Check out our new YouTube series Data Science Project from scratch. Brainstorm ideas -> Collect data (NBA games) -> Clean and Explore data (feature engineering) -> Build a predictive model -> Deploy the model with FastAPI -> Create a web app with Dash
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More and more companies are moving to the Cloud. Google Cloud Platform is one of the top choices.
Check out our tutorial on why and how to use Jupyter Notebook on its AI platform.
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The random forests are powerful, popular, and easy-to-use algorithms for predictive modeling in #MachineLearning.
Learn the basics and try it out in Python.
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How to use Twitter data for #MachineLearning sentiment analysis in #Python?
Discover and apply the end-to-end process with an example in this tutorial.
https://t.co/jQq8bS0QF8
Getting data is critical for #MachineLearning, #DataScience . Large websites like Twitter, Yelp often offer APIs, which provide convenient data exposure. How to request data from these APIs in #Python?
https://t.co/4svW6KURvN
If you are looking to apply machine learning or data science in the industry, check out this guide, which will help you better understand what to expect. https://t.co/HEzczjJA5k
It's critical that the result of your model can generalize to other datasets. Cross-validation is one of the simplest and commonly used techniques that can validate models based on these criteria. #MachineLearning
https://t.co/3A0IHINCye