Name: Machine Learning Algorithms: Supervised Learning Tip to Tail
Brand: Alberta Machine Intelligence Institute
Availability: OnlineOnly
Rating: 4.7 (66 reviews)

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学生对 Alberta Machine Intelligence Institute 提供的 Machine Learning Algorithms: Supervised Learning Tip to Tail 的评价和反馈

4.7

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417 个评分

课程概述

This course takes you from understanding the fundamentals of a machine learning project. Learners will understand and implement supervised learning techniques on real case studies to analyze business case scenarios where decision trees, k-nearest neighbours and support vector machines are optimally used. Learners will also gain skills to contrast the practical consequences of different data preparation steps and describe common production issues in applied ML. To be successful, 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 second course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute....

26 - Machine Learning Algorithms: Supervised Learning Tip to Tail 的 50 个评论（共 66 个）

创建者 Kanala J

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Dec 7, 2020

Excellent Teaching!:)

创建者 RIMMON B

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Oct 1, 2020

Really cool teaching!

创建者 UPPUNURU K R

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Dec 7, 2020

Great expilination

创建者 KOTA V

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Dec 5, 2020

good for learning

创建者 Jorge M R V

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Mar 14, 2021

excellent course

创建者 AVASARALA S

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Dec 7, 2020

Learnedly well

创建者 D V R

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Dec 22, 2020

Great Course

创建者 Danilo C D C J

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Sep 17, 2020

Nice course!

创建者 RUCHITHA S K

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Sep 6, 2021

It was good

创建者 kaki m p

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Dec 15, 2020

good course

创建者 221810304033 N V V

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Aug 5, 2021

too good

创建者 KONDAPALLI D

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Nov 11, 2020

great!

创建者 Enyang W

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Feb 21, 2020

This course covers lots of important ideas and knowledges for Machine Learning practitioners. It is definitely nice to deal with topics such as grid search or scikit-learn, but I think the course only covers these topics in a nutshell, it is more superficially discussed. If you are interested in Machine Learning, you should definitely bring your own motivation to dive deeper into those topics.. Also, Dr. Koop speaks very very fast though.. I attended courses by Andrew Ng, his courses provide a way better comprehensibility for listeners. The notebooks are a bit weird, very easy to understand and are hence not challenging. If you really want to understand the algorithms deeply, I don't think this course is the right one. But all in all, I completed the course, but I don't think I was able to understand everything by taking the course only.

创建者 Luiz C

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Sep 11, 2019

Had higher expectations. Concepts not well and clearly explained. Notebooks bugged (we are actually warned about it), but even so not so interesting. Plan of the Course not so rational: why include the one section about model parameters on its own, rather than for each model.

I give it a 3 as the Instructor is smily and engaging, but it's a 2.5 mark (I have done another ML MOOC on another concurrent platform about the same topic, and the quality was much higher)

创建者 Varun M S

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Jun 25, 2020

The content was good but the videos went too fast and too much theory was involved. For a beginner it was too much to take. I was expecting some Practical and programming aspects in Quizzes and tests but that is okay. Overall a good experience

创建者 BINSHUANG L

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Dec 15, 2019

Good coverage of the topics in supervised learning. However, lacks depth in some of the concepts.

创建者 Sara K

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Sep 30, 2021

The instructor is wonderful. She does not sound like a robot/reading off of cards like so many other instructors do. However, the practice assignments are still in draft form and missing files you need in order to complete them. That is why I gave this a one star.

创建者 Alvaro V

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Jul 11, 2020

Very important concepts about supervised ML are presented. Really liked the course but a little stressed about the graded quices, even though I enjoyed very much.