Free Feature Engineering Course

Feature Engineering

star 4.58  Intermediate level 2.25 learning hrs 3.4K+ Learners

Learn feature engineering from basics in this free online training. This course is taught hands-on by experts. Learn about goal of feature engineering and its process in details. Best for beginners. Enroll for free now!

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About this course

Understanding what parameters or features has what level of impact on the solution to a Machine Learning problem statement is key. If the talk is about using machine learning to solve a problem, then providing the algorithm with high-quality features is what makes the difference between a raw machine learning model and a very well-optimized one. Feature engineering is the most critical aspect of working with data and helping the machine understand the same. This is a skill that all analysts, data scientists, and machine learning engineers must possess. Now, keeping exactly this in mind, we have come up with this course on Feature Engineering to get you all started with the same. The instructor will also be covering a hands-on demonstration with an ample amount of examples to help concrete your understanding.

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Course outline

Understanding Feature Engineering

Goal of Feature Engineering

Process of Feature Engineering

Various Feature Engineering Techniques

Correlation Matrix and Data Handling

Diabetes prediction using Multiple Features

Get access to the complete curriculum once you enroll in the course

Feature Engineering

rating icon 4.58

2.25 Hours

Intermediate

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3.4K+ learners enrolled so far

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Get free course content

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Master in-demand skills & tools

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Test your skills with quizzes

Trusted by 10 Million+ Learners globally

Learner reviews of the Free Courses

4.58
79%
12%
5%
1%
3%
Reviewer Profile

5.0

“Comprehensive and Engaging Course on Feature Engineering”
This course provided a deep understanding of feature engineering techniques. The content was well-structured, with practical examples that helped me apply what I learned to real-world data. The instructor was knowledgeable and made complex topics easy to understand. I highly recommend this course to anyone looking to enhance their data science skills.
“Exceptional Learning Experience.”
I particularly liked the comprehensive coverage of the subject matter and the well-structured content. The instructors were knowledgeable and engaging, and the resources provided were highly valuable. The course not only expanded my knowledge but also built my confidence in applying the concepts practically.
“Enhancing Model Performance Through Feature Engineering: A Transformative Learning Experience”
Throughout the feature engineering course, I gained a deep understanding of how crucial feature engineering is for building effective machine learning models. The course highlighted various techniques such as data imputation, one-hot encoding, and outlier handling, which are essential for improving model accuracy and performance. One of the most impactful moments was learning how to transform raw data into meaningful features that significantly enhance model outcomes. The hands-on exercises allowed me to apply these techniques in real-world scenarios, reinforcing the theoretical concepts with practical experience.
Reviewer Profile

5.0

“that course is good and quiz is also good”
The Feature Engineering online course is highly insightful, providing a solid foundation in transforming raw data into valuable features for machine learning models. The course covers essential techniques like one-hot encoding, data imputation, and feature scaling, making complex concepts accessible. Practical examples and exercises help reinforce learning, making it a valuable resource for both beginners and experienced practitioners looking to enhance their skills.
Reviewer Profile

5.0

“The most exciting part was seeing how feature engineering, like one-hot encoding and outlier handling, can greatly improve model performance and accuracy.”
I enjoyed learning feature engineering techniques because they deepened my understanding of data preprocessing. Mastering one-hot encoding and exploring data imputation and outlier handling highlighted their importance in preparing data effectively, with practical exercises bringing these concepts to life.
Reviewer Profile

5.0

“Feedback on Feature Engineering Beginner Course”
The course offered a solid introduction to feature engineering, covering basics like encoding, scaling, and feature selection. The hands-on exercises were helpful, though more practical examples and industry case studies would enhance learning. Including advanced topics, such as feature importance in machine learning, could better bridge the gap to intermediate levels. Overall, it's a strong starting point for beginners.
Reviewer Profile

5.0

“Feature engineering is crucial across various machine learning and deep learning algorithms, including linear and logistic regression. It involves transforming data to improve model performance, whether through manual or automated techniques.”
I liked how feature engineering's importance spans both traditional and deep learning algorithms, highlighting its role in enhancing model performance. The flexibility in techniques, from manual selection to automated methods, showcases its critical role in transforming raw data into valuable insights across diverse machine learning contexts.
Reviewer Profile

5.0

“Feature engineering is crucial across various machine learning and deep learning algorithms, including linear and logistic regression. It involves transforming data to improve model performance, whether through manual or automated techniques.”
I liked how feature engineering's importance spans both traditional and deep learning algorithms, highlighting its role in enhancing model performance. The flexibility in techniques, from manual selection to automated methods, showcases its critical role in transforming raw data into valuable insights across diverse machine learning contexts.
Reviewer Profile

4.0

“Comprehensive Insight into Feature Engineering Techniques”
I appreciated the detailed exploration of feature engineering concepts, including data imputation, outlier handling, and automatic feature selection. The examples provided made it easier to understand how these techniques improve model performance. The hands-on practice sessions really enhanced my skills in applying these methods to real-world datasets.
Reviewer Profile

5.0

“The Great Feature Engineering Course”
I appreciated the comprehensive exploration of both manual and automatic feature selection techniques. The course provided clear examples and practical exercises that deepened my understanding of how to enhance model performance through effective feature engineering. The detailed coverage of real-world applications and the guidance on selecting the most relevant features were particularly beneficial.

What our learners enjoyed the most

Frequently Asked Questions

Will I receive a certificate upon completing this free course?

Yes, upon successful completion of the course and payment of the certificate fee, you will receive a completion certificate that you can add to your resume.

Is this course free?

Yes, you may enroll in the course and access the course content for free. However, if you wish to obtain a certificate upon completion, a non-refundable fee is applicable.

What is a Feature Engineering Example?

The most common Feature Engineering example is continuous data like the price of some products, coordinates of some object on the map, or the temperature of some industrial processes. You can also take examples of domain data, categorical data, etc.

 

What does Feature Engineering include?

Feature Engineering involves the creation, transformation, extraction, and selection of the features. These features are also known as variables, and these features help create an accurate Machine Learning Algorithm.  

 

What is the purpose of Feature Engineering?

Feature Engineering enhances the Machine Learning processes and helps in increasing the predictive power of these Machine Learning algorithms. This is achieved by creating the features from the raw data.

 

What are Feature Engineering Techniques?

Some of the critical Feature Engineering techniques include Imputation, Discretization, Categorical Encoding, Feature Splitting, Handling Outliers, Variable Transformations, Scaling, and Creating Features.     

 

What are the two steps of Feature Engineering?

Feature Engineering consists of four main steps: Feature Creation, Transformations, Feature Extraction, and Feature Selection. The processes it includes are testing features, deciding on the features to be created, creating the features, testing the impact of the created features on the task, improving the created features if needed, and repeating this process until you reach your result.

 

How do I start as a Feature Engineer?

The first step towards becoming a Feature Engineer is to get familiar with the Data Science domain, specifically the Machine Learning techniques and algorithms. Through Feature Engineering, you will be able to enhance these Machine Learning techniques. You can also enroll in the free Feature Engineering course Great Learning offers and earn your free Feature Engineering certificate.

 

How much does this Feature Engineering course cost?

It is an entirely free course from Great Learning Academy. Anyone interested in learning the basics of Feature Engineering can get started with this course.

Is there any limit on how many times I can take this free course?

Once you enroll in the Feature Engineering course, you have lifetime access to it. So, you can log in anytime and learn it for free online.

Can I sign up for multiple courses from Great Learning Academy at the same time?

Yes, you can enroll in as many courses as you want from Great Learning Academy. There is no limit to the number of courses you can enroll in at once, but since the courses offered by Great Learning Academy are free, we suggest you learn one by one to get the best out of the subject.

Why choose Great Learning Academy for this free Feature Engineering course?

Great Learning Academy provides this Feature Engineering course for free online. The course is self-paced and helps you understand various topics that fall under the subject with solved problems and demonstrated examples. The course is carefully designed, keeping in mind to cater to both beginners and professionals, and is delivered by subject experts. Great Learning is a global ed-tech platform dedicated to developing competent professionals. Great Learning Academy is an initiative by Great Learning that offers in-demand free online courses to help people advance in their jobs. More than 5 million learners from 140 countries have benefited from Great Learning Academy's free online courses with certificates. It is a one-stop place for all of a learner's goals.

What are the steps to enroll in this Feature Engineering course?

Enrolling in any of the Great Learning Academy’s courses is just one step process. Sign-up for the course, you are interested in learning through your E-mail ID and start learning them for free online.

Will I have lifetime access to this free Feature Engineering course?

Yes, once you enroll in the course, you will have lifetime access, where you can log in and learn whenever you want to. 

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