data-science-ipython-notebooks
A comprehensive collection of data science Python notebooks covering deep learning, machine learning, big data, visualization, and essential tools.
What is data-science-ipython-notebooks?
Data Science IPython Notebooks is a curated repository of Jupyter notebooks covering a wide range of data science topics. It provides hands-on tutorials and examples for deep learning frameworks like TensorFlow and Keras, machine learning with scikit-learn, data analysis with pandas and NumPy, and big data tools like Spark and AWS. The project helps learners and practitioners quickly apply concepts through executable code.
Data scientists, machine learning engineers, and developers looking to learn or reference practical implementations of data science libraries and frameworks in Python.
It consolidates high-quality, community-vetted tutorials from various sources into a single, organized repository, saving time compared to searching scattered resources. The notebooks are ready-to-run and cover both foundational and advanced topics.
Overview
Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.
Use Cases
Best For
- Learning deep learning with TensorFlow or Keras through step-by-step examples
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