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Video course · 15 chapters · 638 min · certificate

Master AI Concepts: Neural Networks, CNNs, RNNs & NLP

Master core AI concepts: what AI is, deep learning, convolutional neural networks with padding and filters, recurrent neural networks and word embeddings, and NLP from named entity recognition to pipelines, lemmatization, parsing and hands-on projects.

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What you'll learn

  • Explain AI and deep learning fundamentals
  • Describe how CNNs work, including padding and filters
  • Explain RNNs, activation functions and memory
  • Use word embeddings for meaning
  • Build an NLP pipeline with lemmatization and POS tagging
  • Apply NLP in hands-on projects

Chapters

15 chapters · 637:50
  1. 30:20 01Foundations Members What is AI? From searching Google to asking ChatGPT: what AI is, with examples like healthcare AI.
  2. 85:11 02Deep learning Members Introduction to deep learning Neural networks, hidden layers as black boxes, and handling scaled or stretched images.
  3. 40:30 03CNN Members Convolutional neural networks Why CNNs are needed and how they work, explained simply.
  4. 29:36 04CNN Members CNN architecture Image size and pixels, the journey of CNNs and the architecture overview.
  5. 23:57 05Padding Members Applying networks and padding Valid padding (no padding) and the effect of stride.
  6. 105:19 06Padding Members Types of padding Why padding matters across many layers.
  7. 19:48 07Filters Members Filter values How filter values are learned rather than hand-set.
  8. 63:39 08RNN Members Recurrent neural networks RNNs process sequences; transformers aren't RNN-based.
  9. 39:27 09Embeddings Members RNN architecture and embeddings RNN structure and word embeddings that capture meaning.
  10. 25:37 10RNN Members Tanh, memory and RNN summary Why tanh is used, internal memory and contextual understanding.
  11. 40:01 11NLP Members Named entity recognition Finding names of people, places and organizations in text.
  12. 22:41 12NLP Members NLP and NLG Natural language processing versus generation, and critical steps.
  13. 33:31 13Pipeline Members The NLP pipeline and lemmatization Pipeline steps including stemming and lemmatization.
  14. 36:17 14Analysis Members Stop words, parsing and POS tagging Stop word removal, dependency parsing and part-of-speech tagging.
  15. 41:56 15Projects Members Hands-on NLP and project ideas Hands-on NLP work and AI project ideas.