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Voltar para Applied Machine Learning in Python

Applied Machine Learning in Python, Universidade de Michigan

2,900 classificações
539 avaliações

Informações sobre o curso

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....

Melhores avaliações

por 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!!

por SS

Aug 19, 2017

the content of videos , quiz and exercise all work extremely well together towards the stated goal of the course i.e. to give the learner a good over view of how to apply ML theories into action

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520 avaliações

por Kevin

Feb 15, 2019

It is definitely the best-organized, best-paced, most-worked-on course in this specialization, and from the MOOCs I have ever taken. Strongly recommend for your knowledge and career advance. Great professor!

por Anne Estoppey

Feb 14, 2019

Very nice class for people who have some intermediate knowledge in Python and who want to dig in, or consolidate their knowledge in Machine Learning. Great overview over scikit-learn, also going into details, and I also appreciated the part of the class about model evaluation. First week might seem not overly difficult, but the intensity of the class ramps up significantly in week 2. For me the level was challenging enough, without being overwhelming. I enjoyed taking this class and obtaining my certification at the end was a very nice reward. A big thank you to University of Michigan.

por Pieter Joan Van Voorst Vader

Feb 13, 2019

Inspirational course, learning you in a comprehensive manner, a thorough approach to machine learning with the target specific peculiarities and possible pitfalls.

por Shaukat

Feb 11, 2019

excellent course

por CMC

Feb 09, 2019

A little dated. Overall a good introduction. The informal explanation of SVM was particularly effective.

por Manik Sejwal

Feb 08, 2019

Optional references to the inner workings should be provided. For example how Decision Trees are trained and how the best division is decided.

por Min Li

Feb 06, 2019

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

por lcy9086

Feb 03, 2019

Great course on doing machine learning use sklearn and put little but enough explanation of the theories behind it!

por Phat Nguyen

Feb 02, 2019

Very good introductory course to approach scikit_learn!! Highly Recommend!!

por Alexandre Maurice

Feb 01, 2019

Good class, and it's very nice to have the "applied" machine learning angle (as opposed to focusing on the mathematical / theoretical underpinnings, which are only important at a much later point in time)