Human in loop podcasts

Priti Y.
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Educational podcasts blending human research and AI-generated content, promoting curiosity, critical thinking, and lifelong learning.

  1. 2025. 07. 17.

    Demystifying Overfitting & Model Complexity in ML

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠https://developers.google.com/machine-learning/crash-course⁠⁠⁠ In this episode, we’ll learn what overfitting is, how to detect it using loss curves, why model complexity matters, and how techniques like L2 regularization help improve generalization to unseen data. Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    6분

  2. 2025. 07. 17.

    Data Prep in Machine Learning: Training, Validation & Test Sets Explained

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠https://developers.google.com/machine-learning/crash-course⁠⁠⁠ In this episode, we break down how machine learning models learn effectively by splitting data into training, validation, and test sets. Understand the purpose of each set, why this separation matters, and how it helps reduce overfitting while improving generalization. Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    6분

  3. 2025. 07. 17.

    Working with Categorial Data in Machine Learning

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠https://developers.google.com/machine-learning/crash-course⁠⁠ This episode explores how categorical data is transformed into usable features for machine learning models. From understanding one-hot encoding to tackling real-world labeling challenges and applying feature crosses, we break down key techniques to handle non-numeric data effectively. Perfect for anyone aiming to build better models using human-defined categories. Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    7분

  4. 2025. 07. 17.

    Classification Metrics Made Simple: Precision, Recall & More

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ https://developers.google.com/machine-learning/crash-course In this episode, we dive into the world of classification in machine learning—exploring how models make decisions and how we evaluate their performance. You'll learn what a confusion matrix is, how thresholds affect predictions, and what metrics like accuracy, precision, recall, and F1 score really mean in practice. Whether you're new to ML or brushing up on the fundamentals, this episode will give you the clarity you need to confidently interpret model results. Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    7분

  5. 2025. 07. 17.

    First Steps with Numerical Data in Machine Learning

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠https://developers.google.com/machine-learning/crash-course⁠ Before feeding data into a machine learning model, it’s crucial to understand it. This episode walks you through the essential first steps: visualizing data, calculating basic statistics like mean and percentiles, and spotting outliers that could skew your model. Whether you're using pandas or plotting histograms, these techniques lay the foundation for effective ML pipelines. Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    9분

  6. 2025. 07. 17.

    Understanding Model Confidence: ROC, AUC & Prediction Bias

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠https://developers.google.com/machine-learning/crash-course⁠In this episode, we go beyond accuracy and dive into how to evaluate model performance across all thresholds using ROC curves and AUC. We also break down prediction bias—why your model might look accurate but still be off-target—and how to detect early signs of bias caused by data, features, or training bugs. Tune in to learn how to make more reliable and fair classification models! Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    7분

  7. 2025. 07. 17.

    Making Predictions with Logistic Regression

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠https://developers.google.com/machine-learning/crash-course⁠ In this episode, we break down logistic regression—a core algorithm used for classification. You'll learn how it calculates probabilities instead of direct values, what loss functions are used to measure mistakes, and how regularization helps prevent overfitting. It’s all about teaching machines to say “yes” or “no” with confidence. Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    11분

  8. 2025. 07. 17.

    Gradient Descent & Hyperparameters

    Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠https://developers.google.com/machine-learning/crash-course⁠ What drives a machine learning model to learn? In this episode, we explore gradient descent, the optimization engine behind linear regression, and the crucial role of hyperparameters like learning rate, batch size, and epochs. Understand how models reduce error step by step, and why tuning hyperparameters can make or break performance. Whether you're a beginner or reviewing the basics, this episode brings clarity with real-world analogies and practical takeaways. Disclaimer: This podcast is generated using an AI avatar voice. At times, you may notice overlapping sentences or background noise. That said, all content is directly based on the official course material to ensure accuracy and alignment with the original learning experience.

    7분
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Educational podcasts blending human research and AI-generated content, promoting curiosity, critical thinking, and lifelong learning.

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  • 제작진
    Priti Y.
  • 방송 연도
    2025년
  • 에피소드
    16
  • 등급
    전체 연령 사용가
  • 저작권
    © Priti Y.
  • 웹사이트 보기
    Human in loop podcasts