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Poznaj wskazówki i dobre praktyki dotyczące AI i uczenia maszynowego, rozwijaj kompetencje i buduj kulturę pracy z danymi. Dowiedz się z naszych samouczków, jak maksymalnie wykorzystać modele uczenia maszynowego.
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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
28 lipca 2017
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
20 czerwca 2017
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
4 maja 2017
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
23 marca 2017
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
10 maja 2016
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
3 maja 2016
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
26 kwietnia 2016