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Mastering AI and ML Fundamentals

Published by Pearson

Beginner content levelBeginner

Understand the basics of AI & ML algorithms through real-world use cases

  • Explore the mechanics of AI and Machine Learning in a way that is intuitive and easy to grasp.
  • Understand how AI algorithms operate, including Generative AI, Large Language Models (LLMs), Deep Learning, Neural Networks, Reinforcement Learning, Decision Trees, Regression, Supervised and Unsupervised Learning.
  • Explore real-world AI frameworks by seeing how algorithms are implemented using popular tools like Jupyter Notebook, Google Colab, TensorFlow, PyTorch, and Scikit-learn.
  • Learn by seeing with practical Python demos and examples, so you can start building your own AI projects immediately after the course.

Mastering AI and ML Fundamentals provides focused training on AI and ML principles. You'll learn how the most important algorithms work and when to use them. The course covers key principles behind popular AI and ML algorithms, leading to the modern generative AI revolution. You will also get an inside look into the world of Generative AI, learning how LLMs and ChatGPT work. Expert authors Jerome Henry and Robert Barton will teach core principles and techniques through clear, practical examples.

This one-day, 4-hour training will demystify the world of AI and LLMs. You will see how Artificial Intelligence can help you solve problems faster, automate processes, find hidden patterns, and accelerate work. Best of all, you will gain an intuitive understanding of how it all works through live demos and a sample code repository.

What you’ll learn and how you can apply it

  • Learn the main principles behind the core machine learning techniques, from linear regression, classification, clustering and random forests, neural networks, and Large Language Models
  • Gain an intuitive understanding of how these techniques work and when to use them for various types of problems
  • See how these various algorithms can be implemented using Python

This live event is for you because...

  • You are interested in machine learning, but want a better understanding of how it all works
  • You have started using tools like ChatGPT, but want to understand how it works and how to leverage these tools better
  • You want access to coding examples of the various AI/ML methods so you can start trying it on your own

Prerequisites

  • Basic computer knowledge
  • Some basic knowledge of Python is useful, but not mandatory
  • General awareness of machine learning

Course Set-up

  • No specific setup required
  • We will provide code examples in GitHub for attendees to download

Recommended Preparation

Recommended Follow-up

Schedule

The time frames are only estimates and may vary according to how the class is progressing.

Segment 1: Key Concepts of AI and Machine Learning – Rob (27.5 min)

  • ML and AI: An introduction and quick history
  • The AI family tree
  • Inference and training
  • Parameters and hyperparameters
  • The AI Pipeline
  • Q&A

Segment 2: Unsupervised Learning – Jerome (27.5 min)

  • Clustering first principles
  • How K-Means works, its advantages and limitations
  • DBSCAN – what it is and how to use it
  • Exploring data – PCA – t-SNE
  • Q&A

Break 5 min

Segment 3: Supervised Learning Part 1: Prediction – Rob (27.5 min)

  • What is Supervised Learning and when to use it
  • Linear regression first principles
  • Understanding the Cost function
  • Gradient descent and the learning rate hyperparameter
  • Q&A

Segment 4: Supervised Learning Part 2: Classification – Rob (27.5 min)

  • Classification first principles
  • The Sigmoid function
  • Logistic regression
  • Support Vector Machines (SVM)
  • Classification with Neural Networks
  • Q&A

Break 5 min

Segment 5: Decision Trees – Jerome (27.5 min)

  • What are decision trees and when to use them
  • Decision trees first principles
  • Random forests
  • Other types of decision trees
  • Q&A

Segment 6: Reinforcement Learning – Rob (27.5 min)

  • Learning by trial and error: the idea behind Reinforcement Learning
  • RL first principles
  • Markov decision processes, Monte Carlo methods
  • Q-Learning and Deep Q-Learning
  • Q&A

Break 5 min

Segment 7: Neural Networks & Deep Learning – Jerome (27.5 min)

  • Deep Learning principles and why Deep Learning is deep
  • Artificial Neural Networks (ANN)
  • Activation functions
  • Neural Network families and applications
  • Q&A

Segment 8: Introduction to Generative AI and Large Language Models (LLM) – Jerome (27.5 min)

  • An introduction to LLMs
  • Principles of language modeling
  • Transformers and Attention is All You Need
  • Q&A

Course wrap-up and next steps (5 minutes)

Your Instructors

  • Jerome Henry

    Jerome Henry is a Distinguished Engineer in the Office of the Wireless CTO at Cisco Systems. His main field of research is around optimization of performances in unlicensed wireless networks, which includes aspects of QoS, IoT, privacy, indoor location, but also AI/Machine Learning and LLMs centered on network languages. Jerome has more than 25 years of experience teaching technical courses in more than 15 different countries and 4 different languages, to audiences ranging from graduate degree students to networking professionals and technical support engineers. Jerome joined Cisco in 2012. Before that time, he was consulting and teaching heterogeneous networks and wireless integration with the European Airespace team, which was later acquired by Cisco to become their main wireless solution.

  • Rob Barton

    Rob Barton is a Distinguished Engineer with Cisco. Rob has worked in the IT industry for over 27 years, the last 25 of which have been with Cisco. Rob Graduated from the University of British Columbia with a degree in Engineering Physics. Rob is a published author, with titles on subjects of Generative AI, Quality of Service (QoS), Wireless Communications, and IoT. Additionally, he has co-authored many peer-reviewed research papers and leads Cisco’s academic research partnership program. Rob holds numerous patents in the areas of AI, wireless communications, network security, cloud networking, and IoT. His current areas of work include network automation and Agentic models for IT management.

Skill covered

AI & ML