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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 4.5 based on 1,753 ratings
Length 7 weeks
Effort 6 weeks of study, 5-8 hours/week
Starts Nov 11 (5 weeks ago)
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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What people are saying

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

case study approach in 54 reviews

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.

Pros: Well explained lessons and the case study approach is good to help you understand in what situations you might apply what you are learning.

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.

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looking forward in 41 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.

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ipython notebook in 25 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.

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highly recommended in 21 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!!

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andrew ng in 11 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.

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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.

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Careers

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

RESEARCH SCIENTIST (MACHINE LEARNING) $147k

Machine Learning Engineer 2 $161k

Machine Learning Scientist Manager $170k

Machine Learning Scientist, Personalization $213k

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Coursera

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University of Washington

Rating 4.5 based on 1,753 ratings
Length 7 weeks
Effort 6 weeks of study, 5-8 hours/week
Starts Nov 11 (5 weeks ago)
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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