Explore Sklearn
Published 10/2022
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 2.90 GB | Duration: 6h 16m
to develop machine learning skills
What you'll learn
Students will learn about sklearn, Python's machine learning library
Students will learn about and go over the code of supervised learning classification and regression problems
Students will learn about and go over the code of semi-supervised classification and regression problems
Students will learn about and go over the code of unsupervised regression problems
Students will learn about and go over the code of principal component analysis
Students will learn about and go over the code of feature selection techniques
Requirements
Basic Python programming is a prerequisite to this course
Description
This course is intended to give the student an overview of Python's machine learning library, sklearn. The course is broken down into seven sections, being:-1. Introduction2. Supervised learning3. Semi-supervised learning4. Unsupervised learning5. Dimensionality reduction6. Feature selection7. Other topicsThe student will receive extensive guidance on how to use sklearn. Sklearn's search engine will be used to research sklearn's many functions, which include:-1. Preprocessing functions2. Classification models3. Regression models4. Semi-supervision models5. Clustering models6. Dimensionality reduction functions7. Feature selection functions8. Metrics functionsIn addition to learning about the numerous and varied types of functions in sklearn, The student will go over the code of twelve Jupyter Notebooks. The subject matter of these notebooks are:-1. Supervised classification problems2. Supervised regression problems3. Semi-supervised classification problems4. Semi-supervised regression problems5. Unsupervised classification problems6. Dimensionality reduction7. Feature...
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