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Machine Learning Foundations

A Case Study Approach

This course is a part of Machine Learning, a 4-course Specialization series from Coursera.

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.

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University of Washington

Rating 4.4 based on 2,176 ratings
Length 7 weeks
Effort 6 weeks of study, 5-8 hours/week
Starts Feb 17 (last week)
Cost $79
From University of Washington via Coursera
Instructors Carlos Guestrin, Emily Fox
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Machine Learning

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According to other learners, here's what you need to know

case study approach in 67 reviews

Machine Learning Foundations: A Case Study Approach is a 6-week introductory machine learning course offered by the University of Washington on Coursera.

I can't say that the case study approach is different from other data science courses that I have participated in, but the lecturers present the concepts of machine learning in a clearly explained and memorable way.

I really like the case-study approach very great course in a case study approach, you will be familiar with all basic algorithms and ML methods.

I enjoyed the course and the fact that it uses Python for the exercises I like the case study approach.

Very interesting way of exposing concepts using case study approach which makes it more engaging and useful.

The concepts were very easy to grasp and I endorse the case study approach as a effective introduction to complex topics.

But overal very good course I really liked case study approach.

The best thing for the course was the case study approach.

ok Awesome Course for starting out very Basics of Machine Learning with an easy going approach- Case Study Approach.

Loved the case study approach and how it relates to real world problems.

the case study approach is better for understanding the material.

I love case study approach for the machine learning foundation.

I do like the case study approach which allows a grasp on the real application of ML methods.

I think this is because this is a case study approach and like an introductory course.

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looking forward in 43 reviews

Really enjoyed it and looking forward to new courses Excellent course to get started.

Looking forward to taking the next one.

I am looking forward to the next course to begin implementing and of course, understanding more thoroughly these concepts.

Looking forward to dig deeper into it.

Looking forward to the other 3 courses in this series.

Looking forward to the next courses in the specialization!

Good introduction to machine learning concepts and I'm looking forward to a deeper dive in later courses.

Looking forward to the rest of the specialization for a deeper work with the math and algorithms behind the various techniques covered.

looking forward for the next courses in this specialization.

I am looking forward to complete my specialization Great course for starting ML A good introduction to ML applications, but not as detailed and thorough as I expected.

I'm looking forward to the following of the specialization.

Looking forward to the next courses in the Specialization.

Looking forward to the next course in ML specialization!

I'm looking forward to complete the specialization.

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ipython notebook in 29 reviews

With the IPython notebooks that are already filled in complementing the teaching, everyone can appreciate the applications of machine learning.

Using the coursera iPython notebook did not work because of issues with the GraphLab key you have to individually obtain.

The learning methodology based on study cases is amazing and gripping and the ipython notebooks used in the practical sessions are very instructive.

Really enjoyed working through the IPython notebooks!

Nice Experience Learned iPython Notebook which is good for Machine Learning.Helped me to understand the basics of all the ML techniques and helped me understand where to apply which ML model.

I really enjoyed this course and found it fun to use the iPython notebook to play around with the ML models.

Found the Turi APIs and iPython Notebook approach very effective in getting acquainted to machine learning algorithms.

Very practical by using iPython Notebook Ótimos instrutores e material.

One gets a hang of the ipython notebook and graphlab create environment too.

This course is a greate introduction towards machine learning and the package used graphlab create is really easy to use as well This is a great course i do recommend it for students ready to learn the new way of working with data and not bunch of IF..Else statements Course combines Real Word Applications with simple implementation via IPython Notebooks.

it is an amazing course and a good gate for Machine Learning Good overview of ML methods combined with a gentle introductions to Python and iPython notebook A really good course for beginners who want to understand the concepts and not the underlying algorithms I really liked the case study approach.

I thought it was friendly enough to be appropriate for this kind of intro class, and I really enjoy iPython notebooks for interactive teaching.

IPython notebook and graphlab are amazing tools.

It is really well done, that you have a theory part and a practical "case study" part, were you can follow along with the provided IPython Notebooks.

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highly recommended in 25 reviews

Highly recommended.

Amazing course on Machine Learning.I have tried other courses on Machine Learning but none has made it so simple for me as this course.I started other courses but at some point I was stuck but this course explains all concepts so easily and gradually .Highly recommended for anyone who want to start learning machine learning.Even if you do not have programming experience, its easy to follow.I congratulate both the instructors Emily and Carlos for making this brilliant course.My most favorite part of this course is when Emily is trying to pronounce the name "Pele" and Carlos corrects here lol.

I have learned the basics about Machine Learning in a simple way :) Highly recommended.

also, there are some errors that make it hard to understand the last week's material, but other than that, it's ok. highly recommended This course gives the kick start needed to start a data science career.

Highly, highly recommended.FYI: the Python level required is really minimal, and the total time commitment is around 4 hours per week.

Highly Recommended!

Highly Recommended!!

Highly recommended for beginners!

Interesting real-world problems on real-world data.Highly recommended.

Excellent course, highly recommended.

Great class overall and highly recommended for those with some basic Python skills and a desire to see what machine learning is capable of.

Highly recommended!

Real life applications....thrust them till the end (even if I'm not comfortable learning from such good but branded teachers) Highly recommended.

Its highly recommended for the students who are completely new to the Machine Learning.

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university of washington in 10 reviews

So when I saw this University of Washington specialization and read that they use Python, I was very excited.

I am really appreciative of the time and efforts on the part of the instructors and the University of Washington to make Machine Learning very accessible.

Thanks to Course era and University of Washington for providing a wonderful opportunity.

I am Jun Qi, a Ph.D. student in the department of Electrical Engineering at University of Washington.

I enjoyed learning with good content of videos and do Enhance my knowledge in ML and skilled me to do best Research in my MS Study, Thanks to COURSERA and University of Washington to give financial aid to learn Machine Learning.

Regards, Pratyush Excellent course on the basics of Machine Learning by University of Washington Awesome course will maximum practical applications Good start for ML beginners Very interesting course.

primitive course, didn't expect this low standard from university of Washington One of the best in the business covering all the basics in a very concise and understandable way.

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andrew ng in 12 reviews

I had already completed Andrew Ng's Machine Learning course (Coursera/Stanford), and a couple of courses in the Data Science specialization (Coursera/Johns Hopkins).

Although I loved Andrew Ng's course, I was looking for something more in-depth and a little more useful in my daily work than Octave or R, which are the languages used in these other Coursera courses.

The GraphLabCreate software was neat to see and easy to use, but ultimately I preferred the more first principles approach of Andrew Ng.

However, if you look at some background and practical implementations, Andrew Ng's course is the one to go.

If you are considering this specialization I would recommend the Andrew Ng course instead and the main reason is that it isn't depend on proprietary ML framework.

If you already attended the Machine learning course from Andrew Ng or you have some idea of what is Machine learning about, this is the perfect next step.

You'll be much better off taking Andrew Ng's course, which is significantly more in depth and forces you to write your own solutions to problems instead of relying on a proprietary library.

Big plus for the use of python + notebooks but otherwise, if one is interested just in the overview and not in all the specialization, maybe the Andrew NG course is more detailed.

Having followed several Machine Learning courses, this is now definitely my favourite new course, replacing Andrew Ng's famous course here on Coursera (which was also very good & especially complete, but required too often a leap of faith - this course provides really more details on the "why").

).If you wish to learn machine learning, take the Stanford course on Machine Learning for Andrew Ng.

I appreciate this course that is one of the best mooc courses I ever done.Being a computer science engineer, I found the "hands on" approach particularly amazing, it lets you immediately start applying the topics with real use cases.I started this course after completing the Machine Learning course of Andrew NG (Stanford University), and this made a perfect companion.I think that if I didn't have followed the NG course, probably I would miss something from the theory, particularly maths and statistic that in this course are missing.

This stream along with Andrew NGs is the best ML course available in Coursera.

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real world examples in 8 reviews

The course introduces most of the basis of machine learning in a very simple and clear way illustrated by real world examples Good beginning course.

The best thing is that it shows how machine learning is applied in real world examples This course is very useful for me as a ML beginner.

The course covers wide range of topics along with the real world examples in simple language , The assignments are superb, they help you to grasp the concepts in much detail and sets a strong foundation .

Easy and simple... perfectly explained.Thank you very good for one who has no idea about machine learning , but I dont like dato Good primer to Machine Learning - uses real world examples to introduce different machine learning concepts in an interesting way.

Kudos to the instructors Emily and Carlos for providing a well laid out syllabus with an approach that was grounded on practical concepts and demonstrating hands on with real world examples.

Course was pretty easy to follow and the real world examples helped to visualize the applications of Machine Learning.

Using real world examples was amazing.

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black box in 11 reviews

no capstone) good While the black box approach makes it easy to understand and grasp at a use case level, I missed some of the intuition associated with how these algos get the work done.

Good intro to ML, but would've enjoyed less of the "Black Box" approach in using Graphlab.

Very simple black box approach to ML.

Ok, the platform offered makes things easier, but if you really want to learn machine learning, you can not be limited to a platform, acting as a robot just using pre-written functions in a black box.

But it was an alright introduction to machine learning but not enough if you want to know what makes the 'black box' work.

All algorithms were black boxed.

Makes sense to approach it as a black box and then take deep dives in later series.

By using graphlab as a black box and focusing on specific applications, I really understood why these techniques are useful.

However, the exercises are very general and use 'black box' ML algorithms for most of the solutions.

The machine learning methods covered aren’t necessarily treated as complete black boxes, but the course intentionally avoids getting too deep into the details, putting the emphasis on conceptual understanding.

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capstone project in 11 reviews

Always advisable to have some basics on python , data frame , machine learning(if possible) and you will go really smooth with this intermediate level course.Course material really good for machine learning with real case studies and capstone project on deep learning was indeed the crown of the course.

the course contains misleading information about a capstone project that I discovered -by coincidence - that is no longer exists, the video introduction and the final videos is mentioning the capstone project time and again !

I wish you to provide us with at least IPYNB for the capstone project because that will help us a lot.

But just wondering why the capstone project is removed from the course specialization?

Actually you could even skip this specialization since they canceled the capstone project so investing any money and time here is a waste.

I am interested to continue in this specialization and conduct the Capstone project.

would be perfect if it didnt lie to me saying there is a capstone project and courses 5 and 6, i wish Amazing Learning approach.

good without the missing course and capstone projects The Deep Learning part needs to be improved Great course and best wishes for Carlos and Emily!

Need some more practical Capstone Projects... Had a blast.

Will continue the course and get my certification and enroll for capstone project as well.

I am really looking forward to complete this specialization with the Capstone project.I know how hard it can be to prepare an entire specialization like this.

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good starting point in 6 reviews

Great course to begin your journey into MLBriefly introduces each topic to give a jist about it and also provides a good starting point for using python in ML context what an awesome mooc.

But this is a good starting point for anyone who wishes to complete the specialization.

Great course Very nice overview of the field 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 ;) Great overview with some implementations of what will be covered through the specialization.

The course is really very basic but can be a good starting point.

Good Starting point for freshers who got interest towards Machine Learning The instructions to download GraphLab don't work and even when you sign to use the AWS platform the instructions are also old and I haven't been able to start any of the assignments because of that!

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recommender systems in 9 reviews

The course provides a broad overview of key areas in machine learning, including regression, classification, clustering , recommender systems and deep learning, using short programming case studies as examples.

This course provides a broad overview of many of the topics in machine learning such as regression, classification, deep learning, and recommender systems.

The tools they provide to examine the material are useful and they stretch you out just far enough.My only regret/negative is that they were unable to complete the full syllabus promised for this specialization, which included recommender systems and deep learning.

For example the recommender systems quiz and programming assignment have nothing about factorization except a single superficial question.

Because anyways, after 4 months in the specialization, if somebody continues to the recommender systems module for example, he/she would have forgotten the basics of this so they would need to cover again the recommender systems week in this course.

Excellent overview course, introducing the ideas of regression, classification, clustering, recommender systems, and a sort of 'short cut' of using the early layers pretrained deep neural network for image recognition as feature inputs into a classifier.

jaccardian similarities, recommender systems (precision/recall, AUC), deep learning via transfer learning (not having to explicitly build a model for the problem).

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discussion forum in 9 reviews

a lot of comments in the discussion forum were helpful for me to complete the quiz but lots of feedback suggested improving the lessons to match the quiz or vice versa.i think it would also help upon submitting the quiz to display the answers you chose , not jsut whether they were correct or not Easy to follow and a lot of usefull information that really gets you excited for the upcoming courses.

Need a bit more participation of mentor/TAs in discussion forum Great course.

I am giving a 3-star rating as i) the lectures need to be updated with correct data or need to provide guidance as to when one should expect individual difference when following along with the notebook, ii) instructor / mentor response in the discussion forums is lacking, iii) graphlab is now an outdated tool as it is not commercially available.

Excellent presentation of material Good for ML newbie Instructors or TAs are not available in discussion forums.

There are lot more statisticians out there who are heaps better than myself and it was excellent experience to read the questions and answers in the discussion forum.

However, I would have preferred something more challenging, even for a survey course.Though the discussion forums were generally very active (I posted at least one question that was answered by a TA very promptly), there was a question I had towards the end of the course that never got answered.

I thoroughly enjoyed the course and discussion forum was very helpful to progress.

However few improvements is required to have better user experience 1) The content should be upgraded to latest python 2) Since the course recommends Graphlab, there should be up to date detailed instructions to install the same ( disclaimer that this course is not about not helpful to progress) 3) No response is discussion forum - there's no responses provided to the queries.

I do understand many questions in discussion forum is repetitive and someone may find the answer by scrolling through long unstructured hundreds of responses, however this is not efficient.

Recent comments on the discussion forum no longer receive a response.

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An overview of related careers and their average salaries in the US. Bars indicate income percentile.

Research Scientist-Machine Learning $55k

Cloud Architect - Azure / Machine Learning $75k

Watson Machine Learning Engineer $81k

Machine Learning Software Developer $103k

Software Engineer (Machine Learning) $116k

Applied Scientist, Machine Learning $130k

Autonomy and Machine Learning Solutions Architect $131k

Applied Scientist - Machine Learning -... $136k


Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

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University of Washington

Rating 4.4 based on 2,176 ratings
Length 7 weeks
Effort 6 weeks of study, 5-8 hours/week
Starts Feb 17 (last week)
Cost $79
From University of Washington via Coursera
Instructors Carlos Guestrin, Emily Fox
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Programming
Tags Data Science Machine Learning

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