Machine learning is the study that allows computers to adaptively improve their performance with experience accumulated from the data observed. Our two sister courses teach the most fundamental algorithmic, theoretical and practical tools that any user of machine learning needs to know. This second course of the two would focus more on algorithmic tools, and the other course would focus more on mathematical tools. [機器學習旨在讓電腦能由資料中累積的經驗來自我進步。我們的兩項姊妹課程將介紹各領域中的機器學習使用者都應該知道的基礎演算法、理論及實務工具。本課程將較為著重方法類的工具，而另一課程將較為著重數學類的工具。]
機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
機器學習基石下 (Machine Learning Foundations)---Algorithmic Foundations
Instructor: 林軒田
Access provided by Abu Dhabi National Oil Company
17,320 already enrolled
Gain insight into a topic and learn the fundamentals.
331 reviews
Intermediate level
Some related experience required
9 hours to complete
Flexible schedule
Learn at your own pace
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
2 assignments
Taught in Chinese (Traditional)
See how employees at top companies are mastering in-demand skills
There are 8 modules in this course
weight vector for linear hypotheses and squared error instantly calculated by analytic solution
What's included
4 videos4 readings
gradient descent on cross-entropy error to get good logistic hypothesis
What's included
4 videos
binary classification via (logistic) regression; multiclass classification via OVA/OVO decomposition
What's included
4 videos
nonlinear model via nonlinear feature transform+linear model with price of model complexity
What's included
4 videos1 assignment
overfitting happens with excessive power, stochastic/deterministic noise and limited data
What's included
4 videos
minimize augmented error, where the added regularizer effectively limits model complexity
What's included
4 videos
(crossly) reserve validation data to simulate testing procedure for model selection
What's included
4 videos
be aware of model complexity, data goodness and your professionalism
What's included
4 videos1 assignment
Instructor
Instructor ratings
(24 ratings)Offered by
Why people choose Coursera for their career
Felipe M.
Learner since 2018
"To be able to take courses at my own pace and rhythm has been an amazing experience. I can learn whenever it fits my schedule and mood."
Jennifer J.
Learner since 2020
"I directly applied the concepts and skills I learned from my courses to an exciting new project at work."
Larry W.
Learner since 2021
"When I need courses on topics that my university doesn't offer, Coursera is one of the best places to go."
Chaitanya A.
"Learning isn't just about being better at your job: it's so much more than that. Coursera allows me to learn without limits."
Learner reviews
- 5 stars
93.65%
- 4 stars
5.13%
- 3 stars
0.60%
- 2 stars
0.30%
- 1 star
0.30%
Showing 3 of 331
KJ
Reviewed on Oct 26, 2021
The course is moderately difficult and challenging
CC
Reviewed on Oct 7, 2021
Really great theoretical machine learning course!
WB
Reviewed on Jun 6, 2018
good explaination the foundation of all ML models
Explore more from Data Science
Fractal Analytics
Coursera
University of Washington
Dartmouth College