In this workshop you will learn the fundamentals of machine learning primarily through hands on code labs guided by the instructor. Students will learn about learning algorithms and techniques and their applications. They also will learn to apply machine learning concepts to the real-world business problems.
This workshop is fun and exciting, with practical exercises and the aim is to provide a window into the cutting edge of machine learning by presenting a simplified explanation of learning models.
- Session 1: May. 31st Fri 9am-12pm PST
- 3 Hours/ 1 Session
- 10 Topics / 2 hands-on code labs
- Live session and real time interaction
- Watch session replay anywhere any time
Check the content tab for full course outlines.
WHO SHOULD LEARN:
Developers, data scientists, students who want to get started on machine learning projects or applications.
Familiarity with Python, or willingness to learn it quickly
Basic familiarity with statistics and probability theory is a plus, but not required
If you miss the live session or want to learn again, you can watch recorded sessions any time, along with interactive learning tools, slides, course notes
Module 1: Machine Learning Fundamentals
- Loading dataset and understand its structure
- Two fundamental machine learning methods with implementation in python
- Classification: K Nearest Neighbors
- Regression: Linear Regression
Module 2: Learning and Predicting
- Split the data on Train and Test
- Building classification/regression models
- Predict by using the models
Module 3: Evaluate Learning Algorithms and Model Selection
- Mean Absolute Error (MAE)
- Root Mean Squared Error (RMSE)
Module 4: Software Libraries and Datasets
- Sklearn and matplotlib
- Work with kaggle datasets
Farnoosh is a computer scientist with more than 10 years background in machine learning, software development, and algorithm design. She has also extensive research and teaching experience in different subjects of computer science. Farnoosh received her Ph.D. degree in computer science from Tehran Polytechnic University. She is currently Postdoctoral Fellow in Princess Margaret Cancer Research Centre. Her current focus is on designing and developing machine learning and deep learning models for medical image analysis including segmentation and classification