Workshop: Demystify Machine Learning (6-hr on-line OR 7-hr in-person)

Type: Workshop

Duration: 9 am to 5 pm (excluding one-hour lunch break; lunch & tea break are provided for the 7-hr in-person workshop only)

Target Audience: Busy executives and professionals who want to learn how to apply machine learning techniques on data in order to gain relevant insights and make better decisions.

Synopsis: Unlike the traditional rules-based programming, machine learning allows a computer to learn from data without being explicitly programmed. Machine learning is an exciting field and has many applications (e.g. market segmentation, price prediction, logistic classification).

In this workshop learn the essential building blocks in order to implement a Machine Learning model in Python. There will be  many hands-on opportunities in using key Data Science packages such as Numpy, Pandas, Matplotlib and Scikit-learn. The workshop includes demo of Machine Learning in solving real-life problems.

All hands-on exercises will be done using the latest released version Python 3.X and in the Jupyter notebook programming environment.

For the 7-hr in-person workshop, the last segment is a guided project which gives the participants an opportunity to integrate the dots – apply what they have learnt to build a Machine Learning application in a real-life scenario.

Prerequisites: A participant should have basic experience in Python programming or at the minimum, be able to read Python code. The participants are also required to bring along their own laptops or macs. All hands-on exercises will be done on the participants’ own computers.

Trainer: Eric Hong (MBA, MSc, BEng, PGDE) has worked more than 10 years at US MNCs in business and engineering. He has extensive teaching experience in Software Development, Engineering and Physics. Eric is the founder of IntegrateDots, a consulting and training company that specializes in Data Analytics and Software Automation.

Course Outline

  • Introduction
    • what’s Machine Learning?
    • why use Machine Learning?
  • Getting Ready – Installation of Key Packages and Development Environment
    • installation of Key Data Science packages
    • Jupyter notebook
  • Case Studies of Machine Learning Applications
    • Regression algorithm
    • Naive Bayes algorithm
  • Building Blocks
    • Numpy > arrays
    • Pandas > dataframes
    • Matplotlib > visualization
    • Scikit-learn > toolkit for Machine Learning tasks
  • *Project (only applicable for the 7-hr in-person workshop)
    • build a Machine Learning application