Applied Machine Learning in Python
https://www.coursera.org/learn/python-machine-learning?specialization=data-science-python
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 (cluster
Categoria: Data Science
Subcategoria: Data Analysis
Tipo de Curso: Course
Habilidades: Python Programming,Machine Learning (ML) Algorithms,Machine Learning,Scikit-Learn,
Idioma: English
Subtitulos: Arabic, French, Portuguese (European), Italian, Vietnamese, Korean, German, Russian, English, Spanish
Rating: 4.6stars
Vistas: 8.307
Sitio Web: Coursera
Duracion: Approx. 31 hours to complete
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