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トピック
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名以上のトレーニングをお考えですか？DataCamp for Businessをお試しください
Kaggle Tutorial: Your First Machine Learning Model
Learn how to build your first machine learning model, a decision tree classifier, with the Python scikit-learn package, submit it to Kaggle and see how it performs!
Hugo Bowne-Anderson
2018年1月3日
Kaggle Tutorial: EDA & Machine Learning
In this Kaggle tutorial, you'll learn how to approach and build supervised learning models with the help of exploratory data analysis (EDA) on the Titanic data.
Hugo Bowne-Anderson
2017年12月21日
Convolutional Neural Networks in Python with Keras
In this tutorial, you’ll learn how to implement Convolutional Neural Networks (CNNs) in Python with Keras, and how to overcome overfitting with dropout.
Aditya Sharma
2017年12月5日
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