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Machine Learning Basics with Python and Libraries

Class Code Date Delivery Method Cost
  • Fri Jun 21, 9:00 am to 12:00 pm
  • Fri Jun 28, 9:00 am to 12:00 pm
Live Online - 2 sessions $450
  • Mon Aug 26, 1:00 pm to 4:00 pm
  • Tue Aug 27, 1:00 pm to 4:00 pm
Live Online - 2 sessions $450

Most Technology Training classes will be delivered online until further notice.

Before each sesson, Tech Training will provide a Zoom link for live online classes, along with any required class materials.


This class helps increase awareness about Machine Learning patterns and use cases in the real world, and will help you understand the different ML techniques. Learn about popular ML offerings, and utilize Jupyter Notebooks to perform hands-on labs.


Prerequisite: Basic Python Programming training, or equivalent experience

After this course, you will be able to:  

  • Describe the role of Machine Learning and where it fits into Information Technology strategies
  • Explain the technical and business drivers that result from using Machine Learning
  • Describe Supervised and Unsupervised learning techniques and usages
  • Understand techniques like Classification, Clustering and Regression
  • Discuss how to identify which kinds of technique to be applied for specific use case
  • Understand the popular Machine offerings like Amazon Machine Learning, TensorFlow, Azure Machine Learning, Spark mlib, Python and R etc.
  • Install and Setup Anaconda.
  • Perform hands-on activity using Jupyter Notebooks.


Topic Outline:

Course Introduction

  • History and background of Machine Learning
  • Compare Traditional Programming Vs Machine Leaning 
  • Supervised and Unsupervised Learning Overview
  • Machine Learning patterns
    - Classification
    - Clustering
    - Regression
  • Gartner Hype Cycle for Emerging Technologies
  • Machine Learning offerings in Industry
  • Hands-on exercise 1: Install and Setup Anaconda.
  • Python Libraries
    - NumPy
    - Pandas
    - Scikit Learn
  • Hands-on exercise 2: Data Analysis using Pandas
  • Algorithms
    - Linear Regression
    - Decision Tree
    - Random Forest
    - K-Means Clustering
  • Hands-on exercise 3: Perform Linear regression using Scikit-learn
  • Hands-on exercise 4: Perform Decision tree on Titanic Data set using Scikit-learn
  • References and Next steps
Antony Ross

Antony originally attained a degree in psychology with an emphasis in sport psychology. He began working with athletes and eventually chose to pursue a graduate degree in exercise physiology. He conducted research in muscle physiology while teaching at USC and, subsequently, UCLA.

Custom training workshops are available for this program

Technology training sessions structured around individual or group learning objectives. Learn more about custom training

University IT Technology Training sessions are available to a wide range of participants, including Stanford University staff, faculty, students, and employees of Stanford Hospitals & Clinics, such as Stanford Health Care, Stanford Health Care Tri-Valley, Stanford Medicine Partners, and Stanford Medicine Children's Health.

Additionally, some of these programs are open to interested individuals not affiliated with Stanford, allowing for broader community engagement and learning opportunities.