Foundations of Neural Networks Specialization

Master Neural Networks for AI and Machine Learning. Gain hands-on experience with neural networks, advanced techniques, and ethical AI practices to solve real-world challenges in machine learning and AI applications.

Instructor: Zerotti Woods

What you'll learn

  •   Understand the mathematical foundations of neural networks, including deep learning optimization, regularization, and ethical considerations in AI.
  •   Gain hands-on experience in implementing and analyzing various neural network architectures, such as CNNs, RNNs, and GANs, using Python.
  •   Explore topics like probabilistic models, model evaluation, and bias mitigation, preparing for real-world applications in AI and deep learning.

    • Skills you'll gain

  •   Debugging
  •   Data Ethics
  •   Machine Learning Algorithms
  •   Generative AI
  •   Machine Learning
  •   Deep Learning
  •   Artificial Neural Networks
  •   Reinforcement Learning
  •   Linear Algebra
  •   Artificial Intelligence
  •   Ethical Standards And Conduct
  •   Unsupervised Learning

    • Specialization - 3 course series

    The hands-on assignments in this specialization integrate theoretical and practical expertise to design, train, and evaluate neural network models for real-world challenges. Using Python and frameworks like TensorFlow or PyTorch, students will implement feed-forward, convolutional, and recurrent networks, alongside advanced techniques such as generative adversarial networks and unsupervised learning. Focus areas include optimization, regularization, and ethical considerations like bias and privacy. Deliverables include a functional model addressing a defined problem, a critical evaluation of ethical impacts, and thorough documentation, preparing participants for roles in AI research and development.

    Advanced Neural Network Techniques

    Practical Methodology and Ethics in AI

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