2-Day Python and Machine Learning Bootcamp Series

September 18 – September 19
2 Sessions

Cleveland (Online)

$375 USD

Questions? Read our FAQs

2-Day Python and Machine Learning Bootcamp Series | September 18

Kevin Coyle Photo

Senior Consultant , Capgemini


September 18 – September 19
2 Sessions


$375 USD
$375 USD

Questions? Read our FAQs

About This Workshop Series

Machine Learning is perhaps the most powerful force in technology, and Python is perhaps the most popular language for doing it. This 2-day workshop helps students understand, build, and interpret machine learning models using the python ecosystem.

This workshop will introduce you to the most important machine learning concepts in a way that’s clear, accessible, and easy to put into action. With no assumed knowledge of Machine Learning, you’ll understand how to think about and interpret Machine Learning problems, and best practices for using the most popular machine learning techniques in a variety of domains.

It starts with the basics of Python’s major data science libraries (Numpy, Pandas, and SciKit Learn), and builds on these to help you implement state-of-the-art machine learning techniques on your own data.

Some familiarity with the core Python programming language is recommended from Introduction to Python Bootcamp or similar experience.

General Assembly’s classes, workshops & events are for recreational purposes and are not regulated by the bureau of proprietary school supervision.

If this class is recorded, students will be sent the recording link within 7 days after class completion by email. Students are encouraged to download and save any shared materials (worksheets, deck if applicable) during class for later reference as they are not be shared by email.


Sep 18
Saturday, 18 September 11 am – 6 pm EDT Session I: Python-for-Data Basics, Machine Learning Introduction


  • Introduction to Numpy and Pandas.
  • Popular methods for handling and prepping your data.


  • Introduction to Machine Learning.
  • Regression.

Sep 19
Sunday, 19 September 11 am – 6 pm EDT Session II: Machine Learning Continued, Bringing It All Together


  • Cross Validation.
  • Regularization.


  • Classification.
  • Putting it all together.


In this workshop you'll learn :

  • How to use Numpy and Pandas to import and organize your data.
  • The different types of machine learning problems, and when to use different methods.
  • Best practices for prepping your data to make it Machine Learning ready.
  • Regression Algorithms, and how to use them to predict continuous data.
  • Classification algorithms, and how to use them to predict binary outcomes.
  • Relative strengths and weaknesses of different Machine learning techniques.
  • Best practices to make sure your models can handle data they haven’t seen before.
  • Advice on future learning paths depending on your desired career goals.
  • Visualize the results of your analysis.


Download Anaconda here prior to class.

  • You will need access to a laptop or computer with a working webcam and microphone as well as a strong internet connection.
  • This class will occur completely online using Zoom.
  • Class setup information will be emailed to all students signed up around 24 hours before (and again an hour before) your class launches.
  • Each individual class participant must have purchased a ticket.

About the Instructor

Kevin Coyle Photo

Senior Consultant ,

Kevin Coyle is a data scientist and data science educator. As a Senior Consultant at Capgemini, he sits on the Data Science and AI team and works in the Telecom, Media, and Entertainment sector, creating AI engines that augment the decision making process for stakeholders at enterprise level companies, as well as creating forecasting solutions using a variety of statistical, machine learning, and deep learning models. As an educator, he loves helping students make the transition into the world of coding, data, and obsession with reading what's new on arxiv.org.

He's on a permanent mission to find the tastiest burrito that Los Angeles has to offer. When he's not caught tweaking hyper-parameters or drinking lots of coffee, he loves to spend time with his family outdoors: skiing, playing basketball, or perfecting his photography hobby.

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