COURSE OBJECTIVES:
In this course you will learn the fundamentals of machine learning including intuitions, important theoretical aspects and how to use machine learning algorithms to solve problems. Students will learn about the foundational underpinnings of machine learning as well as how to put that knowledge to the test with practical exercises.
The course takes projects focused approach to teach you machine learning by building machine learning models and projects. The instructor will walk you through a series of curated projects, and explain the key concepts as they arise. Students will learn the theory and how these models work under the hood while writing code.
The course balances learning theory, coding exercises, and working on projects. Students who take this course will be able to:
Identify and frame problems that can be solved by machine learning
Choose the right techniques to the problems
Understand key machine learning concepts and how algorithms / models work
Build and training various models with scikit-learn
Troubleshoot and improve models
Discuss the parts and processes involved in building large scale machine learning applications
COURSE SCHEDULE:
- Session 1: Feb 13th 10am~12pm PST (US Pacific Time, GMT-8)
- Session 2: Feb 13th 12pm~2pm PST
- Session 3: Feb 20th 10am~12pm PST
- Session 4: Feb 20th 12pm~2pm PST
- Session 5: Feb 27th 10am~12pm PST
- Session 6: Feb 27th 12pm~2pm PST
- Session 7: Mar 6th 10am~12pm PST
- Session 8: Mar 6th 12pm~2pm PST
COURSE INCLUDE:
- 4 weeks / 8 sessions / 16 hours
- 8 lectures / 4 hands-on code labs/Capstone project
- Live Sessions (with zoom), Real time interaction
- Slack support during and after class
COURSE CONTENT:
Check the Syllabus tab for full course content.
WHO SHOULD LEARN:
Developers, data scientists, students.
PREREQUISITE:
Basic familiarity with python
Using Jupyter Notebook or Colab
Difficulty Level
Beginner~Intermediate
Est. time spend per week: 4 hours live class (required) + 2 hours homework (required) + 2 hours projects (bonus, optional).
FREE TRIAL
Full refund upon request before the first session ends (Feb 13th, 2021 12:00pm PST). 5% transaction fee is not refundable.
SESSION REPLAY
If missed live sessions, you can watch recordings any time, along with interactive learning tools, slides, course notes
Students have one-year access to all course materials
BENEFITS
Earn Certificate of Completion
Financial aid is available for application, contact for details
PREVIOUS COHORTS:
Cohort 3: Nov 23 ~ Dec 16, 2020
Cohort 2: Mar 23 ~ Apr 9, 2020
Cohort 1: Jan 15 ~ Jan 31, 2020
Module 1: Machine Learning fundamentals
- Introduction to machine learning and its industrial applications
- Review on machine learning programming (pandas, sklearn, etc.)
- Jupyter and Colab notebooks versus Pycharm
Module 2: Machine Learning Algorithms
- Linear regression (theory and practice)
- Logistic regression for classification (theory and practice)
- k-NN for classification
- Introduction to Bayesian statistics + Naive Bayes for classification
Module 3: Regularization
- Why do we need regularization?
- Different types of regularization
- Regularization in practice (regression and classification)
Module 4: Ensemble learning
- Introduction to ensemble learning
- From decision trees to random forests
- Gradient boosting
- Adaboost
Module 5: Feature Engineering
- How to build and hyper-parameter tune KNN for Binary Classification in the cloud
- Linear and nonlinear dimensionality reductions
- Dimension reduction for features extraction
- Dimension reduction for visualization
Module 6: Clustering
- Hierarchical clustering
- Partition based clustering and k-means
- Affinity propagation
Module 7: Reporting results and preparation for interview
- Proper visualization in machine learning
- Performance report in machine learning era
- Common interview questions in data science and machine learning
Module 8: Project overview
What the attendees will learn:
- Kickstart on deep learning
- Introducing the course project
- Project implementation in class
- Q&A about the project and implementation
Ali & Farnoosh
AliAli is the lead of machine learning at Cyclica Inc and leads the team to further improve technology for predicting interaction between ligands and target proteins. As a computational biologist and machine learning specialist, Ali has worked on a series of scientific articles in high impact scientific journals and international conferences covering such fields as transfer learning, dimensionality reduction and unsupervised clustering. He earned a Ph.D degree from the University of Toronto, and a master of a mathematics degree from the University of Waterloo.
Farnoosh KhodakaramiFarnoosh 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
Feb 13, 10:00AM PST(18:00 GMT) | Sat