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學生對 密歇根大学 提供的 Applied Machine Learning in Python 的評價和反饋

6,890 個評分
1,246 條評論


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....



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!!


great experience and learning lots of technique to apply on real world data, and get important and insightful information from raw data. motivated to proceed further in this domain and course as well.


376 - Applied Machine Learning in Python 的 400 個評論(共 1,226 個)

創建者 Mikhail S


Thank you for the very well done course! It's really helpful, has a clear explanation of topics and examples.

創建者 Ahmad A


Excellent Course each topic is both theoretically as well as as practically explained. Really a good course

創建者 Akshay S T


Very Intuitive and helpful course for clearing concepts of machine learning and Python's SciKit Learn module

創建者 Nitin K


Great Course. Helped me to learn the concepts of Machine Learning and uses of respective Sklearn libraries.

創建者 Mohamed A M A


The theoretical part is comprehensive with an excellent balance between the theory and practical exercises.



Excellent collection of various types of Machine Learning Algorithms with visual demonstration and example.

創建者 Rahul S


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

創建者 Christian E


Content and phase are very good. Very clear explanation of topic by the instructor. Appreciate it so much.

創建者 Anurag W


This Course really provides great learning on Advance Machine learning techniques with Python application

創建者 Matt E


Learned a lot in this course! Much better than the previous two and also taught by a different professor.

創建者 Alexander A


Excellent Course. The only one problem is the duration of videos. The codes in Jupyter are very elegants

創建者 Miguel Á B P


What a challenge. Incredible course, no words. Excellent pedagogy from professor Kevyn Collins-Thompson.

創建者 Alejandro R


Good choice for Machine Learning introduction, Data Analysis in Python and applied statistical concepts.

創建者 Mile D


After this course you will be able to do your own analysis using machine learning which is really great.

創建者 Shashwenth.M


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

創建者 Min L


A very good course to start journey on data science. Good combination of reading, lecture and practice.

創建者 Mikhail E


Great course, though was a bit difficult for me, as I wasn't very familiar with math side of the issue

創建者 Francesco S


Excellent couse, I've gained real knowledge and the lecture is very thorough! Challenging and intense.

創建者 Oumeyma F R


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

創建者 Zachary Q


Was a great class where I learned to apply existing knowledge about ML to the actual background info!

創建者 Muhammad A R


Covers most of the basic supervised Machine learning Algorithms in SciKit-Learn from application POV.

創建者 KylinMountain


It's very impressive.

I suggest If we add a kaggle competition as a overall summery, that'll be great.

創建者 Megan J


In depth understanding is required to complete the assignments. Challenging without being demanding.

創建者 Evan G


Quick way to get exposed to supervised learning algorithms. Lays a nice foundation for ML in python.

創建者 David R


Nice survey of machine learning techniques and tutorial on the scikit-learn toolbox. Very helpful.