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.
Этот курс входит в специализацию ''Специализация Прикладная наука о данных с Python'
Специализация Прикладная наука о данных с PythonМичиганский университетОб этом курсе
Чему вы научитесь
Describe how machine learning is different than descriptive statistics
Create and evaluate data clusters
Explain different approaches for creating predictive models
Build features that meet analysis needs
Приобретаемые навыки
- Python Programming
- Machine Learning (ML) Algorithms
- Machine Learning
- Scikit-Learn
от партнера
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Программа курса: что вы изучите
Module 1: Fundamentals of Machine Learning - Intro to SciKit Learn
Module 2: Supervised Machine Learning - Part 1
Module 3: Evaluation
Module 4: Supervised Machine Learning - Part 2
Рецензии
- 5 stars71,63 %
- 4 stars21,20 %
- 3 stars4,82 %
- 2 stars1,14 %
- 1 star1,18 %
Лучшие отзывы о курсе APPLIED MACHINE LEARNING IN PYTHON
In depth course that covers a lot in a short amount of time. If you take some extra time to delve deeper into these topics, you can ensure a great overview of machine learning with python.
It feels good to learn something new and highly skilled demand in Engineering. Thanks to Coursera and instructor for providing such a wonderful opportunity of learning through your platform.
The course was really interesting to go through. All the related assignments whether be Quizzes or the Hands-On really test the knowledge. Kudos to the mentor for teaching us in in such a lucid way.
Excellent course for someone who already has some knowledge of python but not quite familiar with machine learning. This course will teach you the application of machine learning in python.
Специализация Прикладная наука о данных с Python: общие сведения
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