January 2019
Intermediate to advanced
7h 17m
English
Closed Captioning available in English, Japanese, Korean, Chinese (Simplified), Chinese (Traditional)
Overview
6+ Hours of Video Instruction
Learn the main concepts and techniques used in modern machine learning through numerous examples written in scikit-learn OverviewMachine Learning with scikit-learn LiveLessons is your guide to the scikit-learn library, which provides a wide range of algorithms in machine learning that are unified under a common and intuitive Python API. Most of the dozens of classes provided for various kinds of models share the large majority of the same calling interface. Quite often you can easily substitute one algorithm for another with very little or no change in your underlying code. This enables you to explore the problem space quickly and often to arrive at an optimal–or at least satisficing–approach to your problem domain or datasets.
The scikit-learn library is built on the foundations of the numeric Python stack. It uses NumPy for its fundamental data structures and optimized performance, and it plays well with pandas and matplotlib. It is free software under a BSD license. The great bulk of machine learning programming in Python is done with scikit-learn—at least outside the specialized domain of deep neural networks. About the Instructor David Mertz has been involved with the Python community for 20 years, with data science, (under various previous names) and with machine learning since way back when it was more likely to be called “artificial intelligence.” He was a director of the Python Software Foundation for six years and continues to serve on, or chair, a variety of PSF working groups. He has also written quite a bit about Python: the column Charming Python for IBM developerWorks, for many years; Text Processing in Python (Addison-Wesley, 2003); and two short books for O’Reilly. He created the data science training program for Anaconda, Inc., and was a senior trainer for them. Skill LevelIntermediate
Learn How ToUse various machine learning techniques
Explore a dataset
Perform various types of classification
Use regression, clustering, and hyperparameters
Use feature engineering and feature selection
Implement data pipelines
Develop robust train/test splits
Who Should Take This CourseProgrammers and statisticians interested in using Python and the scikit-learn library to implement machine learning
Course RequirementsProgramming experience
Table of ContentsIntroduction
Lesson 1: What Is Machine Learning?
Lesson 2: Exploring a Dataset
Lesson 3: Classification
Lesson 4: Regression
Lesson 5: Clustering
Lesson 6: Hyperparameters
Lesson 7: Feature Engineering and Feature Selection
Lesson 8: Pipelines
Lesson 9: Robust Train/Test Splits
Summary
About Pearson Video Training Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. These professional and personal technology videos feature world-leading author instructors published by your trusted technology brands: Addison-Wesley, Cisco Press, Pearson IT Certification, Prentice Hall, Sams, and Que Topics include: IT Certification, Network Security, Cisco Technology, Programming, Web Development, Mobile Development, and more. Learn more about Pearson Video training at http://www.informit.com/video.Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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