In our very first episode, we had the pleasure of chatting with Luis Serrano—one of the top voices in the AI space. Luis Serrano is a technology and science popularizer, researcher, and practitioner and author of the best-selling book Grokking Machine Learning.

He is currently the developer relations lead at Cohere, and has previously worked at several tech companies including Google and Apple. He's also the brains behind popular ML courses on platforms like Coursera and Udacity, and the popular YouTube channel Serrano Academy, with over 135K subscribers.

In this episode, we unpack Luis's fascinating journey, from his childhood and maths fears to a deep-seated passion for it and all things related, including AI and ML. We explore his career path in detail uncovering the pivotal moments and learnings, as he navigated through big tech players, changing gears from Maths to AI and Quantum AI, and how he ultimately found his true calling.

We further venture into the world of AI, exploring its profound impact on education and society—both the positive advancements and the challenges it presents, and how they are reshaping the world and future. And of course, we touch upon the human side of it all—exploring the themes of humanity and empathy and implications for the future.

The podcast ends with a fun and engaging rapid-fire round, again packed with bite-sized learning. So tune in, learn and get inspired!

Discover valuable machine learning techniques you can understand and apply using just high-school math. In Grokking Machine Learning you will learn: Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math. No specialist knowledge is required to tackle the hands-on exercises using Python and readily available machine learning tools. Packed with easy-to-follow Python-based exercises and mini-projects, this book sets you on the path to becoming a machine learning expert. About the Technology Discover powerful machine learning techniques you can understand and apply using only high school math! Put simply, machine learning is a set of techniques for data analysis based on algorithms that deliver better results as you give them more data. ML powers many cutting-edge technologies, such as recommendation systems, facial recognition software, smart speakers, and even self-driving cars. This unique book introduces the core concepts of machine learning, using relatable examples, engaging exercises, and crisp illustrations. About the Book Grokking Machine Learning presents machine learning algorithms and techniques in a way that anyone can understand. This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you’ll build interesting projects with Python, including models for spam detection and image recognition. You’ll also pick up practical skills for cleaning and preparing data. What's Inside Supervised algorithms for classifying and splitting data Methods for cleaning and simplifying data Machine learning packages and tools Neural networks and ensemble methods for complex datasets About the Reader For readers who know basic Python. No machine learning knowledge necessary. About the Author Luis G. Serrano is a research scientist in quantum artificial intelligence. Previously, he was a Machine Learning Engineer at Google and Lead Artificial Intelligence Educator at Apple. Quotes Did you think machine learning is complicated and hard to master? It’s not! Read this book! Serrano demystifies some of the best-held secrets of the machine learning society. - Sebastian Thrun, Founder, Udacity The first step to take on your machine learning journey. - Millad Dagdoni, Norwegian Labour and Welfare Administration A nicely written guided introduction, especially for those who want to code but feel shaky in their mathematics. - Erik D. Sapper, California Polytechnic State University The most approachable introduction to machine learning I’ve had the pleasure to read in recent years. Highly recommended. - Kay Engelhardt, devstats