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Imbalanced-learn

modelos de ML con datos desequilibrados

Leire Ahedo

Este proyecto es un curso práctico y efectivo para aprender que es el desbalanceo de clases en Machine leraning y como tratarlo. Aprenderemos las técnicas más avanzadas para trabajar con datos desbalanceados como: bSMOTE, ADASYN, SMOTEEN, etc. También aprenderemos a generar modelos capaces de trabajar con datos desbalanceados. Una gran parte de los problemas de clasificación utilizan datos debalanceadas. Si no se tratan estos casos estaremos generando modelos que no estén funcionando correctamente, pese a que a priori parezca que si. Por eso, en este curso aprenderemos a como tratar este tipo de datos.

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Provides an effective and practical approach to understanding and tackling class imbalance in machine learning

Introduces advanced techniques for working with imbalanced data, such as bSMOTE, ADASYN, and SMOTEEN

Helps learners develop models capable of handling imbalanced data

Covers a significant challenge in classification problems, as a large portion of them involve imbalanced data

Suitable for individuals with a foundational understanding of machine learning concepts

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Imbalanced-learn: modelos de ML con datos desequilibrados with these activities:

Review statistics fundamentals

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Refresher in statistics can help establish foundational understanding of sampling distributions and hypothesis testing for future work in machine learning.

Browse courses on Hypothesis Testing

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Organize Course Materials

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Organizing your notes, assignments, and exams will help you stay on top of the material and make it easier to study for exams.

Browse courses on Note-Taking

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Gather all of your course materials, including notes, assignments, quizzes, and exams.
Sort your materials into folders or binders.
Create a system for naming and organizing your files.

Gather resources on machine learning

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Compiling resources on machine learning can aid in building a knowledge base and foster a deeper understanding of concepts and algorithms.

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Search for articles, books, and online courses on machine learning
Organize resources by topic and subtopic
Review resources regularly to reinforce learning

Seven other activities

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Form a study group

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Engaging with peers in a study group can foster collaboration, discussion, and a deeper understanding of machine learning concepts.

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Find like-minded individuals who share an interest in machine learning
Set regular meeting times and establish a study agenda
Collaborate on projects and assignments
Provide feedback and support to each other's learning

Connect with experienced machine learning practitioners

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Seeking guidance from experienced individuals can accelerate learning and provide valuable insights into the field.

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Attend industry events and conferences
Reach out to professionals on LinkedIn or other platforms
Join online communities and forums

Experiment with a data set

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Working with data sets can help solidify understanding of machine learning fundamentals and develop a better intuition for algorithms.

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Import data from a pre-existing source
Explore the data and identify variables
Process and transform the data as needed
Train and evaluate a simple model

Solve coding challenges

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Engaging in coding challenges can enhance practical skills, improve problem-solving abilities, and deepen understanding of machine learning algorithms.

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Identify coding challenges related to machine learning
Attempt to solve challenges самостоятельно or with a group
Review solutions and identify areas for improvement

Build a simple machine learning model

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Building a machine learning model from scratch can help deepen understanding of the process and reinforce knowledge.

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Choose a data set and define a problem statement
Preprocess and explore the data
Select and train a model
Evaluate the model's performance

Contribute to open-source machine learning projects

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Contributing to open-source projects can enhance practical skills while fostering a sense of community and collaboration.

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Identify open-source projects related to machine learning
Review project documentation and identify areas to contribute
Submit code or documentation patches

Work on a machine learning project

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Tackling a machine learning project provides a comprehensive learning experience, fostering practical skills, problem-solving abilities, and a deeper understanding of the field.

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Define a project scope and objectives
Gather and prepare data
Train and evaluate models
Deploy and monitor the model

Career center

Learners who complete Imbalanced-learn: modelos de ML con datos desequilibrados will develop knowledge and skills that may be useful to these careers:

Data Scientist

A Data Scientist uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from raw data in various forms. This course on Imbalanced-learn may be useful for a Data Scientist because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Data Scientists can build more accurate and robust models.

See salaries and explore the career path for Data Scientist

Machine Learning Engineer

A Machine Learning Engineer designs, develops, and deploys machine learning models to solve real-world problems. This course on Imbalanced-learn may be useful for a Machine Learning Engineer because it teaches techniques for handling imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Machine Learning Engineers can build more accurate and robust models.

See salaries and explore the career path for Machine Learning Engineer

Data Analyst

A Data Analyst collects, analyzes, interprets, and presents data to help organizations make informed decisions. This course on Imbalanced-learn may be useful for a Data Analyst because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Data Analysts can provide more accurate and reliable insights to organizations.

See salaries and explore the career path for Data Analyst

Business Analyst

A Business Analyst bridges the gap between business and technology, helping organizations use data and technology to improve their operations and achieve their goals. This course on Imbalanced-learn may be useful for a Business Analyst because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Business Analysts can provide more accurate and reliable insights to organizations, helping them make better decisions.

See salaries and explore the career path for Business Analyst

Statistician

A Statistician collects, analyzes, interprets, and presents data to help organizations understand and make informed decisions. This course on Imbalanced-learn may be useful for a Statistician because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Statisticians can provide more accurate and reliable insights to organizations, helping them make better decisions.

See salaries and explore the career path for Statistician

Data Engineer

A Data Engineer designs, builds, and maintains data pipelines to ensure that data is available, reliable, and accessible to users. This course on Imbalanced-learn may be useful for a Data Engineer because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Data Engineers can build more reliable and robust data pipelines.

See salaries and explore the career path for Data Engineer

Software Engineer

A Software Engineer designs, develops, and maintains software systems. This course on Imbalanced-learn may be useful for a Software Engineer because it teaches techniques for handling imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Software Engineers can build more accurate and robust software systems.

See salaries and explore the career path for Software Engineer

Market Researcher

A Market Researcher conducts research to understand consumer behavior and market trends. This course on Imbalanced-learn may be useful for a Market Researcher because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Market Researchers can provide more accurate and reliable insights to organizations, helping them make better decisions.

See salaries and explore the career path for Market Researcher

Operations Research Analyst

An Operations Research Analyst uses mathematical and analytical techniques to solve complex business problems. This course on Imbalanced-learn may be useful for an Operations Research Analyst because it teaches techniques for handling imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Operations Research Analysts can develop more accurate and robust solutions to business problems.

See salaries and explore the career path for Operations Research Analyst

Financial Analyst

A Financial Analyst analyzes financial data and makes recommendations to help organizations make informed decisions. This course on Imbalanced-learn may be useful for a Financial Analyst because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Financial Analysts can provide more accurate and reliable insights to organizations, helping them make better decisions.

See salaries and explore the career path for Financial Analyst

Risk Analyst

A Risk Analyst identifies, assesses, and manages risks to help organizations make informed decisions. This course on Imbalanced-learn may be useful for a Risk Analyst because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Risk Analysts can provide more accurate and reliable insights to organizations, helping them make better decisions.

See salaries and explore the career path for Risk Analyst

Product Manager

A Product Manager is responsible for the overall success of a product, from its conception to its launch and beyond. This course on Imbalanced-learn may be useful for a Product Manager because it teaches techniques for handling imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Product Managers can make better decisions about product features and functionality.

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Actuary

An Actuary uses mathematical and statistical techniques to assess and manage risks in areas such as insurance and finance. This course on Imbalanced-learn may be useful for an Actuary because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Actuaries can provide more accurate and reliable insights to organizations, helping them make better decisions.

See salaries and explore the career path for Actuary

Auditor

An Auditor examines and evaluates financial records to ensure that they are accurate and compliant with regulations. This course on Imbalanced-learn may be useful for an Auditor because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Auditors can provide more accurate and reliable insights to organizations, helping them avoid financial risks.

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Consultant

A Consultant provides expert advice and guidance to organizations to help them solve problems and improve their performance. This course on Imbalanced-learn may be useful for a Consultant because it teaches techniques for working with imbalanced datasets, which are common in real-world data. By learning how to handle imbalanced data effectively, Consultants can provide more accurate and reliable insights to organizations, helping them make better decisions.

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Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Imbalanced-learn: modelos de ML con datos desequilibrados.

Self-Adaptive Systems for Machine Intelligence

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A specialized book on imbalanced learning, providing a comprehensive overview of different techniques and algorithms.

Imbalanced-learn

modelos de ML con datos desequilibrados

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