Machine Learning Foundations: Core Algorithms and Intuition
Learners will understand the mathematical intuition behind core machine learning algorithms and implement basic predictive models in Python. By completing hands-on examples, learners will prepare data, train standard regression, classification, and clustering models, and evaluate their real-world performance.
- Principiante
- Unas 5,7 horas
- English
- 5 módulos
- 10 lecciones
- 18 preguntas de cuestionario
Creado por Learnvoro Labs
Lo que aprenderás
- Explain the core mathematical concepts of vectors, matrices, probability distributions, and summary statistics required to formulate basic machine learning problems.
- Write introductory Python scripts using standard data libraries to load, manipulate, and inspect datasets for modeling.
- Differentiate between supervised, unsupervised, and reinforcement learning paradigms using concrete real-world problem scenarios.
- Formulate continuous prediction problems using linear regression and categorical prediction problems using logistic regression.
- Trace the step-by-step decision rules of decision tree classifiers and the centroid-assignment process of K-Means clustering on sample data.
- Evaluate model performance using standard validation techniques, confusion matrices, accuracy, and mean squared error.
Cómo aprenderás
- Lecciones paso a paso10 lecciones
- VideoleccionesEscucha mientras aprendes
- Diagramas visualesIdeas hechas visibles
- Lectura en profundidad
- Comprobaciones de conocimientos18 preguntas de cuestionario
Requisitos previos
- Basic high school algebra (arithmetic operations, solving simple linear equations, and reading line graphs)
- Familiarity with general computer usage and navigating a web browser or code editor
Para quién es este curso
- Adult professionals and career changers seeking a foundational, conceptually clear introduction to core machine learning principles and algorithms.
Contenido de un curso
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