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INTRODUCCIÓN A MACHINE LEARNING

Track :

Programming

Lessons no : 16

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What will you learn in this course?
  • Develop and implement basic machine learning algorithms for predictive modeling and data analysis using Python and scikit-learn
  • Analyze data patterns and relationships to improve model accuracy and performance in real-world applications
  • Apply data preprocessing techniques such as normalization, feature selection, and data cleaning for machine learning projects
  • Evaluate model performance using metrics like accuracy, precision, recall, and F1 score to optimize predictive outcomes
  • Utilize supervised and unsupervised learning methods to solve classification, regression, and clustering problems
  • Interpret machine learning results to make data-driven decisions in business, healthcare, finance, and technology sectors
  • Design simple machine learning workflows, including data collection, model training, validation, and deployment
  • Identify ethical considerations and biases in machine learning models to ensure fair and responsible AI solutions

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Lessons | 16


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Swayam Upadhyay

not bad
2025-11-21

Sakthi

good
2025-09-09

CHENNA SAI VIKAS

śṁjbASEF.
2025-04-04

Husniya Musema Shifa

Thanks for all
2025-01-28

Ashwini R

GOOD
2025-01-09

Gaurav Rautela

good
2024-09-12

Harshith H S

Good
2024-06-27

Kalpesh Chandane

It's very good experience
2023-12-17

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Machine learning es la ciencia en la que, para predecir un valor, se aplican algoritmos para que un sistema aprenda patrones dentro de los datos. Con el uso de datos suficientes, se establece la relación entre todas las variables de entrada y los valores a predecir .