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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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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
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
2017年5月4日
Deep Learning with Jupyter Notebooks in the Cloud
This step-by-step tutorial will show you how to set up and use Jupyter Notebook on Amazon Web Services (AWS) EC2 GPU for deep learning.
Dan Becker
2017年3月23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
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
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
2016年4月26日