Based on the “Machine Learning ” crash course from Google for Developers:⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠https://developers.google.com/machine-learning/crash-course⁠Linear Regression - Yale UniversityLinear and Logistic Regression - Stanford UniversityIn this episode, we'll demystify Linear Regression, exploring its power in predicting continuous values and understanding its core mechanics, from the "best-fit line" to the critical role of the least squares method. Discover real-world applications where predicting "how much" is key, and learn how to evaluate its performance effectively.Then, we'll pivot to Logistic Regression, a cornerstone for classification tasks. Understand how it tackles "yes/no" questions by predicting probabilities using the elegant sigmoid function. We'll delve into its distinct mathematical underpinnings and uncover its vital role in scenarios ranging from spam detection to medical diagnostics, alongside its unique evaluation metrics.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.

Ver todos los episodios