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Tutorial di Machine Learning
Ottieni insight e best practice su IA e machine learning, migliora le competenze e crea culture data-driven. Scopri come ottenere il massimo dai modelli di machine learning con i nostri tutorial.
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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 ottobre 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 ottobre 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 agosto 2017
Apache Spark Tutorial: ML with PySpark
Apache Spark tutorial introduces you to big data processing, analysis and ML with PySpark.
Karlijn Willems
28 luglio 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 giugno 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 maggio 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 marzo 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 maggio 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 maggio 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 aprile 2016