AI and Machine Learning

Machine Learning Landscape & Taxonomy Explained: 10 Short Videos to Level Up Your AI Knowledge

This video series, Machine Learning Landscape & Taxonomy, led by Azumo’s AI/ML Specialist Guillermo Anende, breaks down foundational concepts in machine learning and AI. Across 10 concise, engaging videos, you’ll learn about supervised vs. unsupervised learning, anomaly detection, deep learning, reinforcement learning, LLMs, and more. Whether you’re a beginner or brushing up on fundamentals, this series makes complex ideas accessible and actionable.

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June 26, 2025

Welcome to our Machine Learning Landscape & Taxonomy video series, led by Guillermo Anende, AI/ML & Backend Developer at Azumo. In just a few minutes per episode, this 10-part series walks you through the building blocks of machine learning—from the basics of supervised and unsupervised learning to more advanced topics like anomaly detection, reinforcement learning, and large language models (LLMs). Whether you’re new to ML or just need a refresher, these videos are designed to clarify concepts, spark ideas, and show how machine learning applies to real-world use cases.

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Intro & Agenda - Foundations of Modern AI

Guillermo Germade opens the training by outlining the learning objectives, key topics, and real-world examples you’ll explore in this series. Get a quick roadmap of how each segment builds practical AI knowledge you can apply immediately.

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What Is Machine Learning? A 5-Minute Primer

This concise introduction defines machine learning, explains why it matters to every industry, and previews the core learning paradigms covered in the series. Perfect for viewers who need a clear, non-technical overview before diving deeper.

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Supervised vs. Unsupervised Learning -The Big Picture

Guillermo breaks down the two most common ML paradigms, comparing how each learns from data, typical use cases, and performance trade-offs. Understand when to label data, when not to, and how that decision shapes your project’s success.

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Supervised Learning in 7 Minutes - Algorithms & Examples

Explore regression, classification, and real-world case studies—credit scoring, spam filtering, and more. Guillermo explains how labeled data drives model training, evaluation metrics, and practical tips for choosing the right algorithm.

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Unsupervised Learning in 5 Minutes - Clustering & Insights

Learn how algorithms like K-Means, DBSCAN, and PCA uncover hidden patterns in unlabeled data. Guillermo shows quick examples—from customer segmentation to anomaly detection—illustrating why unsupervised methods are indispensable for exploratory analysis.

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Anomaly Detection vs. Classification - Credit-Card Fraud Walkthrough

Using a credit-card fraud scenario, Guillermo contrasts classification with anomaly detection, explaining data imbalance, false-positive costs, and model selection. A hands-on illustration of choosing the right technique for rare-event problems.

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Deep Learning Explained - Structured vs. Unstructured Data

See how neural networks handle images, audio, and text compared to traditional tabular data. Guillermo demystifies CNNs, RNNs, and transformers, highlighting when deep learning adds value and when simpler models still win.

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Reinforcement Learning - Quick Tour & Future Outlook

Get a high-level view of agents, rewards, and policy learning. From game-playing AIs to robotics, Guillermo outlines where reinforcement learning shines today and what breakthroughs could unlock next-gen autonomous systems.

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LLMs & AGI - Large Language Models Toward General Intelligence

Guillermo explains how transformer-based LLMs work, their leap in reasoning and creativity, and what they mean for the path to Artificial General Intelligence. Includes real examples of GPT-style models and ethical considerations.

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Computer Vision Essentials - Object Detection with Supervised Learning

Discover how convolutional neural networks power object detection, classification, and image segmentation. Guillermo walks through dataset prep, labeling, and evaluation, giving you a practical blueprint for launching a computer-vision project.

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Stay tuned for the next session in the series, where we’ll dive into Model Evaluation - focusing on how to assess performance in both classification and regression tasks. You’ll learn the key metrics, when to use them, and how to interpret results to improve your models.

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