Volver a Applied Machine Learning in Python

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7,585 calificaciones

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1,386 reseña

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....

FL

13 de oct. de 2017

Very well structured course, and very interesting too! Has made me want to pursue a career in machine learning. I originally just wanted to learn to program, without true goal, now I have one thanks!!

OA

8 de sep. de 2017

This course is ideally designed for understanding, which tools you can use to do machine learning tasks in python. However, for deep understanding ML algorithms you should take more math based courses

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por Fabiano B

•8 de mar. de 2019

The course is a great overview of the basic algorithms that every machine learning practitioner should know. Since it has a limited amount weeks to cover such a broad subject, you will have to dig a little deeper by yourself. I found the reading material also very interesting. The final project is awesome and it will definitely make you experiment what is exactly what a Data Scientist should do.

por Ling G

•17 de ago. de 2017

This is a great course I learned a lot, especially it familiarize me with the SKlearn toolkit which is very very handy. I notice that the SKlearn documentation contains a good figure which shows a rule of thumb which learner to use. I recommend you to include in course reading, because some students might find it very useful.

http://scikit-learn.org/stable/tutorial/machine_learning_map/index.html

por Alan J

•2 de jul. de 2017

This was an awesome and engaging course. Machine Learning is a vast field with lots of ground to cover. This course gives a broad overview of all the different parts of machine learning without going too deep and also keeping everyone engaged. The assignments, especially the last one test what you learned and keeps you on your toes. A good beginner course to Machine Learning. Thank You!

por Lawrence O

•29 de jun. de 2017

Very informative about machine learning approaches ie supervised and unsupervised learning. And then goes into detail about the techniques such as regression and classification for supervised learning and clustering (K-Means) for unsupervised learning. Other techniques are discussed such as Principal Component Analysis etc.

I enjoyed it and would recommend for all data enthusiast.

por Peter B

•11 de jul. de 2018

Kevyn is an absolute joy to learn from. His enthusiasm for the topic is contagious, and his explanations are clear. The course content is well curated, tested, and reinforced. At the end of this course I feel confident that I can *actually* apply machine learning to real world problems and competitions. This is not just a 'good' course, it's a new gold standard in e-learning.

por Irene Z

•24 de jul. de 2019

Much more detailed than the previous two courses. The lecturer teaches with more verbose slides and thus gives you a more detailed overview than the lecturer in the first two courses in this specialisation. The assignments are much easier as well. But still thoroughly useful and I have to admit a welcome break from the gruelling process that typified the first two courses!

por Luigi C

•24 de abr. de 2021

The course allows you to play with a multitude of supervised (and unsupervised - optional) machine learning methods. Professor Collins-Thompson is very clear and knows perfectly well how to convey concepts and how they should be applied in real situations. I recommend it to anyone like me who needs to be able to develop simple codes and understand what they are doing.

por Shashi M

•25 de sep. de 2017

Very good course for a wide spectrum of audience interested in Machine Learning. I just had a basic learning of ML and Python, but the course was structured so well that I could catch-up. Also offers an interesting peak into Neural Networks and Deep learning. Overall, an excellent course with clear and attainable objectives, backed by high quality content and data.

por Haochen W

•1 de dic. de 2019

This is great course with very practical methods to sovle real problems in various fields. I think there should be a additional course regarding Deep learning, which I think would be very successful as well.

Moreover, this course can be combined with Andrew`s ML so that we can have both theoritical concepts and practical experience of Machine Learning in python.

por T.V.S T

•24 de sep. de 2020

This course gives you a very good knowledge how to apply machine learning techniques (mostly supervised learning) and basic things, like how to preprocess the data and what are the pros and cons of various models and which models to be used based on the kind of data given, and many more basics which are required for a deeper understanding of Machine Learning

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por xixicy

•10 de abr. de 2018

The content (slides, python scripts) is very structured. The lecturer explained very clearly. The reference articles were super inspiring. Also, the assignment is very well designed and relevant to what's covered (in comparison, some other courses might have very difficult assignments which need much more self-learning and cause frustration). Thank you!!

por Benjamin M L

•14 de mar. de 2018

Excellent course, easy to understand, useful and enjoyable to do! Two minor comments: it took me a longer than the estimated times to complete the Quizzes; I have Python programming proficiency and a small amount of background in Machine Learning. I would have preferred the final assessment to have an extension to it which required a more advanced model.

por Fabrice L

•24 de jun. de 2017

Great course!! And this field of science/technology is fascinating.

The only comment that I would do is that it might have been useful to include a whole pipeline on the creation of a simple machine learning software from the data collection to the end result. I guess that is the goal of the next course on text processing, so I'm looking forward to it.

por David V

•28 de jul. de 2017

Excellent course!

Machine Learning is today a buzzword and you do not really know what it is until you do it. The University of Michigan has put together a great program that takes you from the basics of Python to the latest Machine Learning techniques.

I started without knowing Python, and well, I cannot say that it has always been easy, but I DID IT!

por Alexander T

•1 de jun. de 2019

Thank you all for such an awesome series of courses.

I find these courses really challenging, especially the final assignment. But it is rewarding too, coz you feel, that you CAN solve such tasks in real life too.

Thank you Michigan team for such efforts. During the last 1.5 years I managed to progress from 0 programming knowledge to solving ML tasks

por Callum Z Y Y

•11 de feb. de 2020

It was a good introduction to machine learning. The assignments and quizzes were well designed to encourage self-learning, which in my opinion is one of the most valuable skills an aspiring data scientist could learn. All in all I am very satisfied with the course and I look forward to enrolling in the other courses in the specialization.

por Binil K

•10 de jul. de 2017

This is a very nice course in Applied Machine Learning. For getting the most out of it, it would be nice to have taken ML Specialization from Andrew Ng which will take a deep divce into the working of ML models or have good amount of knowledge in ML. Having familiar with ML concepts, you would find this course really useful.

Regards,

Binil

por Pranav S

•2 de jul. de 2020

It was great learning experience.This course exposed me to various parameters of machine learning using python programming and helped me to gather knowledge about the significant use of Pyhton Programming in the field of machine learnig.Pandas,Regression topics are rightly and deeply understood to me because of this course.

THANKYOU!!!

por Mustafa K

•22 de sep. de 2017

This is the most useful machine learning course in the internet. It helped me to understand machine learning algorithms very well that I never saw in other courses. This course covers most of the machine learning algorithms that needed nowadays. Thanks to Michigan University and Coursera to make this course to be available online.

por Zhao H

•21 de jun. de 2018

Highly recommended. Great practical overview of machine learning approaches.One shouldn't expect the underlying implementations from this course due to the time strain - only a few weeks, and should take Andrew Ng's machine learning class for that.To go even deeper for some methods, one should take more machine learning classes.

por Martyna S

•16 de nov. de 2017

Very interesting and engaging course. I liked graphical comparisons of different models and their params. Module notebooks were very handy while doing assignments. All homeworks were not trivial, developing and demand attention to detail. Big plus for teachers posts at forum - they help a lot while doing quizzes and assignments.

por WILVER S Y

•1 de may. de 2021

It is a wonderful course that convers a basics of Machine Learning, the instructor provides an excellent explanation of the topics and the Jupyter Notebooks helps you understand and document all the concepts learned through the course. If you want to build some knowledge in ML this is a good option to start this great journey!

por Steve M

•15 de abr. de 2018

An excellent overview of current machine learning knowledge and practices. This course is very information dense and requires additional reading and time for the assignments. It is challenging for an 'intermediate' level course. Some prior knowledge of machine learning is recommended, and strong Python skills are required.

por Juan D

•15 de jun. de 2020

Very applied course, while still teaching you the basic concepts. You can start using machine learning solutions to your problems right away with confidence. The course covers a lot of ground, so expect some topics to be treated rather superficially. It provides a lot of material if you want to expand your knowledge though.

por Lewis M

•13 de ene. de 2019

Very good course for either an introduction to machine learning or to refresh old skills. It's also very good at putting emphasis on topics that data scientists may overlook / not pay much attention too, so having this as a reminder to look deeply into each algorithm and its application or limitations is incredibly helpful.

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