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!

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