Building Machine Learning Solutions with scikit-learn | Path
Год выпуска: 2019
Производитель: Pluralsight
Сайт производителя://app.pluralsight.com/paths/skill/building-machine-learning-solutions-with-scikit-learn
Автор: Janani Ravi / Chetan Prabhu
Продолжительность: 24h 30m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание:
This skill teaches learners how to build machine learning solutions using Python and the pervasive scikit-learn package, including application of classifcation, regression, and clustering.
What you will learn:
Design and implement common machine learning solutions using scikit-learn
Evaluation and validation of scikit-learn machine learning models
Prerequisites:
Statistics and Probability
Data Analytics Literacy
Python programming
Related Topics:
Deep Learning Literacy
Machine Learning Literacy
Other Machine Learning Libraries, such as PyTorch
Computer Vision
Image Recognition
Содержание
Beginner
Experience the machine learning workflow as implemented in scikit-learn, and use that workflow to build simple classification, regression, and clustering models.
Building Your First scikit-learn Solution (Janani Ravi, 2019)
Building Classification Models with scikit-learn (Janani Ravi, 2019)
Building Regression Models with scikit-learn (Janani Ravi, 2019)
Building Clustering Models with scikit-learn (Janani Ravi, 2019)
Intermediate
Build sophisticated neural network models, apply dimension reduction techniques, and combine model approaches.
Building Neural Networks with scikit-learn (Janani Ravi, 2019)
Reducing Dimensions in Data with scikit-learn (Janani Ravi, 2019)
Employing Ensemble Methods with scikit-learn (Janani Ravi, 2019)
Advanced
Select the appropriate model for your business problem and data, and evaluate the effectiveness of that model.
Preparing Data for Modeling with scikit-learn (Janani Ravi, 2019)
Scaling scikit-learn Solutions (Janani Ravi, 2019)
Model Evaluation and Selection Using scikit-learn (Chetan Prabhu, 2019)
Файлы примеров: присутствуют
Субтитры: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 30fps, 100 kb/s
Аудио: AAC 48000Hz 2.0 chn 96 kbit/s
