Python Machine Learning - Part 1 [Video]

More Information
Learn
  • Discover the different types of machine learning and know when to use them
  • Explore machine learning algorithms and implement them in Python
  • Use powerful open source machine learning libraries to train predictive models
  • Use pandas, NumPy, and matplotlib to manipulate data
  • Evaluate and fine-tune machine learning models
About

Machine learning and predictive analytics are transforming the way that businesses and other organizations operate. Being able to understand trends and patterns in complex data is critical to success, and is becoming one of the key strategies for unlocking growth in a challenging contemporary marketplace. Python can help you deliver key insights into your data. Its unique capabilities as a language let you build sophisticated algorithms and statistical models that can reveal new perspectives and answer key questions that are vital for success.

This video gives you access to the world of predictive analytics and demonstrates why Python is one of the world’s leading data science languages. If you want to ask better questions of data, or need to improve and extend the capabilities of your machine learning systems, this practical data science courseis invaluable. It coversa wide range of powerful Python libraries, including scikit-learn, Theano, and Keras, and featuresguidance and tips on everything from sentiment analysis to neural networks. With this video,you’ll soon be able to answer some of the most important questions facing you and your organization.

Style and Approach

This step-by step guide will walk you through connecting the fundamental theory of machine learning with practical tips for implementation using Python, complete with visualizations and hands-on code examples.

Features
  • Leverage Python’s most powerful open source libraries for deep learning, data wrangling, and data visualization
  • Get to know effective strategies and best practices to improve and optimize machine learning systems and algorithms
  • Ask—and answer— tough questions of your data with robust statistical models, built for a range of datasets
Course Length 3 hours 22 minutes
ISBN 9781786461636
Date Of Publication 27 Jan 2017

Authors

Jason Wolosonovich

Jason is an avid Python machine learning practitioner, obsessed college football fan, and German Shepherd lover. Jason completed his graduate and undergraduate degrees at Arizona State University. During that time, Jason conducted statistical analysis and visual communication analysis for the Arizona State Football program and was part of a 4-person team that placed 3rd nationally in The Great Minds Challenge: IBM Watson Edition, a collegiate machine learning competition. Jason currently works for TransDev and zTrip where he combines data from multiple enterprise sources to gain actionable insights about customers. Jason also recently taught a Machine Learning workshop for a Fortune 500 company and is currently learning to leverage the Apache Spark ecosystem using both Scala and Python. You can find him on LinkedIn(https://www.linkedin.com/in/wolosonovich). You can also write to him at jason@avaland.io or jmwoloso@asu.edu