2. Statistics: Learn the fundamentals of statistics required for Data Science
@sumanth_077
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Harvard Free Online Courses: Python, Data Science, Machine Learning
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Harvard University is offering online courses on Python, Data Science, Machine Learning, Data Preprocessing, Data Visualization & Statistics Here are 5 FREE courses you don't want to miss:
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CodeRabbit: AI-Powered Code Review Tool for Pull Requests
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Introducing CodeRabbit, an AI-powered code reviewer that significantly speeds up the code review process while also improving the quality of the code! CodeRabbit works seamlessly within the pull request workflow and collaborates with the developer and the reviewer to ensure
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Sharing Daily Content on Python, Data Science, and Machine Learning
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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AI Integration Simplifies MLOps and ETL Pipeline Complexity
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By bringing AI directly to data developers and organizations can avoid the complexity of building and integrating MLOps and ETL pipelines as well as migrating and duplicating data across multiple environments. Check this out: https://
superduperdb.com -
SuperDuperDB Simplifies AI Integration on Existing Data Infrastructure
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SuperDuperDB eliminates complex MLOps pipelines, specialized vector databases – and the need to migrate and duplicate data by integrating AI at the data’s source, directly on top of your existing data infrastructure. This simplifies building and managing AI applications.
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Open-source Framework for Building AI Applications in Databases
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AI in your Database. An open-source framework for building AI Applications right in your databases:
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Daily Content Sharing on Python, Data Science, and Machine Learning
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That's a wrap! If you are interested in any of these below topics: – Python – Data Science – Machine Learning – Data Analysis – LLMs – MLOps Find me → @Sumanth_077 I'm sharing daily content over here.
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cuDF Pandas Accelerator: Overcome In-Memory Limitations
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Pandas operates in-memory, as it loads the entire dataset into the local memory of the machine it is running on. This limits its ability to handle large datasets. With cuDF’s pandas accelerator you can now bring accelerated computing to pandas workflows.
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RAPIDS cuDF Library Now Integrated in Google Colab
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Also their cuDF library will automatically know if you're running on GPU or CPU and speed up your processing. RAPIDS cuDF is now integrated directly into Google Colab. Checkout the Colab Notebook: https://
colab.research.google.com/github/rapidsa
i-community/showcase/blob/main/getting_started_tutorials/cudf_pandas_colab_demo.ipynb?ncid=ref-inor-594904
… Check out the Repo: