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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

7,981 個評分
1,452 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!!



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


426 - Applied Machine Learning in Python 的 450 個評論(共 1,442 個)

創建者 Thomas S


A very good review of important fundamental concepts in Machine Learning focusing on the usage of Sklearn.

創建者 Rahul S


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

創建者 Chris E


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

創建者 Lari L


The course gives deep knowledge on the subject as well as best practices and strong practice assignments.

創建者 abdelrahman a


the most interesting thing in the course was treating the students as if they are already data scientists

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 Fettah K


Taking these lessons from some of the world's most prestigious universities and professors is priceless.

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 Allyson D d L


Another good course of the specialization. The videos are a little boring but the assignments are good.

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.



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.



Very Good Course. I'll highly recommended pre requisites are basic knowledge of python programming.

創建者 David R


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