Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. In this class, you will learn about the most effective machine learning techniques nowadays. More importantly, you'll learn about not only the theoretical underpinnings of learning but also the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.

Lecture Time: Sundays 1:00 - 4:00 MDT Oct 4 to Nov 15, 2020 (Except the Thanksgiving long weekend)

Registration:
Register on Eventbrite via https://www.eventbrite.ca/e/machine-learning-bootcamp-tickets-122118486681. The lecture meeting link will be sent to the members after Eventbrite registration. Registration will be open until Oct 18th.

The lectures are free and we will take a $30 deposit (for GDG Cloud Edmonton members) and a $50 deposit (for non-members).
The deposit will be fully refunded (except Eventbrite’s processing fee) if the participants sign up to teach one topic with the course leaders; or attend all course sessions.

Why take this course:
- Mentorship opportunity with experienced Data Scientists
- Theoretic + hands-on coding labs
- Engage and help to build a strong local DS community

Head Instructor: Daniel Ma (Statistics Canada)
Course Leaders:
- Chris Chen (Imperial oil )
- Tim Gilbertson (Enverus)
- Mehrshad Esfahani (ExxonMobil)
- Leyuan Yu (Manulife)
- Rohan Saha (UofA)
- Hasan Badran (ATB Financial)

Course structure: This course consists of 6 topics and each lecture (3 hours) will be delivered by a course leader teamed up with two other course participants. Each topic consists of two parts, including a theoretical framework and hands-on code labs on how to apply the ML methods in Python.

Covered topics:
Week 1 (Oct 4): Python Basics: Pandas Data Analysis & Visual Data Analysis
Week 2 (Oct 18): Linear Models & Andrew Ng’s ML (2.1 - 4.6)
Week 3 (Oct 25): Supervised Learning: Decision Trees, Random Forests & Bagging
Week 4 (Nov 1): Supervised Learning: Boosting (Gradient Boost, Adaboost, XGboost)
Week 5 (Nov 8): Supervised Learning: SVM, Naive Bayes, KNN
Week 6 (Nov 15): Unsupervised Learning: Clustering, Dimensionality Reduction, Recommender systems, Deep Learning

Course materials:
- Yorko’s ML course
- Andrew Ng’s ML course
- Theoretic references - An Introduction to Statistical Learning and The Elements of Statistical Learning

Prerequisite:
- Strong will to learn Python (experience with programming in C/C++/Matlab/Java/Javascript/R can be helpful)
- College-level calculus and linear algebra
- Basic knowledge in probability and statistics