Udemy - Loss Functions and Learning Rates for Data Scientists

Loss Functions & Learning Rates for Data Scientists

https://WebToolTip.com

Published 7/2026
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz, 2 Ch
Language: English | Duration: 2h 41m | Size: 967.08 MB

Loss Functions for Data Scientists: Minimize Error & Optimize Learning Rates in ML Models

What you'll learn
Select appropriate loss functions (MSE, MAE, Huber, log loss, cross‑entropy) for regression and classification tasks.
Explain how loss functions define model “error” and guide gradient descent and parameter updates in ML models.
Interpret loss curves to detect underfitting, overfitting, bad learning rates, and instability during model training.
Tune learning rates and related hyperparameters to avoid overshooting minima, divergence, or excessively slow training.
Diagnose training issues using loss behavior and apply fixes such as changing loss, learning rate, or regularization.

Requirements
Basic Python skills, including functions, loops, and basic NumPy and/or pandas.
Working knowledge of core ML models (linear/logistic regression, basic neural networks) and the train/validation/test workflow.
Comfort with basic calculus and probability ideas (derivatives as slope, mean, variance, simple logs), though formal proofs are not required.
Ability to run code in Jupyter or a similar environment (locally or in the cloud) to follow the hands‑on examples.