each technique and a discussion about their advantages, limitations, and underlying assumptions, followed by the best programming practices to implement them in Python.
By the end of the course, you will be able to decide which feature engineering technique you need based on the variable characteristics and the models you wish to train. And you will also be well placed to test various transformation methods and let your models decide which ones work best.
Step-up your Career in Data Science
You’ve taken your first steps into data science. You know about the most commonly used prediction models. You’ve even trained a few linear regression or classification models. At this stage, you’re probably starting to find some challenges: your data is dirty, lots of values are missing, some variables are not numerical, and others extremely skewed. You may also wonder whether your code is efficient and performant or if there is a better way to program. You search online, but you can’t find consolidated resources on feature engineering. Maybe just blogs? So you may start to wonder: how are things really done in tech companies?
In this course, you will find answers to those questions. Throughout the course, you will learn multiple techniques for the different aspects of variable transformation, and how to implement them in an elegant, efficient, and professional manner using Python. You will leverage the power of Python’s open source ecosystem, including the libraries NumPy, Pandas, Scikit-learn, and special packages for feature engineering: Feature-engine and Category encoders.
By the end of the course, you will be able to implement all your feature engineering steps into a single elegant pipeline, which will allow you to put your predictive models into production with maximum efficiency.
Leverage the Power of Open Source
We will perform all feature engineering methods utilizing Pandas and Numpy, and we will compare the implementation with Scikit-learn, Feature-engine, and Category encoders, highlighting the advantages and limitations of each library. As you progress in the course, you will be able to choose the library you like the most to carry out your projects.
There is a dedicated Python notebook with code to implement each feature engineering method, which you can reuse in your projects to speed up the development of your machine learning models.
The Most Comprehensive Online Course for Feature Engineering
There is no one single place to go to learn about feature engineering. It involves hours of searching on the web to find out what people are doing to get the most out of their data.
That is why, this course gathers plenty of techniques used worldwide for feature transformation, learnt from data competitions in Kaggle and the KDD, scientific articles, and from the instructor’s experience as a data scientist. This course therefore provides a source of reference where you can learn new methods and also revisit the techniques and code needed to modify variables whenever you need to.
This course is taught by a lead data scientist with experience in the use of machine learning in finance and insurance, who is also a book author and the lead developer of a Python open source library for feature engineering. And there is more:
-
The course is constantly updated to include new feature engineering methods.
-
Notebooks are regularly refreshed to ensure all methods are carried out with the latest releases of the Python libraries, so your code will never break.
-
The course combines videos, presentations, and Jupyter notebooks to explain the methods and show their implementation in Python.
-
The curriculum was developed over a period of four years with continuous research in the field of feature engineering to bring you the latest technologies, tools, and trends.
Want to know more? Read on…
This comprehensive feature engineering course contains over 100 lectures spread across approximately 10 hours of video, and ALL topics include hands-on Python code examples that you can use for reference, practice, and reuse 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? Enrol today and join the world’s most comprehensive course on feature engineering for machine learning.
Course Curriculum
Chapter 1: Welcome
Lecture 1: Course curriculum overview
Lecture 2: Course requirements
Lecture 3: How to approach this course
Lecture 4: Setting up your computer
Lecture 5: Resources to learn machine learning skills
Chapter 2: Course material
Lecture 1: Course material
Lecture 2: Download Jupyter notebooks
Lecture 3: Download datasets
Lecture 4: Download presentations
Chapter 3: Variable Types
Lecture 1: Variables | Intro
Lecture 2: Numerical variables
Lecture 3: Categorical variables
Lecture 4: Date and time variables
Lecture 5: Mixed variables
Chapter 4: Variable Characteristics
Lecture 1: Variable characteristics
Lecture 2: Missing data
Lecture 3: Cardinality
Lecture 4: Rare labels
Lecture 5: Variable distribution
Lecture 6: Outliers
Lecture 7: Linear models assumptions
Lecture 8: Linear model assumptions – additional reading resources (optional)
Lecture 9: Variable magnitude
Lecture 10: Summary table
Lecture 11: Additional reading resources
Chapter 5: Missing Data Imputation – Basic
Lecture 1: Basic imputation methods
Lecture 2: Mean or median imputation
Lecture 3: Arbitrary value imputation
Lecture 4: Frequent category imputation
Lecture 5: Missing category imputation
Lecture 6: Adding a missing indicator
Lecture 7: Basic methods – considerations
Lecture 8: Basic imputation with pandas
Lecture 9: Basic imputation with pandas – demo
Lecture 10: Basic methods with Scikit-learn
Lecture 11: Mean or median imputation with Scikit-learn
Lecture 12: Arbitrary value imputation with Scikit-learn
Lecture 13: Frequent category imputation with Scikit-learn
Lecture 14: Missing category imputation with Scikit-learn
Lecture 15: Adding a missing indicator with Scikit-learn
Lecture 16: Imputation with GrdiSearch – Scikit-learn
Lecture 17: Basic methods with Feature-engine
Lecture 18: Mean or median imputation with Feature-engine
Lecture 19: Arbitrary value imputation with Feature-engine
Lecture 20: Frequent category imputation with Feature-engine
Lecture 21: Arbitrary string imputation with Feature-engine
Lecture 22: Adding a missing indicator with Feature-engine
Lecture 23: Wrapping up
Lecture 24: Treat: our movie pick
Chapter 6: Missing Data Imputation – Alternative Methods
Lecture 1: Alternative imputation methods
Lecture 2: Complete Case Analysis
Lecture 3: CCA – considerations with code demo
Lecture 4: End of distribution imputation
Lecture 5: Random sample imputation
Lecture 6: Random imputation – considerations with code
Lecture 7: Mean or median imputation per group
Lecture 8: CCA with pandas
Lecture 9: End of distribution imputation with pandas
Lecture 10: Random sample imputation with pandas
Lecture 11: Mean imputation per group with pandas
Lecture 12: CCA with Feature-engine
Lecture 13: End of distribution imputation with Feature-engine
Lecture 14: Random sample imputation with Feature-engine
Lecture 15: Imputation – Summary table
Lecture 16: Wrapping up
Chapter 7: Multivariate Missing Data Imputation
Lecture 1: Multivariate imputation
Lecture 2: KNN imputation
Lecture 3: KNN imputation – Demo
Lecture 4: MICE
Lecture 5: missForest
Lecture 6: MICE and missForest – Demo
Lecture 7: Additional reading resources (Optional)
Lecture 8: Treat: Our book recommendation
Chapter 8: Categorical Encoding – Basic methods
Lecture 1: Categorical encoding | Introduction
Lecture 2: One hot encoding
Lecture 3: One hot encoding with pandas
Lecture 4: One hot encoding with sklearn
Lecture 5: One hot encoding with Feature-engine
Lecture 6: One hot encoding with Category encoders
Lecture 7: Ordinal encoding
Lecture 8: Ordinal encoding with pandas
Lecture 9: Ordinal encoding with sklearn
Lecture 10: Ordinal encoding with Feature-engine
Lecture 11: Ordinal encoding with Category encoders
Lecture 12: Count or frequency encoding
Lecture 13: Count encoding with pandas
Lecture 14: Count encoding with Feature-engine
Lecture 15: Count encoding with Category encoders
Lecture 16: Unseen categories
Lecture 17: Wrapping up
Chapter 9: Categorical encoding – Monotonic
Lecture 1: Categorical encoding | Monotonic
Instructors
-
Soledad Galli
Data scientist | Instructor | Software developer -
Train in Data Team
Data scientists | Instructors | Software engineers
Rating Distribution
- 1 stars: 27 votes
- 2 stars: 35 votes
- 3 stars: 226 votes
- 4 stars: 968 votes
- 5 stars: 2029 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
You may also like
- Digital Marketing Foundation Course
- Google Shopping Ads Digital Marketing Course
- Multi Cloud Infrastructure for beginners
- Master Lead Generation: Grow Subscribers & Sales with Popups
- Complete Copywriting System : write to sell with ease
- Product Positioning Masterclass: Unlock Market Traction
- How to Promote Your Webinar and Get More Attendees?
- Digital Marketing Courses
- Create music with Artificial Intelligence in this new market
- Create CONVERTING UGC Content So Brands Will Pay You More
- Podcast: The top 8 ways to monetize by Podcasting
- TikTok Marketing Mastery: Learn to Grow & Go Viral
- Free Digital Marketing Basics Course in Hindi
- MailChimp Free Mailing Lists: MailChimp Email Marketing
- Automate Digital Marketing & Social Media with Generative AI
- Google Ads MasterClass – All Advanced Features
- Online Course Creator: Create & Sell Online Courses Today!
- Introduction to SEO – Basic Principles of SEO
- Affiliate Marketing For Beginners: Go From Novice To Pro
- Effective Website Planning Made Simple