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学生对 Alberta Machine Intelligence Institute 提供的 Data for Machine Learning 的评价和反馈

4.4
56 个评分
15 条评论

课程概述

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute....

热门审阅

EG

Jan 09, 2020

The whole specialization is extremely useful for people starting in ML. Highly recommended!

MM

Dec 01, 2019

What is different about this course is its focus of ML applied to the real world.

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1 - Data for Machine Learning 的 15 个评论(共 15 个)

创建者 Emil K

Mar 22, 2020

The instructor is great, but please fix the programming assignment! There are so many typos it's embarassing. Also, the autograder EXPECTS typos in some variable names, so you can't even pass it if your answers are correct.

创建者 Hari N L

Jun 26, 2020

The experience with the programming assignment was very bad. There was an error that was occurring at frequent intervals which crashed my jupyter notebook, making me to start afresh. I was facing an issue in reopening the notebook where it took a long time and the mathematical notations were also not loaded properly.

创建者 Emilija G

Jan 09, 2020

The whole specialization is extremely useful for people starting in ML. Highly recommended!

创建者 Camilo A C F

Jul 05, 2020

Good course, if you follow the previous ones and if you know some python (Pandas).

创建者 Miguel A S M

Dec 01, 2019

What is different about this course is its focus of ML applied to the real world.

创建者 Naruki H

Jul 17, 2020

Excellent content with good programming assignments and examples.

创建者 Tony J

Jul 17, 2020

This is the best!!!

创建者 Valerii M

Mar 31, 2020

Nice course!

创建者 Pratama A A

Jun 09, 2020

Well this course absolutely good,but you need patience when doing programming assignment,and there's a lot error tho,but what we need is that information,anna gave us the easiest insight

创建者 SHREYAS C

Jun 12, 2020

Really good,... one thing you have to change is that your assumption of people knowing Python for Jupyter Notebook really well... the week 3 assignment was a pain for quite sometime

创建者 Abdullah A

Dec 24, 2019

the course is very powerful and I have jump to higher level regarding data wrangling and how to deal with data. the assessment have some error which can be fixed easily

创建者 Danijel T

Jul 22, 2020

The instructor is knowledgable and materials are moderately useful.

Notebook with assignment is broken. There are many typos and elements which are not rendered properly. Notebook is huge and every subtask depends on previous state. It takes time to reload all previous tasks if you did not solve everything in one go. Final quiz basically repeats all the questions from previous quiz.

Course could use more polish.

创建者 Halil T

Sep 18, 2020

deeply theoretical but excellent assignment file (good review for pandas library )

创建者 Jhon F B L

Apr 26, 2020

The course is great but the courser a notebooks were a nigthmare

创建者 Lam C V D

Aug 26, 2020

Bad Grader system and complicated coding taught. Instructions given unclear and no instructor support at all.