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Applied Machine Learning in Python

Applied Machine Learning in Python

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8.9/10 (Our Score)
Product is rated as #56 in category Machine Learning

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 …

Instructor Details

Kevyn Collins-Thompson is an Associate Professor of Information and Computer Science in the School of Information at the University of Michigan. He works on developing algorithms and systems for effectively connecting people with information, especially for educational goals. This involves bringing together methods from applied machine learning, human-computer interaction (HCI), and natural language processing. He also has more than a decade of industry experience as a software engineer, manager, and researcher.

Specification: Applied Machine Learning in Python

Duration

24 hours

Year

2017

Level

Intermediate

Certificate

Yes

Quizzes

Yes

85 reviews for Applied Machine Learning in Python

4.5 out of 5
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  1. Alpan A

    Very good curriculum with a hands on project. However thera are some limitations with the platform with grading

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  2. Yang L

    love the final assignment. Had great fun!

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  3. YYuan

    This course involves lots of concepts and algorithms in machine learning. As it is said by the teacher, for time, effort and aim limitations, this course only involves basic concepts and usage of sci kit learn. It is a good hand on course for beginners. Assignments are not so challenging compared with the previous two courses in the same specialization. I just finish assignments by following the module code in the course. I feel like not study as much as I expected through the assignment. I hope assignments can be changed by varieties and difficulties to let students know how a machine learning project is like and how the evaluation works but not simply call the precision/accuracy/recall function and the assignment finishes. Generally, you still learn a lot if you want ‘applied machine learning’

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  4. Ashish C

    This is the best course for machine learning. Assignments are really good. It make sure you know all the things that are taught to you. Even some times I had to go through the lectures again to complete the assignment.

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  5. RICARDO D

    Excellent material for intro to ML

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  6. Joshua A

    An excellent overview of Machine Learning in Python.

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  7. Archunan G

    Course is interesting and nice . quiz made well .

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  8. Radha S

    Excellent course !

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  9. Benjamin C

    Good class with a lot of interesting material. However consider correcting some issues like, each exam we are told to read file from the folder readonly and each time I got 0/100 at my first submission because the file was not in this folder!! Anyway, quality was present.

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  10. Rahul S

    This course is Beautifully crafted to cover most of the important concepts of supervised machine learning.

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  11. Mario P

    I struggled with this course. The lectures cover a great deal of information extremely fast. I appreciate that there are more lectures than in previous courses in the specialization and the information is better presented IMHO. The assignments were quite difficult and I struggled. Relying heavily on discussion forums and online posts.

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  12. Vikas K

    best course in detailed version

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  13. Haris P D

    A great course!

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  14. Boyan Z

    A very useful course that gives very good overview for the applied side of machine learning for solving various problems.

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  15. Carlos D R

    The course offers you a lots fot tools the face ML problems. There are few errors in the notebooks, but everyting is well documented in the forum. Good overview to represent data and train basic models.

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  16. Marshall

    I learned a lot about machine learning with python and would definitely recommend for someone with decent python background.. Some of the assignments have some very unnecessary technical hurdles that are unrelated to the material.

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  17. Shashwenth.M

    Seriously THE BEST for gaining a broad knowledge about machine learning techniques in a applied manner.

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  18. Oumeyma F R

    What I loved about this course is the consistency of its content and the quality of its presentation.

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  19. Amit A

    The course is excellent and Professor Kevyn Collins Thompson goes to the lengths and breaths to explain various machine learning algorithms and also provides a hands on the syntaxes for the code to provide a deeper intuition to the problem. The course has a lot of info to be digested and one must go at his/her own pace to grasp all the details. There were some issues with the grader but thanks to the excellent mentors on the decision board, they helped me sort out all the issues. So thanks to the entire team once again.

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  20. Andrew R

    The Applied Data Science with Python specialization continues to deliver with Applied Machine Learning. Both quizzes and assignments are challenging but exceptionally well architected. I’m walking away with a great deal of beginner to intermediate skills in machine learning and scikit learn!

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  21. Amit P

    This course is an excellent run through of the pipeline for developing, running and evaluating machine learning models. The video lectures were monotonous and long, though. The last assignment was especially meaningful and enjoyable. Highly recommended.

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  22. vipul k s

    Really good course. The instructor taught in a very precise way. The teachings were spot on and comprehensive. After this, now I can start to work on real life projects.

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  23. Miriam Y R L

    good

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  24. Gourav S

    It can be more detailed. It is on broader terms only. I will recommend Andrew Ng ML course to do as well because it covers too many things than this module. Otherwise, this is a good module as well. 🙂 Enjoyed doing it.

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  25. ANIMESH T

    great course

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  26. Darshan S

    Not enough real life examples throughout the video, makes it very hard to concentrate during the whole lecture.

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  27. Tony L

    Great exercise!

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  28. Ram N T

    The course material and Professor Kevyn Collins Thompson is awesome. A person who’s seeking to learn ML should try this course.

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  29. keshav b

    Instructor tell the thing which are far beyond from asignments and quizes

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  30. Kuleafenu J

    I have rely enjoyed this course because it is very informative

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  31. Pakin S

    How can i pass without reading discuss about problem with notebook

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  32. Vinit D

    Excellent Course!

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  33. Pakin S

    How can i pass without reading discuss about problem with notebook

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  34. Vinit D

    Excellent Course!

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  35. David W

    A good introduction to Scikit learn

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  36. David W

    A good introduction to Scikit learn

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  37. Deepak

    Very Good

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  38. Deepak

    Very Good

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  39. Ankush G

    A good stepping stone towards a career in data science.

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  40. Ankush G

    A good stepping stone towards a career in data science.

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  41. chris l

    An excellent course if you are prepared to be patient and do lots of additional research outside the given content.

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  42. Sonmitra M

    The course content was good and the assignments were designed brilliantly. I learned more while completing assignments and reading discussion forums. The auto grader should be improved, it’s time wasting and frustrating experience. No response from discussion forums even on technical issues can keep you waiting for weeks unless you solve the issue by your own by reading 2 3 years old post and meanwhile lost money, time and patience.

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  43. chris l

    An excellent course if you are prepared to be patient and do lots of additional research outside the given content.

    Helpful(0) Unhelpful(0)You have already voted this
  44. Sonmitra M

    The course content was good and the assignments were designed brilliantly. I learned more while completing assignments and reading discussion forums. The auto grader should be improved, it’s time wasting and frustrating experience. No response from discussion forums even on technical issues can keep you waiting for weeks unless you solve the issue by your own by reading 2 3 years old post and meanwhile lost money, time and patience.

    Helpful(0) Unhelpful(0)You have already voted this
  45. Carolyn O

    I had no ML background, although I have the math the models are based on. The material seemed more than week’s worth for a couple of weeks. The quizzes make sure you don’t miss the key points you need to take away and need for the assignment. Most information or key words are in the slides, but course expects you to be independent enough (intermediate) to learn closely related ideas on your own via StackOverFlow and discussion forums. The discussion forums were especially helpful for this course, but then online discussions makes it more studying alone. Discussions helped me trouble shoot and get better ideas how to approach the problems generally. I can explore and use ML and sklearn on my own, which thankfully seems to be a goal of this professor. No material could be left out, but when more videos, better longer time estimate for the week would be nice.

    Helpful(0) Unhelpful(0)You have already voted this
  46. Amir A C

    Unfortunately, for me, this course (not the specialization) seems to be a “review of” Applied Machine Learning in Python” rather than “teaching” Applied Machine Learning in Python. Some codes used in the notebook were skipped by the instructor.

    Helpful(0) Unhelpful(0)You have already voted this
  47. Carolyn O

    I had no ML background, although I have the math the models are based on. The material seemed more than week’s worth for a couple of weeks. The quizzes make sure you don’t miss the key points you need to take away and need for the assignment. Most information or key words are in the slides, but course expects you to be independent enough (intermediate) to learn closely related ideas on your own via StackOverFlow and discussion forums. The discussion forums were especially helpful for this course, but then online discussions makes it more studying alone. Discussions helped me trouble shoot and get better ideas how to approach the problems generally. I can explore and use ML and sklearn on my own, which thankfully seems to be a goal of this professor. No material could be left out, but when more videos, better longer time estimate for the week would be nice.

    Helpful(0) Unhelpful(0)You have already voted this
  48. Amir A C

    Unfortunately, for me, this course (not the specialization) seems to be a “review of” Applied Machine Learning in Python” rather than “teaching” Applied Machine Learning in Python. Some codes used in the notebook were skipped by the instructor.

    Helpful(0) Unhelpful(0)You have already voted this
  49. MUHAMMAD M M

    it’s very good explanation about how we use Machine Learning in real life problem.

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  50. MUHAMMAD M M

    it’s very good explanation about how we use Machine Learning in real life problem.

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  51. Michel H

    helpfull, but so many information in little time. Difficult to get clarified the ideas behind

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  52. Michel H

    helpfull, but so many information in little time. Difficult to get clarified the ideas behind

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  53. Hassan N

    That’s Very Great

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  54. Hassan N

    That’s Very Great

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  55. Xuening H

    Pro: I really like all the homework. The data is dirty and the work is a little bit challenging but doable. Con: I prefer more animation in slices during the lectore to keep me concentrated. I get distracted watching the lecture’s face.

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  56. Hafiz M S Z

    I learn many new things which I have not learn in my university.The best quality courses encourage me to learn and explore more in deep Machine learning.

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  57. Xuening H

    Pro: I really like all the homework. The data is dirty and the work is a little bit challenging but doable. Con: I prefer more animation in slices during the lectore to keep me concentrated. I get distracted watching the lecture’s face.

    Helpful(0) Unhelpful(0)You have already voted this
  58. Hafiz M S Z

    I learn many new things which I have not learn in my university.The best quality courses encourage me to learn and explore more in deep Machine learning.

    Helpful(0) Unhelpful(0)You have already voted this
  59. Tiberiu T

    A comprehensive review of the most important concepts and methods in machine learning.

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  60. Tiberiu T

    A comprehensive review of the most important concepts and methods in machine learning.

    Helpful(0) Unhelpful(0)You have already voted this
  61. Juan S

    Good overview to a lot of different ML techniques

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  62. Juan S

    Good overview to a lot of different ML techniques

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  63. RAMISETTI B R

    wow!!!great feeling from learning here

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  64. Ana K A d M

    Excellent balance between theory and practice!

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  65. RAMISETTI B R

    wow!!!great feeling from learning here

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  66. Ana K A d M

    Excellent balance between theory and practice!

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  67. Ravi M

    Course was designed in a well structured manner and the basic concepts were covered for Regression and Classification. Many many thanks to University of Michigan for creating it.

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  68. Vinay K M

    Just Awesome

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  69. Jin Kyu C

    I would not recommend this course except for week 1. According to some forum posts, not only is this course a bit outdated (needs fixes to many parts and they haven’t fixed them for at least 2 years), seemingly small but crucial parts of the assignments are not covered in the lecture videos which were very frustrating and time wasting to figure out (4 week course ended up being 10 weeks for me with a result of 93,4% final grade). Combing through the videos turned out to be futile and of course, relying on external sources such as stackoverflow was also not very helpful since the questions asked need to be extremely specific to the course. Even simply submitting the assignments were met with difficulties; and it’s similar forum posts week after week.

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  70. Yiannis K

    I really enjoyed the notebooks and the videos of Prof Thomson

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  71. Callum Z Y Y

    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.

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  72. Harry A

    An ideal platform to start your discovery and exploration of the world of Machine Learning. A programming knowledge, however, is a necessary prerequisite.

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  73. WhiteCR

    Good course for practicing machine learning algorithms with Python Sci kit Learn package.

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  74. WhiteCR

    Good course for practicing machine learning algorithms with Python Sci kit Learn package.

    Helpful(0) Unhelpful(0)You have already voted this
  75. Harry A

    An ideal platform to start your discovery and exploration of the world of Machine Learning. A programming knowledge, however, is a necessary prerequisite.

    Helpful(0) Unhelpful(0)You have already voted this
  76. Narendhiran

    Lectures were a bit slow, I personally felt pace could be increased and more content could be covered in areas like boosting and all.The assignments gave me a hands on approach in using sklearn library.I felt it was over all a very good course and would definitely recommend it for others. Thank You Yours sincerely, Narendhiran.R

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  77. Narendhiran

    Lectures were a bit slow, I personally felt pace could be increased and more content could be covered in areas like boosting and all.The assignments gave me a hands on approach in using sklearn library.I felt it was over all a very good course and would definitely recommend it for others. Thank You Yours sincerely, Narendhiran.R

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  78. Sudhir K D J

    Very poor configurations. I am tired of submitting assignments on auto grader. This is the first time I am having such terrible experience with Coursera. Hope you improve.

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  79. Tue V

    I have learnt a lot from this course. Thanks so much

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  80. Akash D

    Thank You! Sir

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  81. Mohamed S

    A comprehensive course by a wold class university,some teaching could have been better by using more interactive methods.

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  82. Mohamed R

    one of the worst courses i ever had

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  83. Mahindra S R

    Useful for understanding the application part of ML whereas Andrew Ng’s course gives a more in depth understanding of the topics

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  84. Indrajit P

    Very well structured and informative course ! All the lectures are concise and give enough context for self exploration. The assignments provide are a good hands on experience as well !!

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  85. Nicolas M

    Great course!

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