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IBM: Machine Learning with Python: A Practical Introduction

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Machine Learning can be an incredibly beneficial tool to uncover hidden insights and predict future trends. This Machine Learning with Python course will give you all the tools you need to get started with supervised and unsupervised learning.

Machine Learning with Python: A Practical Introduction
5 semanas
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Machine Learning with Python: A Practical Introduction

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Please Note: Learners who successfully complete this IBM course can earn a skill badge — a detailed, verifiable and digital credential that profiles the knowledge and skills you’ve acquired in this course. Enroll to learn more, complete the course and claim your badge!

This Machine Learning with Python course dives into the basics of machine learning using Python, an approachable and well-known programming language. You'll learn about supervised vs. unsupervised learning, look into how statistical modeling relates to machine learning, and do a comparison of each.

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such as Train/Test Split, Root Mean Squared Error (RMSE), and Random Forests. Along the way, you’ll look at real-life examples of machine learning and see how it affects society in ways you may not have guessed!

Most importantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

We'll explore many popular algorithms including Classification, Regression, Clustering, and Dimensional Reduction and popular models such asTrain/Test Split, Root Mean Squared Error and Random Forests.

Mostimportantly, you will transform your theoretical knowledge into practical skill using hands-on labs. Get ready to do more learning than your machine!

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Machine Learning with Python: A Practical Introduction

De un vistazo

  • Language English
  • Video Transcript English
  • Associated programs
  • Associated skillsUnsupervised Learning, Machine Learning, Statistical Modeling, Python (Programming Language), Random Forest Algorithm, Algorithms

Lo que aprenderás

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  • Explain the difference between the two main types of machine learning methods: supervised and unsupervised
  • Describe Supervised learning algorithms, including classification and regression
  • Describe Unsupervised learning algorithms, including Clustering and Dimensionality Reduction
  • Explain how statistical modelling relates to machine learning and how to compare them
  • Discuss real-life examples of the different ways machine learning affects society
  • Build a prediction model using classification

Plan de estudios

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Module 1 - Introduction to Machine Learning
Applications of Machine Learning
Supervised vs Unsupervised Learning
Python libraries suitable for Machine Learning

Module 2 - Regression
Linear Regression
Non-linear Regression
Model evaluation methods

Module 3 - Classification
K-Nearest Neighbour
Decision Trees
Logistic Regression
Support Vector Machines
Model Evaluation

Module 4 - Unsupervised Learning
K-Means Clustering
Hierarchical Clustering
Density-Based Clustering

Module 5 - Recommender Systems
Content-based recommender systems
Collaborative Filtering

¿Quién puede hacer este curso?

Lamentablemente, las personas residentes en uno o más de los siguientes países o regiones no podrán registrarse para este curso: Irán, Cuba y la región de Crimea en Ucrania. Si bien edX consiguió licencias de la Oficina de Control de Activos Extranjeros de los EE. UU. (U.S. Office of Foreign Assets Control, OFAC) para ofrecer nuestros cursos a personas en estos países y regiones, las licencias que hemos recibido no son lo suficientemente amplias como para permitirnos dictar este curso en todas las ubicaciones. edX lamenta profundamente que las sanciones estadounidenses impidan que ofrezcamos todos nuestros cursos a cualquier persona, sin importar dónde viva.

Este curso es parte del programa IBM Data Science Professional Certificate

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Instrucción por expertos
10 cursos de capacitación
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1 año
3 - 6 horas semanales

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Costo

99 US$

Free

Acceso a los materiales del curso

Ilimitado

Limitado

Caduca el 30 may

World class institutions and universities

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