Handledningar i maskininlärning

Få insikter och beprövade metoder inom AI och maskininlärning, höj kompetensen och bygg datadrivna kulturer. Lär dig hur du får ut mesta möjliga av maskininlärningsmodeller med våra handledningar.

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LDA2vec: Word Embeddings in Topic Models

Learn more about LDA2vec, a model that learns dense word vectors jointly with Dirichlet-distributed latent document-level mixtures of topic vectors.

Lars Hulstaert

19 oktober 2017

Web Scraping & NLP in Python

Learn to scrape novels from the web and plot word frequency distributions; You will gain experience with Python packages requests, BeautifulSoup and nltk.

Hugo Bowne-Anderson

13 oktober 2017

Detecting Fake News with Scikit-Learn

This scikit-learn tutorial will walk you through building a fake news classifier with the help of Bayesian models.

Katharine Jarmul

24 augusti 2017

Apache Spark Tutorial: ML with PySpark

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

Karlijn Willems

28 juli 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 juni 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 maj 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 mars 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 maj 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 maj 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 april 2016