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Applied Machine Learning in Python

This course is a part of Applied Data Science with Python, a 5-course Specialization series from Coursera.

This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. The course will start with a discussion of how machine learning is different than descriptive statistics, and introduce the scikit learn toolkit through a tutorial. The issue of dimensionality of data will be discussed, and the task of clustering data, as well as evaluating those clusters, will be tackled. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. cross validation, overfitting). The course will end with a look at more advanced techniques, such as building ensembles, and practical limitations of predictive models. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.

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University of Michigan

Rating 4.5 based on 336 ratings
Length 5 weeks
Starts May 6 (next week)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Business
Tags Data Science Data Analysis Business Leadership And Management

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What people are saying

We analyzed reviews for this course to surface learners' thoughts about it

learned a lot in 15 reviews

learned a lot !

I learned a lot, but through pain and struggle.

learned a lot!

Learned a lot from this course.

I was excited going into this course because the others in the series were taught well and I had learned a lot.

Learned a lot in this course!

to give the learner a good over view of how to apply ML theories into action very good This is a great course I learned a lot, especially it familiarize me with the SKlearn toolkit which is very very handy.

I've learned a lot.

andrew ng in 11 reviews

Interesting course, similar to Andrew Ng's machine learning course, but covers a slightly different spectrum of topics, and skips things like inner workings of gradient descent in order to have more of a focus on practical aspects of sklearn and python.

Like in Andrew NG course or in Text Mining.

The perfect python complement to Andrew Ngs machine learning course.

Well presented and good organized python notebooks, quiz and assignments.Enjoyed the project very much.Looking forward for future classes this is an interesting machine learning coursecan quickly understand the basic idea of machine learning and know how to build different models in python and select models based on different standardsit is a very good course to start with machine learning and can arouse the interests of learning more in this emerging field It is a very practical course if you have learned the Andrew Ng's Machine Learning course.

Combined with Andrew Ng`s course on Machine Learning it`s great foundation for futher development as AI specialist.

Perfect and hard course than Andrew Ng's ML course!

I took Andrew Ng's Machine Learning course before this one, which I would highly recommend!

It's a perfect checkpoint after Andrew Ng's machine learning courses, by making experimental practices over theoric practices.

real world in 7 reviews

Challenging but worthwhile mix of essential theory (explained well) and hand-on practice with good, sensible exercises to help one get a confident grasp of scikit learn packages which one can use in the real world.

It demystifies the topic of machine learning and provides a perfect introduction how to approach real world problems.

The use of Scikit Learn helps to give a concrete understanding of ML as well as how many specific algorithms can be utilized in real world problems.

Great professor with lot of real world experience.

Very good weekly assignments allow students to well consolidate course contents on real world practices.

I mean if the goal is to train our to do some real world data you may can shrink the dataset, the large dataset would takes more time to training which would cost more time to debug.

A course that gives not only solid understanding of Machine Learning, but provides with skills to actually practice it on real world datasets.

rather than in 7 reviews

It provides a great foundation for the rest of the courses in this specialization, but I wish other courses would be developed in collaboration with this intro course, rather than a series of independently designed courses.

Seems as if they were concerned in covering a specific amount of topics rather than making the concept of machine learning more approachable.

I do have the math background to follow the proofs, but I would rather spend my time doing rather than proofing.

The lecture content here structured to discuss broader machine learning concepts, rather than setup to follow along in the notebook.

Autograder is not too verbose and as a result I spent some time struggling with debugging the code rather than figuring out how to solve machine learning related problems.

Finally, the accompanying Jupyter Notebooks are very helpful and there are many helpful links to outside resources as well.A few of the lecture videos feel like an early draft rather than production-quality, with lots of time spent on repeating phrases.

The video lectures are very much to the point and this course is especially useful for someone who is more interested in application of Ml algorithms rather than their development.

familiar with in 7 reviews

It is helpful for me to be familiar with scikit-learn Awesome Insturctor Comprehensive and interesting course in Machine Learning.

By this course, one can get familiar with sklearn and pandas basic operation!

Homework assignments can get complicated, and you should be familiar with advanced data structure manipulation in pandas and numpy to make progress.

In the four week, I think I am not familiar with most of these method and I need to practice more in the future.

Having familiar with ML concepts, you would find this course really useful.Regards,Binil I am a beginner in Machine Learning.

Excellent clarity, recommended for getting started with ML Excellent course for someone who already has some knowledge of python but not quite familiar with machine learning.

Maybe if you're already familiar with linear regression, it's not as hard to follow.

very informative in 7 reviews

I enjoyed this course it was fun and very informative.

One of the very informative from the basic to intermediate course.

If you are expecting more theories and understanding of the algorithms, this one may not for you Very informative & highly useful.

Very informative and covers Machine Learning (along with scikit learn) in great breadth!!

Ist gut Very informative but bit too difficult.

Very informative and educational A very good course that I would recommend for others to take.


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University of Michigan

Rating 4.5 based on 336 ratings
Length 5 weeks
Starts May 6 (next week)
Cost $79
From University of Michigan via Coursera
Instructors Christopher Brooks, Kevyn Collins-Thompson, Daniel Romero, V. G. Vinod Vydiswaran
Download Videos On all desktop and mobile devices
Language English
Subjects Data Science Business
Tags Data Science Data Analysis Business Leadership And Management

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