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机器学习教程

获取有关 AI 与机器学习的洞见与最佳实践、提升技能、构建数据文化。通过我们的教程,学习如何最大化发挥机器学习模型的价值。
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Group培训2人或以上?试试DataCamp for Business

Apache Spark Tutorial: ML with PySpark

Apache Spark tutorial introduces you to big data processing, analysis and ML with PySpark.

Karlijn Willems

2017年7月28日

Scikit-Learn Tutorial: Baseball Analytics Pt 2

A Scikit-Learn tutorial to using logistic regression and random forest models to predict which baseball players will be voted into the Hall of Fame
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Daniel Poston

2017年6月20日

Scikit-Learn Tutorial: Baseball Analytics Pt 1

A scikit-learn tutorial to predicting MLB wins per season by modeling data to KMeans clustering model and linear regression models.
Daniel Poston's photo

Daniel Poston

2017年5月4日

Preprocessing in Data Science (Part 3): Scaling Synthesized Data

You can preprocess the heck out of your data but the proof is in the pudding: how well does your model then perform?
Hugo Bowne-Anderson's photo

Hugo Bowne-Anderson

2016年5月10日

Preprocessing in Data Science (Part 2): Centering, Scaling and Logistic Regression

Discover whether centering and scaling help your model in a logistic regression setting.
Hugo Bowne-Anderson's photo

Hugo Bowne-Anderson

2016年5月3日

Preprocessing in Data Science (Part 1): Centering, Scaling, and KNN

This article will explain the importance of preprocessing in the machine learning pipeline by examining how centering and scaling can improve model performance.
Hugo Bowne-Anderson's photo

Hugo Bowne-Anderson

2016年4月26日