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.
Ce cours fait partie de la Spécialisation Science des données appliquée avec Python
Spécialisation Science des données appliquée avec PythonUniversité du MichiganÀ propos de ce cours
Ce que vous allez apprendre
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
Compétences que vous acquerrez
- Python Programming
- Machine Learning (ML) Algorithms
- Machine Learning
- Scikit-Learn
Offert par
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Programme de cours : ce que vous apprendrez dans ce cours
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
Avis
- 5 stars71,63 %
- 4 stars21,20 %
- 3 stars4,83 %
- 2 stars1,15 %
- 1 star1,18 %
Meilleurs avis pour APPLIED MACHINE LEARNING IN PYTHON
Not for the faint of heart and some experience with Python, in particular Pandas, is preferred. Great overview of the different methods used in machine learning. One of the better courses imo.
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.
Great content and good instruction. Need to fix the files in the assignments though. It's hard to keep track in the forums and frustrating go back and forth to find out why it's not working.
À propos du Spécialisation Science des données appliquée avec Python
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