Учебные материалы по машинному обучению

Узнайте о лучших практиках и подходах в области ИИ и машинного обучения, развивайте навыки и формируйте культуру работы с данными. Научитесь получать максимум от моделей машинного обучения с помощью наших руководств.

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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!

3 января 2018 г.

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.

21 декабря 2017 г.

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.

5 декабря 2017 г.

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.

19 октября 2017 г.

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.

13 октября 2017 г.

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.

24 августа 2017 г.

Apache Spark Tutorial: ML with PySpark

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

28 июля 2017 г.

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

20 июня 2017 г.

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.

4 мая 2017 г.

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.

23 марта 2017 г.

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?

10 мая 2016 г.

Preprocessing in Data Science (Part 2): Centering, Scaling and Logistic Regression

Discover whether centering and scaling help your model in a logistic regression setting.

3 мая 2016 г.