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Algorithms

AI and ML Algorithms for Non-mathematicians and Data Science Beginners

Published by Pearson

Beginner content levelBeginner

Demystify AI and LLMs like ChatGPT

Explore how the main machine learning algorithms work “under the hood,” without forcing you to be a mathematician.

  • Learn how AI algorithms work, including Large Language Models (used by ChatGPT), Deep Learning, Reinforcement Learning, and Unsupervised Learning
  • See how these algorithms are deployed on popular tools like Tensorflow, Pytorch, and Sckitlearn
  • Learn through real-world, practical examples using Python so you can get started with your own AI projects as soon as the class ends

AI and ML Algorithms for Non-mathematicians and Data Science Beginners was built to give you the focused training you need to learn the principles of AI and ML and explore the ideas behind them. We’ll take a look at using math basics with Python—concepts you can use without being a mathematician or a professional data scientist. Expert authors and trainers Jerome Henry and Robert Barton will teach core principles and techniques and then show you how to use them through clear and practical examples.

This one-day / 4-hour training will demystify the world of AI and LLMs. You will see how the tools of Artificial Intelligence Markup Language (AI/ML) can help you solve problems faster, automate processes, find hidden patterns, and accelerate your work. You will also get an inside look into the world of AI, learning about passcode terms like LLMs, ChatGPT, TensorFlow and Pytorch, along with clarifying what the many acronyms, like SVMs, XG Boost and DBSCAN do. It can be difficult to understand how these all relate to each other, how they fit in the ML landscape, and also how they work exactly.

What you’ll learn and how you can apply it

By the end of the live online course, you’ll understand:

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

And you’ll be able to:

  • Implement a core Python program for ML, with libraries like SciKit, TensorFlow or Pytorch
  • Explain how various AL/ML techniques work
  • Select the correct AI/ML technique for various projects

This live event is for you because...

  • You are interested in machine learning, but want a better understanding 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 (to be added later)

Recommended Preparation

Recommended Follow-up

Schedule

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

Segment 1: Introduction to the World of AI/ML (30 min)

  • ML and AI: Introduction and history
  • The various ML types: ML landscape
  • Popular Applications of ML/AI
  • Q&A

Segment 2: Unsupervised Learning (30 min)

  • Clustering Principles
  • How K-Means works, its advantages and limitations
  • Hierarchical Clustering to find the right cluster count
  • DBSCAN to augment K-means in complex clouds
  • Q&A
  • Break

Segment 3: Supervised Learning (40 min)

  • When to use Supervised Learning
  • Linear regression: Cost function and gradient descent to understand your data
  • Logistic regression and classification
  • Support Vector Machines to find the boundary between groups
  • Q&A

Segment 4: Random Forests (20 min)

  • Why build forests in machine learning—non-numeric data
  • Building your first tree, Gini impurity
  • Building a full forest
  • Finding the best algorithm automatically: XG and other Boosters
  • Q&A
  • Break

Segment 5: Reinforcement Learning (30 min)

  • The ideas behind Reinforcement Learning
  • RL Principles
  • Tabular methods: Markov decision processes, Monte Carlo policy evaluation
  • RL at scale with linear value function approximation and deep reinforcement learning techniques
  • Q&A

Segment 6: Deep Learning (55 min)

  • Deep Learning principles, and why Deep Learning is deep
  • Artificial Neural Networks (ANN) step by step
  • Convolutional Neural Networks (CNN) for image recognition
  • Natural Language Processing (NLP) for speech recognition
  • Long Short-Term Memory (LSTM) networks
  • Large Language Models (LLM) vs NLP
  • Q&A

Segment 7: Introduction to Large Language Models (LLM) (35 min)

  • An introduction to LLMs
  • Foundational Models and Word Embedding
  • Transformers
  • Fine Tuning LLMs
  • Q&A

Course wrap-up and next steps (5 min)

Your Instructors

  • Rob Barton

    Rob Barton is a Distinguished Engineer with Cisco. Rob has worked in the IT industry for over 26 years, the last 23 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 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 wireless communications, network security, cloud networking, IoT, and Machine Learning. His current areas of work include wireless communications of all types, IT/OT convergence, network automation, and AI/ML in networking systems.

  • Jerome Henry

    Jerome Henry is a Principal Engineer in the Office of the Wireless CTO at Cisco. His main field of research is around optimization of clients performances in unlicensed wireless networks, which includes Machine Learning, IoT, indoor location, QoS and privacy. Jerome has more than 20 years 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. Jerome is a certified wireless networking expert (CWNE No. 45), CCIE, and has developed multiple courses and authored several wireless books and video courses. Jerome holds more than 300 patents, is a member of the IEEE, where he was elevated to Senior Member in 2013, and also represents Cisco with Wi-Fi Alliance working groups. He is based in Research Triangle Park, North Carolina.