AI-ML Decoded: From Fundamentals to Future

The world of AI moves fast. This podcast bridges the gap between simple overviews and dense technicality. Whether you are a Data Scientist prepping for interviews, a student navigating the math, a business leader, or just a curious soul, this is your roadmap. We skip the hype to provide a rigorous, practical guide to the "how" and "why" of Machine Learning.Note: In the spirit of the topic, this show uses AI-generated voices and scripts. However, accuracy is our priority: all content is rigorously verified by a human expert with a PhD in Engineering and professional ML experience.

AI-ML Decoded: From Fundamentals to Future is a education podcast hosted by Elius Etienne, with 15 episodes on record and a Required Pod Score of 80. PitchCentric scores this show on Booking Probability, Listen Score, and live audience signals refreshed every 24 hours.

Elius Etienne hosts AI-ML Decoded: From Fundamentals to Future, a education show with 15 episodes published.

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  Episode #14

  E14. An Overview of MLOps

  Jan 11, 20269 minS1

  <p>Episode 14: An Overview of MLOps</p><p>In our season finale, we answer the most practical question of all: What happens after the model is trained? We explore MLOps—the critical "assembly line" practices that take a model from a laptop experiment to a production-ready system.</p><p>In this episode, we cover:</p><p>The Origin: How the concept of "Technical Debt" led to merging ML with DevOps.</p><p>The Pipeline: A tour of the 5 key components, including Feature Stores, Deployment, and Monitoring.</p><p>The Enemy: Understanding Data Drift and Model Drift (why models get worse over time).</p><...

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  Episode #13

  E13. An Overview of Machine Learning Libraries

  Jan 11, 20266 minS1

  <p>Episode 13: An Overview of Machine Learning Libraries</p><p>Developers don't build AI from scratch. In this episode, we open the toolbox to explore the essential Machine Learning Libraries and frameworks that power the industry.</p><p>In this episode, we cover:</p><p>The Foundation: Why NumPy and its "tensors" are the bedrock of all ML code.</p><p>The Core Frameworks:</p><p>TensorFlow: Google's powerhouse for production and scaling.</p><p>Keras: The user-friendly interface for deep learning.</p><p>PyTorch: Meta's flexible favorite for researchers.</p><p>Scikit-learn: The standard for traditional algorithms (Regression/Clustering).</p><p>Spec...

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  Episode #12

  E12. An Overview of Model Training

  Jan 11, 20269 minS1

  <p>Episode 12: An Overview of Model Training</p><p>We've used the word "training" in every episode. Now, we break down exactly what it means. In this episode, we explore the step-by-step workflow of how a model actually "learns" from data.</p><p>In this episode, we cover:</p><p>The Core Concept: How "learning" is really just adjusting mathematical Weights and Biases to minimize a Loss Function.</p><p>Model vs. Algorithm: Why these terms aren't interchangeable (Recipe vs. Meal).</p><p>The 3 Paradigms Recap: A quick look at how Supervised, Unsupervised, and Reinforcement learning differ in their goals (Accuracy...

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  Episode #11

  E11. An Overview of Generative AI

  Jan 11, 202612 minS1

  <p>Episode 11: An Overview of Generative AI</p><p>It’s the topic everyone is talking about. In this episode, we broaden our scope from NLP to the entire field of Generative AI—the technology that creates original text, images, code, and audio from a simple prompt.</p><p>In this episode, we cover:</p><p>The Surge: How ChatGPT thrust AI into the headlines and what analysts predict for 2026.</p><p>The 3 Phases:</p><p>Training: Building massive Foundation Models on petabytes of data.</p><p>Tuning: Customizing via Fine-Tuning and RLHF (Human Feedback).</p><p>Generation: Using RAG (Retrieval Augment...

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  Episode #10

  E10. An Overview of Natural Language Processing

  Jan 11, 20268 minS1

  <p>Episode 10: An Overview of Natural Language Processing</p><p>If Computer Vision allows machines to "see," Natural Language Processing (NLP) allows them to "understand." In this episode, we explore the science behind how computers communicate, from old-school spellcheckers to the Transformers powering ChatGPT.</p><p>In this episode, we cover:</p><p>The Evolution: From rigid Rules-Based systems to Statistical N-Grams and today’s Deep Learning models.</p><p>The Pipeline: How raw text is transformed via Tokenization, Lemmatization, and Word Embeddings (like Word2Vec).</p><p>Key Tasks:</p><p>Named Entity Recognition (NER): Identifying people and places.</p><p>Sentiment An...

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