Introduction to Feature Engineering and Dimensionality Reduction, Theory and Practice in Python

Artificial Intelligence (AI) is indispensable these days. From preventing white-collar fraud, real-time aberration detection to forecasting customer churn, businesses are finding new ways to apply machine learning (ML). But how does this technology make accurate predictions? What is the secret behind the fail-proof AI magic? Let us start at the beginning.

The focus of the data science community is usually on algorithm selection and model training. While these elements are important, the most vital element in the AI/ML workflow isn’t how you choose or tune algorithms but what you input to AI/ML. This is where Feature Engineering plays a crucial role. Feature Engineering is essentially the process in which you apply domain knowledge and draw out analytical representations from raw data, preparing it for machine learning. Evidently, the holy grail of data science is Feature Engineering. 

So, understanding the concepts of Feature Engineering and Dimensionality Reduction are the basic requirements for optimizing the performance of most of the machine learning models. Sophisticated and flexible models are sometimes useless if applied to data with irrelevant features. 

The course Introduction to Feature Engineering and Dimensionality Reduction, Theory and Practice in Python has been crafted to reflect the in-demand skills today, helping you to understand the concepts and methodology with respect to Python. The course is:

• Easy to understand.
• Imaginative and descriptive.
• Exhaustive.
• Practical with live coding.
• Establishes links between Feature Engineering and performance of Data Science models.

How is this course different?

This course is created for beginners, but we will go into great detail gradually.

This course is essentially a compilation of all the basics, thus encouraging you to move forward and experience much more than what you have learned. You are assigned activities/tasks in every module. The aim is to assess/(further build) your learning and update your knowledge based on the concepts and methods you have previously learned. Hence, your learning is step-by-step and totally related.

Data Science is, without a doubt, a rewarding career. You solve some of the most interesting problems, and in the bargain, you are rewarded with a handsome salary package. A clear understanding of Feature Engineering and Dimensionality Reduction will help you find new business solutions and ensure upward career growth.

Unlike other expensive courses, this in-depth course has been priced low and is easily affordable. You can master the concepts and methodologies of Feature Engineering and Dimensionality Reduction at a fraction of the cost of comparable courses. Our tutorials are grouped into a series of short HD videos along with code notebooks. 

So, without any further delay, start this course. Embrace yourself with the latest AI knowledge.

Teaching is our passion:

We strive to create online tutorials with subject-matter experts who can help you in understanding the concepts very clearly. We aim to ensure that you have a strong basic understanding before you move onward to the advanced version. Our learning resources include high-quality video content, questions that assess what you have learned, relevant course material, course notes, and handouts. In case you have any doubts, you can approach our friendly team.

Successful completion of this course will enable you to:

• Relate the concepts and theories in Data Science with Feature Engineering and Dimensionality Reduction
• Understand the methodology of Feature Engineering and Dimensionality Reduction with Data Science using real datasets.

Who this course is for:

• Data Scientists.
• Business Analysts. 
• People who want to get their data speak.
• People who want to learn Feature Engineering and Dimensionality Reduction with real datasets in Data Science.
• Individuals who are passionate about numbers and programming.
• People who want to learn Feature Engineering and Dimensionality Reduction along with its implementation in realistic projects.