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SAEED AGHABOZORGI and Joseph Santarcangelo
Este curso se sumerge en los conceptos básicos del aprendizaje automático mediante un lenguaje de programación accesible y conocido, Python. En este curso, repasaremos dos componentes principales: Primero, aprenderá sobre el propósito del aprendizaje automático y dónde se aplica al mundo real. En segundo lugar, obtendrá una descripción general de los temas del aprendizaje automático, como el aprendizaje supervisado o no supervisado, la evaluación de modelos y los algoritmos del aprendizaje automático. En este curso, practicarás con ejemplos de la vida real de aprendizaje automático y verás cómo afecta a la sociedad de formas que...
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Este curso se sumerge en los conceptos básicos del aprendizaje automático mediante un lenguaje de programación accesible y conocido, Python. En este curso, repasaremos dos componentes principales: Primero, aprenderá sobre el propósito del aprendizaje automático y dónde se aplica al mundo real. En segundo lugar, obtendrá una descripción general de los temas del aprendizaje automático, como el aprendizaje supervisado o no supervisado, la evaluación de modelos y los algoritmos del aprendizaje automático. En este curso, practicarás con ejemplos de la vida real de aprendizaje automático y verás cómo afecta a la sociedad de formas que quizás no hayas adivinado. Con solo dedicar unas horas a la semana durante las próximas semanas, esto es lo que obtendrá. 1) Nuevas habilidades para agregar a su currículum, como regresión, clasificación, agrupamiento, aprendizaje de sci-kit y SciPy 2) Nuevos proyectos que puede agregar a su cartera, incluida la detección de cáncer, la predicción de tendencias económicas, la predicción de la rotación de clientes, los motores de recomendación y muchos más. 3) Y un certificado en aprendizaje automático para demostrar su competencia y compartirlo en cualquier lugar que desee en línea o fuera de línea, como perfiles de LinkedIn y redes sociales. Si elige tomar este curso y obtener el certificado del curso de Coursera, también obtendrá una insignia digital de IBM al completar con éxito el curso.
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Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Explores machine learning, which is highly relevant to data science
Uses Python, which is an industry-standard language
Taught by Joseph Santarcangelo and SAEED AGHABOZORGI, who are recognized experts
Includes hands-on examples that show how machine learning can be applied in real settings
Provides opportunities to build a portfolio of machine learning projects
Requires basic programming and math skills

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Reviews summary

Effective machine learning with python

This course dives into the basics of Machine Learning with a popular programming language, Python. Many reviewers found this course helpful and interesting. Concepts are well explained and there are examples throughout the course.
Course has good examples of real-life uses of ML.
"... pude hacer mis proyectos , como por ejemplo algoritmos de arboles de decision, K- vecinos, etc."
"IBM/Coursera Data Science Professional Certificate opened the doors of knowledge of the world of data science, and made me more prepared and inspired to continue expanding the search for knowledge in data science my special thanks Coursera/IBM!"
Concepts are well-explained.
"En esta parte del curso he aprendido a utilizar diferentes técnicas de aprendizaje automático y a ver su aplicación práctica"
Took a long time to receive a grade on final project.
"tuve problemas para que alguien me calificara el ejercicio final y pasó un mes desde que envié el ejercicio final hasta que me calificaron"

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 Aprendizaje Automático con Python with these activities:
Brush Up on Basics of Programming with Python
Familiarize yourself with Python syntax, basic data types, variables, and flow control to strengthen your foundation.
Browse courses on Python Basics
Show steps
  • Review online tutorials or courses on introductory Python concepts.
  • Practice writing simple Python programs to reinforce your understanding.
Participate in a Machine Learning Study Group
Connect with fellow learners and exchange knowledge, insights, and support through regular study sessions.
Show steps
  • Find or create a study group with individuals of similar interests.
  • Establish a schedule and meeting format.
  • Discuss course material, work on assignments, and share resources.
Explore Scikit-learn Tutorials
Deepen your understanding of Scikit-learn's machine learning algorithms and techniques by working through guided examples.
Browse courses on scikit-learn
Show steps
  • Identify relevant tutorials on Scikit-learn's website or other online resources.
  • Follow the tutorials step-by-step, implementing the code and understanding the concepts.
Four other activities
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Show all seven activities
Contribute to Open-Source Machine Learning Projects
Gain practical experience and enhance your understanding of machine learning by contributing to open-source projects.
Browse courses on Machine Learning Projects
Show steps
  • Identify open-source machine learning repositories that align with your interests.
  • Review the project documentation and identify areas where you can contribute.
  • Make code contributions, submit bug reports, or participate in discussions.
Solve Coding Challenges on Leetcode
Strengthen your problem-solving and coding skills by practicing on Leetcode's curated coding challenges.
Browse courses on Coding Challenges
Show steps
  • Select problems appropriate to your skill level.
  • Attempt to solve the problems on your own before looking at solutions.
  • Review solutions and identify areas for improvement.
Write a Comprehensive Report on Machine Learning Applications
Demonstrate your understanding of machine learning applications by researching and writing a detailed report on their impact in various industries.
Show steps
  • Choose a specific industry or domain to focus on.
  • Research and gather information on machine learning applications within that domain.
  • Analyze the data and identify trends and patterns.
  • Organize and present your findings in a well-written report.
Develop a Machine Learning Model for a Real-World Problem
Apply your knowledge to a practical project by building a machine learning model that addresses a real-world problem.
Show steps
  • Identify a problem that aligns with your interests or industry.
  • Gather and prepare the necessary data.
  • Choose and implement an appropriate machine learning algorithm.
  • Evaluate the performance of your model and iterate to improve its accuracy.

Career center

Learners who complete Aprendizaje Automático con Python will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineers are responsible for designing, developing, and deploying machine learning models. They work closely with Data Scientists to identify business problems that can be solved with machine learning, and then develop and implement the necessary models. The Aprendizaje Automático con Python course can be a valuable resource for aspiring Machine Learning Engineers. This course will provide a solid foundation in machine learning concepts and techniques, which are essential for Machine Learning Engineers.
Data Scientist
The role of a Data Scientist is to use data to solve business problems and uncover trends. Data Scientists are responsible for collecting, cleaning, and analyzing data to find patterns and insights. They then use these insights to develop predictive models and make recommendations. The Aprendizaje Automático con Python course can be a valuable resource for aspiring Data Scientists. This course will provide a solid foundation in machine learning concepts and techniques, which are essential for Data Scientists.
Data Analyst
Data Analysts are responsible for collecting, cleaning, and analyzing data to identify trends and patterns. They use their findings to make recommendations to businesses on how to improve their operations. The Aprendizaje Automático con Python course can be a valuable resource for aspiring Data Analysts. This course will provide a solid foundation in machine learning concepts and techniques, which can be used to enhance data analysis.
Statistician
Statisticians are responsible for collecting, analyzing, and interpreting data. They use their findings to make recommendations to businesses and organizations on how to make informed decisions. The Aprendizaje Automático con Python course may be a useful resource for aspiring Statisticians who want to learn about machine learning. This course can help Statisticians build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated statistical models.
Software Engineer
Software Engineers are responsible for designing, developing, and maintaining software applications. They work with a variety of programming languages and technologies to create software that meets the needs of users. The Aprendizaje Automático con Python course may be a useful resource for aspiring Software Engineers who want to learn about machine learning. This course can help Software Engineers build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated software applications.
Financial Analyst
Financial Analysts are responsible for analyzing financial data and making recommendations to investors. They use a variety of techniques to analyze financial data, including machine learning. The Aprendizaje Automático con Python course may be a useful resource for aspiring Financial Analysts who want to learn about machine learning. This course can help Financial Analysts build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated financial analysis models.
Risk Analyst
Risk Analysts are responsible for identifying and assessing risks to businesses. They use a variety of methods to assess risk, including machine learning. The Aprendizaje Automático con Python course may be a useful resource for aspiring Risk Analysts who want to learn about machine learning. This course can help Risk Analysts build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated risk assessment models.
Product Manager
Product Managers are responsible for developing and managing products. They work with a variety of stakeholders to define product requirements, design product features, and launch products to market. The Aprendizaje Automático con Python course may be a useful resource for aspiring Product Managers who want to learn about machine learning. This course can help Product Managers build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated products.
Consultant
Consultants are responsible for providing advice to businesses and organizations on a variety of topics. They use a variety of techniques to provide advice, including machine learning. The Aprendizaje Automático con Python course may be a useful resource for aspiring Consultants who want to learn about machine learning. This course can help Consultants build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated consulting advice.
Quantitative Analyst
Quantitative Analysts are responsible for using mathematical and statistical models to analyze financial data. They use their findings to make recommendations to investors on how to allocate their assets. The Aprendizaje Automático con Python course may be a useful resource for aspiring Quantitative Analysts who want to learn about machine learning. This course can help Quantitative Analysts build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated financial models.
Actuary
Actuaries are responsible for assessing and managing financial risk. They use a variety of techniques to assess risk, including machine learning. The Aprendizaje Automático con Python course may be a useful resource for aspiring Actuaries who want to learn about machine learning. This course can help Actuaries build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated risk assessment models.
Market Researcher
Market Researchers are responsible for collecting and analyzing data on consumer behavior. They use their findings to make recommendations to businesses on how to market their products and services. The Aprendizaje Automático con Python course may be a useful resource for aspiring Market Researchers who want to learn about machine learning. This course can help Market Researchers build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated market research models.
Teacher
Teachers are responsible for educating students on a variety of subjects. They use a variety of techniques to teach students, including machine learning. The Aprendizaje Automático con Python course may be a useful resource for aspiring Teachers who want to learn about machine learning. This course can help Teachers build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated teaching methods.
Business Analyst
Business Analysts are responsible for analyzing business processes and identifying opportunities for improvement. They use a variety of techniques to analyze business processes, including machine learning. The Aprendizaje Automático con Python course may be a useful resource for aspiring Business Analysts who want to learn about machine learning. This course can help Business Analysts build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated business analysis models.
Operations Research Analyst
Operations Research Analysts are responsible for using mathematical and analytical techniques to solve business problems. They use a variety of techniques to solve problems, including machine learning. The Aprendizaje Automático con Python course may be a useful resource for aspiring Operations Research Analysts who want to learn about machine learning. This course can help Operations Research Analysts build a foundation in machine learning concepts and techniques, which can be used to develop more sophisticated operations research models.

Reading list

We've selected ten 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 Aprendizaje Automático con Python.
Comprehensive reference on deep learning. It covers advanced topics such as convolutional neural networks, recurrent neural networks, and generative models.
Provides a comprehensive overview of pattern recognition and machine learning. It covers topics such as supervised and unsupervised learning, model selection, and Bayesian inference.
Provides a comprehensive introduction to reinforcement learning. It covers topics such as Markov decision processes, dynamic programming, and value-based methods.
Provides a more theoretical and mathematical approach to machine learning. It covers probability theory, Bayesian inference, and graphical models.
Provides a theoretical foundation for machine learning. It covers topics such as statistical learning theory, support vector machines, and kernel methods.
Provides a theoretical and practical treatment of sparsity in machine learning. It covers topics such as compressed sensing, sparse regression, and Bayesian inference.
Focuses on practical applications of machine learning using popular Python libraries. It provides step-by-step instructions and examples for building and evaluating machine learning models.
Provides a comprehensive introduction to Bayesian reasoning and its applications in machine learning. It covers topics such as probability theory, Bayesian inference, and graphical models.
Provides a practical guide to machine learning for hackers. It covers topics such as data wrangling, model building, and deploying machine learning models.
Covers deep learning concepts and techniques using Python. It provides a thorough introduction to neural networks, convolutional neural networks, and recurrent neural networks.

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