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Juan Pablo Yepes
Bienvenidos a este curso basado en un proyecto de regresión logística con Numpy y Python. En este proyecto, aprenderás uno de los conceptos bases del machine learning sin usar ninguna de las bibliotecas o librerías populares de machine learning como scikit-learn y statsmodels. El objetivo de este proyecto es que implementes por ti mismo toda la carpintería, incluyendo descenso de gradiente, función de costo, y regresión logística, que se utilizan en diversos algoritmos de aprendizaje, para que tengas una comprensión más profunda de los fundamentos. Para cuando complete este proyecto, podrá construir un modelo de regresión logística utilizando Python y Numpy, realizar análisis de datos exploratorios básicos, e implementar el descenso de gradientes desde cero.
Read more
Bienvenidos a este curso basado en un proyecto de regresión logística con Numpy y Python. En este proyecto, aprenderás uno de los conceptos bases del machine learning sin usar ninguna de las bibliotecas o librerías populares de machine learning como scikit-learn y statsmodels. El objetivo de este proyecto es que implementes por ti mismo toda la carpintería, incluyendo descenso de gradiente, función de costo, y regresión logística, que se utilizan en diversos algoritmos de aprendizaje, para que tengas una comprensión más profunda de los fundamentos. Para cuando complete este proyecto, podrá construir un modelo de regresión logística utilizando Python y Numpy, realizar análisis de datos exploratorios básicos, e implementar el descenso de gradientes desde cero.
Read more
Bienvenidos a este curso basado en un proyecto de regresión logística con Numpy y Python. En este proyecto, aprenderás uno de los conceptos bases del machine learning sin usar ninguna de las bibliotecas o librerías populares de machine learning como scikit-learn y statsmodels. El objetivo de este proyecto es que implementes por ti mismo toda la carpintería, incluyendo descenso de gradiente, función de costo, y regresión logística, que se utilizan en diversos algoritmos de aprendizaje, para que tengas una comprensión más profunda de los fundamentos. Para cuando complete este proyecto, podrá construir un modelo de regresión logística utilizando Python y Numpy, realizar análisis de datos exploratorios básicos, e implementar el descenso de gradientes desde cero.
Este curso se ejecuta en la plataforma de proyectos prácticos de Coursera llamada Rhyme. En Rhyme, se realizan proyectos de forma práctica en el navegador. Tendrás acceso instantáneo a escritorios en la nube pre-configurados que contienen todo el software y los datos que necesitas para el proyecto. Todo ya está configurado directamente en tu navegador de Internet para que puedas concentrarte en el aprendizaje. Para este proyecto, obtendrás acceso instantáneo a un escritorio en la nube con Python, Jupyter, Numpy y Seaborn preinstalados.
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OpenCourser.com/course/3ux177/regresion
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What's inside
Syllabus
Introducción a Pandas para Data Science
Bienvenidos a este curso basado en un proyecto de regresión logística con NumPy y Python. En este proyecto, aprenderás uno de los conceptos bases del machine learning sin usar ninguna de las bibliotecas o librerías populares de machine learning como scikit-learn y statsmodels. El objetivo de este proyecto es que implementes por ti mismo toda la carpintería, incluyendo descenso de gradiente, función de costo, y regresión logística, que se utilizan en diversos algoritmos de aprendizaje, para que tengas una comprensión más profunda de los fundamentos.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Teaches core machine learning concepts using Python and NumPy
Designed to build a deep understanding of the fundamentals of machine learning
Suitable for individuals with basic data science or programming experience
Provides practical experience in implementing machine learning algorithms from scratch
May be challenging for beginners with limited programming or machine learning knowledge
Save this course
Save Regresión logística con NumPy y Python to your list so you can find it easily later:
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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 Regresión logística con NumPy y Python with these
activities:
Organize your learning resources
Show steps
Organize your notes, assignments, quizzes, and exams to create a cohesive and easily accessible knowledge base, facilitating efficient review and retention of course materials.
Show steps
-
Create a dedicated folder or notebook for course materials.
-
Regularly add and update your materials.
-
Review your organized materials periodically.
Review linear algebra concepts
Show steps
Refresh your knowledge of linear algebra concepts such as vectors, matrices, and linear transformations to strengthen your understanding of machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review notes and textbooks from previous linear algebra courses.
-
Solve practice problems and examples to test your understanding.
-
Attend online tutorials or workshops to reinforce key concepts.
Participate in online discussions
Show steps
Engage with fellow students in online forums or discussion groups to exchange ideas, ask questions, and deepen your understanding of the concepts through collaborative learning.
Show steps
-
Join online discussion forums or groups related to logistic regression.
-
Post questions or comments to initiate discussions.
-
Actively participate in discussions by responding to others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement gradient descent from scratch
Show steps
Gain hands-on experience in implementing gradient descent, a fundamental optimization algorithm used in machine learning, to enhance your understanding of its mechanics.
Show steps
-
Study the mathematical concepts behind gradient descent.
-
Code the gradient descent algorithm in Python or NumPy.
-
Apply your implementation to a simple data set.
-
Analyze the results and adjust your code as needed.
Explore advanced topics in logistic regression
Show steps
Expand your knowledge of logistic regression by exploring advanced topics such as regularization, overfitting, and ROC curves, enhancing your understanding of the model's capabilities and limitations.
Browse courses on
Logistic Regression
Show steps
-
Watch online tutorials or attend webinars on advanced logistic regression topics.
-
Read research papers or articles to gain deeper insights.
-
Discuss your findings with classmates or instructors.
Build a logistic regression model using Numpy
Show steps
Apply your knowledge of logistic regression and Numpy to create a functional model that can make predictions based on real-world data, solidifying your understanding of the concepts.
Show steps
-
Choose a dataset and prepare it for modeling.
-
Implement the logistic regression algorithm in Numpy.
-
Train and evaluate your model using cross-validation.
-
Interpret the results and make predictions.
Write a blog post or article on logistic regression
Show steps
Consolidate your understanding of logistic regression by writing and sharing an article or blog post, allowing you to articulate concepts clearly and deepen your knowledge through the process of explanation.
Show steps
-
Choose a specific aspect of logistic regression to focus on.
-
Research and gather relevant information.
-
Write your article or blog post, explaining the concepts clearly.
-
Publish and share your article or blog post online.
Participate in a data science competition
Show steps
Apply your skills in a practical and competitive setting by participating in a data science competition, challenging yourself to solve real-world problems and further hone your abilities.
Show steps
-
Find and register for a data science competition that aligns with your interests.
-
Study the competition data and problem statement.
-
Develop and implement your solution.
-
Submit your solution and analyze the results.
Organize your learning resources
Show steps
Organize your notes, assignments, quizzes, and exams to create a cohesive and easily accessible knowledge base, facilitating efficient review and retention of course materials.
Show steps
Show steps
Show steps
- Create a dedicated folder or notebook for course materials.
- Regularly add and update your materials.
- Review your organized materials periodically.
Review linear algebra concepts
Show steps
Refresh your knowledge of linear algebra concepts such as vectors, matrices, and linear transformations to strengthen your understanding of machine learning algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes and textbooks from previous linear algebra courses.
- Solve practice problems and examples to test your understanding.
- Attend online tutorials or workshops to reinforce key concepts.
Participate in online discussions
Show steps
Engage with fellow students in online forums or discussion groups to exchange ideas, ask questions, and deepen your understanding of the concepts through collaborative learning.
Show steps
Show steps
Show steps
- Join online discussion forums or groups related to logistic regression.
- Post questions or comments to initiate discussions.
- Actively participate in discussions by responding to others.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Implement gradient descent from scratch
Show steps
Gain hands-on experience in implementing gradient descent, a fundamental optimization algorithm used in machine learning, to enhance your understanding of its mechanics.
Show steps
Show steps
Show steps
- Study the mathematical concepts behind gradient descent.
- Code the gradient descent algorithm in Python or NumPy.
- Apply your implementation to a simple data set.
- Analyze the results and adjust your code as needed.
Explore advanced topics in logistic regression
Show steps
Expand your knowledge of logistic regression by exploring advanced topics such as regularization, overfitting, and ROC curves, enhancing your understanding of the model's capabilities and limitations.
Browse courses on
Logistic Regression
Show steps
Show steps
Show steps
- Watch online tutorials or attend webinars on advanced logistic regression topics.
- Read research papers or articles to gain deeper insights.
- Discuss your findings with classmates or instructors.
Build a logistic regression model using Numpy
Show steps
Apply your knowledge of logistic regression and Numpy to create a functional model that can make predictions based on real-world data, solidifying your understanding of the concepts.
Show steps
Show steps
Show steps
- Choose a dataset and prepare it for modeling.
- Implement the logistic regression algorithm in Numpy.
- Train and evaluate your model using cross-validation.
- Interpret the results and make predictions.
Write a blog post or article on logistic regression
Show steps
Consolidate your understanding of logistic regression by writing and sharing an article or blog post, allowing you to articulate concepts clearly and deepen your knowledge through the process of explanation.
Show steps
Show steps
Show steps
- Choose a specific aspect of logistic regression to focus on.
- Research and gather relevant information.
- Write your article or blog post, explaining the concepts clearly.
- Publish and share your article or blog post online.
Participate in a data science competition
Show steps
Apply your skills in a practical and competitive setting by participating in a data science competition, challenging yourself to solve real-world problems and further hone your abilities.
Show steps
Show steps
Show steps
- Find and register for a data science competition that aligns with your interests.
- Study the competition data and problem statement.
- Develop and implement your solution.
- Submit your solution and analyze the results.
Career center
Learners who complete Regresión logística con NumPy y Python will develop knowledge and skills
that may be useful to these careers:
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use Python and Numpy to build financial models. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for quantitative analysts. This course also teaches you how to perform basic data analysis, which is another essential skill for quantitative analysts.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers use Python and Numpy to build machine learning models. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for machine learning engineers. This course also teaches you how to perform basic data analysis, which is another essential skill for machine learning engineers.
Data Scientist
Data Scientist
To be a successful Data Scientist, you need to know how to use Python, Numpy, and Pandas. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for data scientists. This course also teaches you how to perform basic data analysis, which is another essential skill for data scientists.
Data Analyst
Data Analyst
Data Analysts use Python and Numpy to analyze data and find trends. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for data analysts. This course also teaches you how to perform basic data analysis, which is another essential skill for data analysts.
Business Analyst
Business Analyst
Business Analysts use Python and Numpy to analyze data and make recommendations to businesses. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for business analysts. This course also teaches you how to perform basic data analysis, which is another essential skill for business analysts.
Statistician
Statistician
Statisticians use Python and Numpy to analyze data and draw conclusions. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for statisticians. This course also teaches you how to perform basic data analysis, which is another essential skill for statisticians.
Operations Research Analyst
Operations Research Analyst
Operations Research Analysts use Python and Numpy to solve complex problems. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for operations research analysts. This course also teaches you how to perform basic data analysis, which is another essential skill for operations research analysts.
Financial Analyst
Financial Analyst
Financial Analysts use Python and Numpy to analyze financial data. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for financial analysts. This course also teaches you how to perform basic data analysis, which is another essential skill for financial analysts.
Risk Analyst
Risk Analyst
Risk Analysts use Python and Numpy to analyze risk. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for risk analysts. This course also teaches you how to perform basic data analysis, which is another essential skill for risk analysts.
Software Engineer
Software Engineer
Software Engineers use Python and Numpy to build software applications. This course teaches you how to use these libraries to build a logistic regression model, which is a fundamental skill for software engineers. This course also teaches you how to perform basic data analysis, which is another essential skill for software engineers.
Customer Success Manager
Customer Success Manager
Customer Success Managers use Python and Numpy to analyze data and make decisions about customer success strategies. This course teaches you how to use these libraries to build a logistic regression model, which may be useful for customer success managers who need to analyze data and make decisions about customer success strategies.
Project Manager
Project Manager
Project Managers use Python and Numpy to manage projects. This course teaches you how to use these libraries to build a logistic regression model, which may be useful for project managers who need to analyze data and make decisions.
Sales Manager
Sales Manager
Sales Managers use Python and Numpy to analyze data and make decisions about sales strategies. This course teaches you how to use these libraries to build a logistic regression model, which may be useful for sales managers who need to analyze data and make decisions about sales strategies.
Product Manager
Product Manager
Product Managers use Python and Numpy to analyze data and make decisions about products. This course teaches you how to use these libraries to build a logistic regression model, which may be useful for product managers who need to analyze data and make decisions about products.
Marketing Manager
Marketing Manager
Marketing Managers use Python and Numpy to analyze data and make decisions about marketing campaigns. This course teaches you how to use these libraries to build a logistic regression model, which may be useful for marketing managers who need to analyze data and make decisions about marketing campaigns.
For more career information including salaries, visit:
OpenCourser.com/course/3ux177/regresion
Reading list
We've selected seven 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
Regresión logística con NumPy y Python.
Provides a comprehensive overview of statistical learning methods, including regression, classification, and clustering. It valuable resource for anyone who wants to learn more about the fundamentals of machine learning and how it can be applied to real-world problems.
Save
Provides a comprehensive overview of deep learning methods. It valuable resource for anyone who wants to learn more about the fundamentals of deep learning and how it can be applied to real-world problems.
Provides a comprehensive overview of regression modeling techniques. It valuable resource for anyone who wants to learn more about the fundamentals of regression modeling and how it can be applied to actuarial and financial problems.
Provides a comprehensive overview of statistical methods used in data science. It valuable resource for anyone who wants to learn more about the fundamentals of statistical methods and how they can be applied to data science problems.
Provides a gentle introduction to machine learning for beginners. It covers a wide range of topics, including supervised learning, unsupervised learning, and deep learning.
Provides a comprehensive overview of Python programming for data analysis. It valuable resource for anyone who wants to learn more about the fundamentals of Python programming and how it can be applied to data analysis problems.
Provides a comprehensive overview of NumPy programming for data science. It valuable resource for anyone who wants to learn more about the fundamentals of NumPy programming and how it can be applied to data science problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3ux177/regresion
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Effort
2 horas
Level
Introductory
Via
Coursera
Institution
Coursera Project Network
Instructor
Juan Pablo Yepes
Language
Español
Good to know
Teaches core machine learning concepts using Python and NumPy
Designed to build a deep understanding of the fundamentals of machine learning
Suitable for individuals with basic data science or programming experience
Provides practical experience in implementing machine learning algorithms from scratch
May be challenging for beginners with limited programming or machine learning knowledge
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