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.
Este curso forma parte de Programa especializado: Ciencias de los Datos Aplicada con Python
Programa especializado: Ciencias de los Datos Aplicada con PythonUniversidad de MíchiganAcerca de este Curso
Qué aprenderás
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
Habilidades que obtendrás
- Python Programming
- Machine Learning (ML) Algorithms
- Machine Learning
- Scikit-Learn
Ofrecido por
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Programa - Qué aprenderás en este curso
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
Reseñas
- 5 stars71,63 %
- 4 stars21,19 %
- 3 stars4,82 %
- 2 stars1,14 %
- 1 star1,18 %
Principales reseñas sobre APPLIED MACHINE LEARNING IN PYTHON
Very good mix of video and python notebook. Some improvement can be done with the AutoGrader like get back the error python stack trace.
Globally, very good course - strongly recommanded
EXTREMELY USEFUL AND GOOD COURSE, CONGRATULATIONS TO ALL THE PEOPLE INVOLVE.
Honestly, I never thought I could learn so much in an online course, excited for the rest of the specialization
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.
Acerca de Programa especializado: Ciencias de los Datos Aplicada con Python
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