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1
Intro
2
Introduction
3
Outline
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Goals
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Cyclearn
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Scikitlearn
7
Data Processing Functions
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Preprocessing Functions
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Model Support
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Model Functions
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Builtin Data Sets
12
Making Data
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Pipeline
14
Google Collab
15
Data
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Data dummies
17
Scaling Imputation
18
Logistic Regression
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Cross Validation
20
Gradient Boost
21
Retrieve Models
22
Resources
23
What does TensorFlow do
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TensorFlow can be used
25
Tensorflow Code
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore advanced Python libraries for data science in this comprehensive 1 hour 45 minute conference talk from the Data Science Festival. Dive deep into three essential Python packages: scikit-learn, TensorFlow, and PyTorch Geometric, covering their functionalities, implemented methodologies, and practical code exercises. Learn about data processing, preprocessing, model support, and built-in datasets in scikit-learn. Discover TensorFlow's capabilities and use cases, including code examples. Gain insights into additional tools like Siuba and Plotly for enhanced stakeholder engagement. Access accompanying iPython notebooks and datasets through the provided GitHub repository. Enhance your data science toolkit and stay up-to-date with cutting-edge Python libraries essential for machine learning, deep learning, and AI applications.

Advanced Python Libraries for Data Science

Data Science Festival
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