Machine Learning in Python for Everyone (Video Collection)

Overview

12+ of Video Instruction
Machine Learning in Python for Everyone video collection is based on three video courses that teach everything about the foundations and tools for machine learning. As machine learning has moved from futuristic AI projects to data analysis on your desk, you need to begin to build models and start coding machine learning tasks.

This master class includes the following courses:

• Machine Learning with Python for Everyone Part 1: Learning Foundations, 2nd Edition
• Machine Learning with Python for Everyone, Part 2: Measuring Models
• Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox

Machine Learning with Python for Everyone Part 1: Learning Foundations is code-along sessions moving you from introductory machine learning concepts to concrete code. These videos skew away from heavy mathematics and focus on using Python, scikit-learn. Our emphasis on stories, graphics and code builds your understanding of machine learning. You learn how to load and explore simple datasets; build, train, and perform basic learning evaluation for a few models; compare the resource usage of different models in code snippets and scripts; and briefly explore some of the software and mathematics behind these techniques.

Machine Learning with Python for Everyone, Part 2: Measuring Models teaches the fundamental metrics used to evaluate general learning systems and specific metrics used in classification and regression. You learn techniques for getting the most informative learning performance measures out of your data. You come away with a strong toolbox of numerical and graphical techniques to understand how your learning system will perform on novel data.

Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox teaches about fundamental classification and regression metrics like decision tree classifiers and regressors, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You learn techniques for feature engineering, including scaling, discretization, and interactions. Finally, you tackle implementing pipelines for more complex processing and nested cross-validation for tuning hyperparameters.

About the Instructor

Dr. Mark Fenner, owner of Fenner Training and Consulting, LLC, has taught computing and mathematics to diverse adult audiences since 1999, and holds a PhD in computer science. His research has included design, implementation, and performance of machine learning and numerical algorithms; developing learning systems to detect user anomalies; and probabilistic modeling of protein function.

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.