Introduction to Machine Learning

Instructors: Lawrence Carin +3 more

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Skills you'll gain

  •   Image Analysis
  •   PyTorch (Machine Learning Library)
  •   Natural Language Processing
  •   Artificial Neural Networks
  •   Deep Learning
  •   Machine Learning
  •   Supervised Learning
  •   Unsupervised Learning
  •   Applied Machine Learning
  •   Medical Imaging
  •   Computer Vision
  •   Reinforcement Learning
  • There are 6 modules in this course

    This course will provide you a foundational understanding of machine learning models (logistic regression, multilayer perceptrons, convolutional neural networks, natural language processing, etc.) as well as demonstrate how these models can solve complex problems in a variety of industries, from medical diagnostics to image recognition to text prediction. In addition, we have designed practice exercises that will give you hands-on experience implementing these data science models on data sets. These practice exercises will teach you how to implement machine learning algorithms with PyTorch, open source libraries used by leading tech companies in the machine learning field (e.g., Google, NVIDIA, CocaCola, eBay, Snapchat, Uber and many more).

    Basics of Model Learning

    Image Analysis with Convolutional Neural Networks

    Recurrent Neural Networks for Natural Language Processing

    The Transformer Network for Natural Language Processing

    Introduction to Reinforcement Learning

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