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Отзывы учащихся о курсе Applied Machine Learning in Python от партнера Мичиганский университет

4.6
stars
Оценки: 4,522
Рецензии: 780

О курсе

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....

Лучшие рецензии

OA

Sep 09, 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

FL

Oct 14, 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!!

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226–250 из 763 отзывов о курсе Applied Machine Learning in Python

автор: Yuwei Y

Jun 04, 2019

I like this course very much. It focuses on ML application and it's easy to understand. Definitely recommend it!

автор: Vasilis S

Aug 12, 2018

Great course! Assignment 4 is very interesting and allows you to apply all you've learnt in this course at once.

автор: Ryan J

Feb 11, 2018

Incredibly insightful and helpful for a recent master's graduate looking to augment his skills on the job market

автор: Matthias B

Aug 05, 2017

Great course, very hands-on. Maybe difficult to follow without any prior knowledge in machine learning, though.

автор: Liu L

Jan 03, 2019

This course provides a good introduction to using python in machine learning. It helps me to get hands on it.

автор: Mikhail S

Aug 27, 2017

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

автор: Nitin k

Apr 22, 2019

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

автор: Mohamed A M A

Jan 19, 2019

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

автор: HISHAM I A

Nov 05, 2018

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

автор: Rahul S

Dec 08, 2019

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

автор: Christian E

Jan 19, 2019

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

автор: Anurag W

Jul 18, 2019

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

автор: Matt E

Aug 29, 2017

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

автор: Miguel Á B P

Jul 28, 2018

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

автор: Alejandro R

Jul 08, 2018

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

автор: Mile D

Oct 17, 2017

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

автор: Shashwenth.M

Dec 19, 2019

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

автор: Min L

Feb 06, 2019

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

автор: Francesco S

Mar 20, 2018

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

автор: Oumeyma F R

Dec 23, 2019

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

автор: Zachary Q

Aug 19, 2019

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

автор: Muhammad A R

Sep 24, 2018

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

автор: KylinMountain

Jun 08, 2018

It's very impressive.

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

автор: Megan J

Dec 31, 2018

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

автор: Evan G

Jul 24, 2018

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