Introduction to Machine Learning with Python

Room 2
09:00 - 17:00

2 Days

Are you curious about machine learning but need help figuring out where to start? Machine learning is a pervasive technology relevant to today's IT development, but transitioning your skillset to machine learning can be challenging. In this workshop, you’ll level up on machine learning and jumpstart your journey.
Machine Learning

If you're a programmer who wants to learn and apply machine learning techniques, this workshop is for you. We'll explore machine learning concepts and see the steps to process and prepare data for machine learning, train and deploy a machine learning model, and generate real-time predictions. We will explore, Scikit-Learn, a machine learning library that makes it easy to train custom models with just a few lines of code. You'll walk away understanding how to leverage machine learning to solve business problems and the libraries available to build machine learning solutions. No Ph.D. or machine learning experience is required!

Technologies covered:

  • Machine learning techniques: regression, classification, clustering, and more
  • Machine learning library: Scikit-Learn
  • Machine learning algorithms: Linear Regression, Radom Forest Regressor, and XGBoost
  • Data science libraries: NumPy, Pandas, Matplotlib, and more
  • Python-based IDE: Jupyter Notebook

Intended Audience:
The workshop is intended for developers and software engineers looking to transition to machine learning.

Day 1: Machine learning and data

  • Machine learning overview and benefits
  • Real-world uses of machine learning
  • Workshop project overview
  • Machine learning lifecycle
  • Collect and store training data
  • Prepare and clean training data
  • Visualize and analyze training data

Day 2: Model training and deployment

  • Feature Engineering
  • Built-in learning algorithms
  • Train and tune the model
  • Evaluate and qualify the model
  • Deploy and host the model
  • Make predictions using the model
  • Monitor and debug the model
  • Detect and prevent bias

The ability to read basic Python code is necessary, or familiarity with another programming language is strongly recommended. No prior experience with machine learning is required.

Computer setup:
Attendees must bring their laptops with access to Jupyter Notebooks and Python.

Kesha Williams

Kesha Williams is an award-winning technology leader teaching others how to transform their lives through technology. She has 25+ years of experience architecting, designing, and building enterprise web applications. Her passions include teaching cloud topics and leading software engineering teams. Kesha holds multiple AWS certifications and is recognized as an AWS Machine Learning Hero, Alexa Champion, AWS Ambassador, and HackerRank All-Star. She currently serves as the Program Director of Slalom's Cloud Residency and on the Board of Directors for Women in Voice.