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Avis et commentaires pour d'étudiants pour Machine Learning Foundations: A Case Study Approach par Université de Washington

13,085 évaluations

À propos du cours

Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied how to predict house prices based on house-level features, analyze sentiment from user reviews, retrieve documents of interest, recommend products, and search for images. Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box. Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. -Describe the core differences in analyses enabled by regression, classification, and clustering. -Select the appropriate machine learning task for a potential application. -Apply regression, classification, clustering, retrieval, recommender systems, and deep learning. -Represent your data as features to serve as input to machine learning models. -Assess the model quality in terms of relevant error metrics for each task. -Utilize a dataset to fit a model to analyze new data. -Build an end-to-end application that uses machine learning at its core. -Implement these techniques in Python....

Meilleurs avis


16 oct. 2016

Very good overview of ML. The GraphLab api wasn't that bad, and also it was very wise of the instructors to allow the use of other ML packages. Overall i enjoyed it very much and also leaned very much


18 août 2019

The course was well designed and delivered by all the trainers with the help of case study and great examples.

The forums and discussions were really useful and helpful while doing the assignments.

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451 - 475 sur 3,043 Avis pour Machine Learning Foundations: A Case Study Approach

par Aradhika N

21 juin 2017

Love how the modules are broken down into small segments of 3-5 minutes on an average. Makes it easier and definitely not monotonous as compare to other courses. The professors are amazing!

par Mahmoud A E

28 févr. 2016

The top-down approach of this course is the best way to understand concepts and view solutions for real-world applications. This way I can go deeper after understanding why I am doing this.

par Nagendra K M R

22 sept. 2018

Explanations are provided in detail which helps even the beginners to master the Machine Learning. Case studies are very interestinghelpful to master the concepts and gain the confidence.

par Robert R

25 mars 2018

A running Jupyter notebook with working examples. Very nice. I couldn't get my local system setup the way they explained, probably because my Python is 3.x is newer than 2.x. Not sure.

par Dauren

22 déc. 2017

Gives a good overview of tools and models used in Machine Learning. Once taken this course, you will have a general knowledge of domain upon which Machine Learning methods can be applied.

par Ramy S

22 juin 2019

Excellent course. I am currently working at and find that this is a perfect supplementary course that will allow a professional to solve business problems. I highly recommend.

par Joseph L

28 févr. 2016

Had a blast. I have no background in ML whatsoever. But the tools, concepts and exercises presented is really interesting and really help set the mood for the rest of the specialization.

par Rogelio Z R

3 déc. 2015

Emily and Carlos are amazing! The course is well laid out, specially as part of the specialization, taking the regression course would have been different without the foundations course.

par Francesco P

16 mars 2021

Nice introduction to DL, easy to follow with the suggested turicreate or any other framework.

IT is juts a pity that the specialisation this course belong to will no longer be completed.

par Prabuddha K

2 avr. 2017

Brilliant overview. Many thanks to the teachers for designing such a comprehensive overview. This course must be followed by all the others in the specialization for best understanding.

par Richard K

16 déc. 2016

Great course, really well designed and with some interesting real life case studies. Lectures are clear and informative and the assignments help cement your understanding of the content

par James P

27 nov. 2016

Very nice overview / introduction to machine learning. Setting up the environment initially was annoying but well worth the effort to be able to analyze/solve more realistic use cases.

par Hassan F

8 févr. 2016

Great overview of basic ML concepts in different situations along with hands on exercises. It was really helpful, with examples and little programming challenges that help learn easily.

par Bilong C

29 déc. 2015

This is very great course to get students introduced to different machine learning algorithms before digging into the details. And the Graphlab used in the course is really easy to use.


18 janv. 2022

Very good, thorough and helpful! has a good mix of theory and practice. However, the assignments can often be tough to complete and often need extra reading and/or knowledge to tackle.

par Jay

15 août 2020

great starter course to dive into machine learning. it gives you some idea on type of the problem that ML can handle. not much details, though! they are left for the following courses.

par Neha R

25 mai 2017

It's a really good course and covers all the basics extensively.

It is well structured and the case-study approach actually helps understanding the topics in a better manner and easily.

par 龙腾

1 juil. 2016

It's an very interesting and intuitive course. But using the Graphlab Libarary require more CS background. this course should add more document and instructions on how to use Graphlab.

par Kim K L

11 déc. 2015

This is a really really great course ... and that the professors appear to really enjoy teaching and are fun fun to watch and learn from is an additional bonus. Keep up the great work!

par Barış D S

14 févr. 2016

Great course, great framework, thank you. But in my humble opinion, the lecture videos are too short. Lectures are generally divided into several videos, covered a lot of transitions.

par 向韵桦

31 janv. 2016

It's really helpful to pull back and have a overall look at these algorithms. Especially, the professors gave a very clear talk and explanation which made this course more impressive.

par rambarki g

11 mars 2018

This was a awesome moment for me it was really cool. The people of course era i love them .Thank you so much for financial aid. Keep supporting people like thank you thanks a lot!!!!

par Wenxin X

25 févr. 2016

In my opinion, the course is well designed. I generate a rough idea about the basic concepts of machine learning through it. These concepts are important but made easy to understand.

par Jerome G

28 déc. 2015

Excellent overview of machine learning technique !

Even if the subject is complexe, it's easy to understand, and a good starting point to go deeper, as a deep human learning can be ;)

par Udaibir S B

11 mai 2020

The course was up to the mark, the quality of the assignments and quiz was also good which created the course more interesting to learn and learned many new things with this course.