[ CourseWikia.com ] Feature Engineering and Dimensionality Reduction with Python
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MP4 | h264, 1280x720 | Lang: English | Audio: aac, 48000 Hz | 11h 7m | 3.56 GB
What you'll learn
The importance of Feature Engineering and Dimensionality Reduction in Data Science.
The mathematical foundations for Feature Engineering and Dimensionality Reduction Theory.
The important concepts from absolute beginning with comprehensive unfolding with examples in Python.
Practical explanation and live coding with Python.
Relationship of Feature Engineering and Dimensionality Reduction with modern Machine Learning.
Implementation from scratch in NumPy as well as exploring scikit-learn package and building feature engineering pipelines
Requirements
No prior knowledge needed. We will start from the basics and gradually build up your knowledge in the subject.
A willingness to learn and practice.
A knowledge Python will be a plus.
Description
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.
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