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Practical Machine Learning with Python

Learn how to put the latest open source technology into practice with hands-on training, delivered by industry experts, aligned to your desired business outcomes

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3 Days

Available On-Site

Available Virtually

Open Enrollments Available
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Customizable

Machine learning is a type of artificial intelligence wherein computer programs learn new capabilities when exposed to data. This 3-day instructor-led training course teaches the basics of machine learning with practical hands-on labs using Python and various support libraries.

Day one introduces the foundational concepts of data science and machine learning. Hands-on labs progressively build a basic collection of tools and experiments reinforcing the concepts covered in the lecture. Attendees will learn how to create a basic Python development environment for machine learning while producing several basic but useful and instructive programs. Basic probability, statistics, and basic data curation skills are developed throughout.

Days two and three build on the foundational skills imparted in day one, introducing a formal classification of the most common machine learning algorithms and their purposes. Modules and labs give attendees experience using the most common algorithms and a chance to create real solutions, such as fraud detection and recommendation engines.

Upon completion attendees will have a broad but practical understanding of machine learning and a base from which to pursue real applications and further study.

Delivery

Available for Instructor-Led (ILT) in-person/onsite training or Virtual Instructor-Led training (VILT) delivery; Open Enrollment options may be available.

Who Should Attend

Application Developers, Analysts and Data Scientists

What Attendees will learn

This course is designed to provide attendees with a practical introduction to machine learning using Python. Learning modules include:

  • Data science and machine learning
  • Working with and curating data
  • The machine learning process and algorithms
  • Decision trees and ensemble methods
  • Probability based learning
  • Deep Learning

Prerequisites

Each attendee should be familiar with Python and will require the ability to run a 64 bit virtual machine (provided with the course). Basic Linux command line skills are valuable but not required.