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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...
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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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Know what's good
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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
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
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
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
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
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
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
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
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
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.

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.
A comprehensive guide to data mining and machine learning, including a chapter on imbalanced data and methods for dealing with it.
Covers the fundamentals of statistical learning, including a section on sampling and overfitting, which are relevant to understanding imbalanced data.
Provides an introduction to machine learning using the R programming language, with a section on data preprocessing and dealing with imbalanced data.
Focuses on practical aspects of predictive modeling, including a discussion on overfitting and techniques for evaluating model performance on imbalanced data.

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