機械学習チュートリアル

AIと機械学習に関するインサイトとベストプラクティスでスキルを高め、データ文化を構築しましょう。チュートリアルで機械学習モデルを最大限に活用する方法を学べます。

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2人以上をトレーニングしますか?DataCamp for Businessを試す

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

2017年10月19日

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

2017年10月13日

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

2017年8月24日

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