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Foundations of Machine Learning and Generative AI

Course Summary

The Foundations of Machine Learning and Generative AI course equips participants with the foundational knowledge to explore more advanced topics such as Retrieval-Augmented Generation (RAG), improving agents for more complex workflows, and deploying scalable applications powered by LLMs. By understanding the basics of model fine-tuning and transformer mechanics, participants will be prepared to tackle next-level challenges like advanced LLM optimization, hybrid models, and efficient application deployment in cloud or local environments. After completing this course, participants will have the skills to design functional prototypes, understand the principles of transfer learning, and leverage LLM APIs for advanced integrations.

Prerequsites:

  • Basic Python knowledge
  • Some experienice with NumPy and Pandas
Purpose
Develop skills to design functional prototypes, understand the principles of transfer learning, and leverage LLM APIs for advanced integrations.
Audience
Engineers and Developers looking to advance their skills and knowledge of machine learning, deep learning, and large language models (LLMs).
Role
Software Developers | Data Scientists
Skill level
Intermediate
Style
Lecture | Hands-on Activities | Use Cases | Labs
Duration
3 days
Related technologies
Gen AI | ML } Python | NumPy | Hugging Face | TensorFlow | Keras

 

Course objectives
  • Understand Core Machine Learning Concepts
  • Perform Practical Natural Language Processing (NLP)
  • Develop Foundational Deep Learning Skills
  • Explore Transformers and Prompt Engineering
  • Implement Transfer Learning with Pre-Trained Models
  • Create and Integrate a Chatbot Using LLMs
  • Build Confidence in Applying LLMs and ML Tools

What you'll learn:

In this Foundations of Machine Learning and Generative AI course, you'll learn:

Fundamentals of Machine Learning and NLP

  • Introduction to ML Concepts and Workflow
    • Overview of supervised vs. unsupervised learning.
    • ML lifecycle: problem framing, modeling, evaluation, and deployment.
    • Tools overview: scikit-learn, TensorFlow, Hugging Face.
  • Supervised Learning in scikit-learn
    • What is Regression?
      • Linear regression: concept and application.
    • What is Classification?
      • Logistic regression for binary classification.
      • Spam detection using logistic regression.
    • Model Evaluation
      • Metrics: Accuracy, Precision/Recall, F1 Score, and ROC Curve.
      • Comparing models using scikit-learn tools.
  • Introduction to NLP: Sentiment Analysis with scikit-learn and Gensim
    • Text Processing Basics
      • Tokenization, stopwords removal, and vectorization.
    • Gensim Word2Vec
      • Basics of word embeddings and their applications.
    • Sentiment Analysis
      • Dataset: Yelp reviews dataset.
      • Using scikit-learn for sentiment classification.

Deep Learning and Transformers

  • Deep Learning Basics
    • Key differences between ML and DL.
    • Anatomy of a neural network: layers, activations, loss functions, and optimizers.
  • Introduction to Transformers
    • What are transformers, and why are they important?
    • Understanding the attention mechanism and "Attention is All You Need."
  • Prompt Engineering with Hugging Face
    • Basics of prompt engineering for LLMs: one-shot, few-shot, and zero-shot learning.
    • Using Hugging Face for text classification and generation tasks.

Building Applications and Transfer Learning with LLMs

  • Introduction to Transfer Learning
    • What is transfer learning, and why is it useful for LLMs?
    • Fine-tuning pre-trained models for specific tasks using Hugging Face.
      • Fine-tuning a sentiment analysis model on a custom dataset.
      • Differences between encoder-based (e.g., BERT) and decoder-based (e.g., GPT) models.
  • Building a Chatbot with Hugging Face and Flask
    • Overview of chatbot workflows.
      • Integrate a Hugging Face model for generating chatbot responses.

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