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Jupyter Notebooks: Fundamentals of Machine Learning and Deep Learning

Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. 


Source: from the Support Vector Machines chapter, here


  • The machine learning landscape
  • End to end machine learning project
  • Classification
  • Training linear models
  • Support vector machines
  • Decision trees
  • Ensemble learning and random forests
  • Dimensionality reduction
  • Unsupervised learning
  • Neural nets with Keras
  • Training deep neural networks
  • Custom models and training with Tensorflow
  • Loading and preprocessing data
  • Deep computer vision with CNNs
  • Processing sequences using rnns and CNNs
  • NLP with rnns and attention
  • Autoencoders and GANs
  • Reinforcement learning
  • Training and deploying at scale

You can access this material here. For other free tutorials (including from Berkeley, Harvard, Columbia, Google, Microsoft and so on), follow this link.

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