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Leire Ahedo
Este proyecto es un curso práctico y efectivo para aprender Machine Learning con Python. Aprenderás todos los pasos de desarrollo de un modelo y a evaluar su desempeño. Al finalizar este curso, habrás generado un proyecto completo de Machine Learning desde cero.
Register for this course and see more details by visiting:
OpenCourser.com/course/3q14zz/machine
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Focuses on using Machine Learning with Python, a crucial skillset for data scientists and programmers
Provides hands-on practice in developing and evaluating Machine Learning models
Guides learners through the entire process of building a Machine Learning project from scratch
Taught by industry experts who have extensive experience in Machine Learning
Suitable for individuals with a basic understanding of programming and data analysis
Save this course
Save Machine Learning con Python. Nivel intermedio to your list so you can find it easily later:
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Reviews summary
Superficial review course
The instructor introduces the different stages of machine learning modeling. Unfortunately, the information outside the modeling process is superficial in nature. The functions used are not explained well. The course can serve as a refresher for those with strong machine learning knowledge. This course is not for beginners.
This course is good for a refresher.
"... sirve para repasar siempre que se tenga un conocimiento relativamente avanzado de modulaciones y regresiones, pero un repaso muy ligero."
The functions are not explained well.
"... No se explican casi nada las funciones ejecutadas."
The information is too superficial.
"... La información que se da fuera de eso es demasiado superficial."
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 Machine Learning con Python. Nivel intermedio with these
activities:
Review Python programming
Show steps
熟悉 Python 程式語言，因為它是在這門課程中實作機器學習演算法的主要語言。
Browse courses on
Python
Show steps
-
Review the basics of Python syntax.
-
Practice writing simple Python programs.
-
Review Python libraries for machine learning.
Attend study group sessions
Show steps
Participate in study group sessions to discuss course concepts and collaborate on projects.
Browse courses on
Machine Learning
Show steps
-
Find or create a study group.
-
Meet regularly to discuss course topics.
-
Work together on projects.
Follow online tutorials on Machine Learning
Show steps
Go through online tutorials to supplement course materials and enhance understanding of Machine Learning concepts.
Browse courses on
Machine Learning
Show steps
-
Identify reputable online tutorials.
-
Follow the tutorials step by step.
-
Implement the concepts in your own projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Machine Learning by Tom Mitchell
Show steps
Review the book Machine Learning to solidify the understanding of the concepts of Machine Learning.
View
Machine Learning (McGraw-Hill International...
on Amazon
Show steps
-
Identify key concepts in each chapter.
-
Summarize the key concepts in your own words.
-
Create flashcards for the key concepts.
Solve LeetCode problems on Machine Learning
Show steps
Practice solving LeetCode problems to enhance coding skills and apply the concepts of Machine Learning.
Show steps
-
Identify LeetCode problems related to Machine Learning.
-
Solve the problems using Python or a preferred programming language.
-
Review solutions and compare approaches with others.
Start a Machine Learning project
Show steps
Develop a Machine Learning project to apply the course concepts in a practical and meaningful way.
Browse courses on
Machine Learning
Show steps
-
Brainstorm ideas for a project.
-
Gather data.
-
Build a machine learning model.
-
Evaluate the model.
-
Deploy the model.
Create a data visualization project
Show steps
Create a data visualization project to present Machine Learning results in a visually appealing and informative manner.
Browse courses on
Data Visualization
Show steps
-
Identify a dataset to work with.
-
Clean and prepare the data.
-
Choose appropriate visualization techniques.
-
Create the data visualization.
-
Present your findings.
Write or record a blog post about Machine Learning
Show steps
Contribute to the online learning community by sharing your understanding of Machine Learning concepts in a blog post.
Show steps
-
Choose a topic to write about.
-
Research the topic.
-
Write or record your blog post.
-
Publish or share your blog post.
Review Python programming
Show steps
熟悉 Python 程式語言，因為它是在這門課程中實作機器學習演算法的主要語言。
Browse courses on
Python
Show steps
Show steps
Show steps
- Review the basics of Python syntax.
- Practice writing simple Python programs.
- Review Python libraries for machine learning.
Attend study group sessions
Show steps
Participate in study group sessions to discuss course concepts and collaborate on projects.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Find or create a study group.
- Meet regularly to discuss course topics.
- Work together on projects.
Follow online tutorials on Machine Learning
Show steps
Go through online tutorials to supplement course materials and enhance understanding of Machine Learning concepts.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify reputable online tutorials.
- Follow the tutorials step by step.
- Implement the concepts in your own projects.
Five other activities
Expand to see all activities and additional details
Show all eight activities
Review Machine Learning by Tom Mitchell
Show steps
Review the book Machine Learning to solidify the understanding of the concepts of Machine Learning.
View
Machine Learning (McGraw-Hill International...
on Amazon
Show steps
Show steps
Show steps
- Identify key concepts in each chapter.
- Summarize the key concepts in your own words.
- Create flashcards for the key concepts.
Solve LeetCode problems on Machine Learning
Show steps
Practice solving LeetCode problems to enhance coding skills and apply the concepts of Machine Learning.
Show steps
Show steps
Show steps
- Identify LeetCode problems related to Machine Learning.
- Solve the problems using Python or a preferred programming language.
- Review solutions and compare approaches with others.
Start a Machine Learning project
Show steps
Develop a Machine Learning project to apply the course concepts in a practical and meaningful way.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Brainstorm ideas for a project.
- Gather data.
- Build a machine learning model.
- Evaluate the model.
- Deploy the model.
Create a data visualization project
Show steps
Create a data visualization project to present Machine Learning results in a visually appealing and informative manner.
Browse courses on
Data Visualization
Show steps
Show steps
Show steps
- Identify a dataset to work with.
- Clean and prepare the data.
- Choose appropriate visualization techniques.
- Create the data visualization.
- Present your findings.
Write or record a blog post about Machine Learning
Show steps
Contribute to the online learning community by sharing your understanding of Machine Learning concepts in a blog post.
Show steps
Show steps
Show steps
- Choose a topic to write about.
- Research the topic.
- Write or record your blog post.
- Publish or share your blog post.
Career center
Learners who complete Machine Learning con Python. Nivel intermedio will develop knowledge and skills
that may be useful to these careers:
Data Scientist
Data Scientist
As a Data Scientist, you would use data to solve business problems and make predictions. This course would provide you with a solid foundation in machine learning, one of the most important tools for data scientists. You will learn how to clean and prepare data, build and evaluate models, and communicate your findings to stakeholders.
Machine Learning Engineer
Machine Learning Engineer
As a Machine Learning Engineer, you would design, develop, and maintain machine learning models that solve real-world problems. This course, with its hands-on approach, would give you the skills you need to succeed in this role. You will learn how to work with real-world data, build and evaluate models, and deploy them into production.
Quantitative Analyst
Quantitative Analyst
As a Quantitative Analyst, you would use mathematical and statistical models to analyze financial data and make investment decisions. This course would provide you with a strong foundation in machine learning, which is increasingly being used in quantitative finance. You will learn how to build and evaluate models, and apply them to real-world financial problems.
Software Engineer
Software Engineer
As a Software Engineer, you would design, develop, and maintain software systems. This course would help you build a foundation in machine learning, which is increasingly being used to improve software systems. You will learn how to apply machine learning to tasks such as natural language processing, image recognition, and anomaly detection.
Business Analyst
Business Analyst
As a Business Analyst, you would use data to solve business problems. This course would help you build a foundation in machine learning, which is increasingly being used to automate tasks and improve decision-making. You will learn how to identify opportunities for machine learning, and how to build and evaluate models.
Operations Manager
Operations Manager
As an Operations Manager, you would be responsible for the day-to-day operations of a business. This course would help you understand how to use machine learning to automate tasks and improve efficiency. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Risk Manager
Risk Manager
As a Risk Manager, you would be responsible for identifying and managing risks. This course would help you understand how to use machine learning to automate tasks and improve risk management. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Customer Success Manager
Customer Success Manager
As a Customer Success Manager, you would be responsible for ensuring the success of customers. This course would help you understand how to use machine learning to automate tasks and improve customer support. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Compliance Officer
Compliance Officer
As a Compliance Officer, you would be responsible for ensuring that a company complies with all applicable laws and regulations. This course would help you understand how to use machine learning to automate tasks and improve compliance. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Marketing Manager
Marketing Manager
As a Marketing Manager, you would be responsible for developing and executing marketing campaigns. This course would help you understand how to use machine learning to automate tasks and improve targeting. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Cybersecurity Analyst
Cybersecurity Analyst
As a Cybersecurity Analyst, you would be responsible for protecting a company's computer systems from cyberattacks. This course would help you understand how to use machine learning to automate tasks and improve cybersecurity. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Project Manager
Project Manager
As a Project Manager, you would be responsible for planning and executing projects. This course would help you understand how to use machine learning to automate tasks and improve project management. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Sales Manager
Sales Manager
As a Sales Manager, you would be responsible for leading and motivating a sales team. This course would help you understand how to use machine learning to automate tasks and improve lead generation. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Financial Analyst
Financial Analyst
As a Financial Analyst, you would be responsible for analyzing financial data and making investment recommendations. This course would help you understand how to use machine learning to automate tasks and improve financial analysis. You will learn how to identify opportunities for machine learning, and how to work with data scientists to develop and implement machine learning solutions.
Product Manager
Product Manager
As a Product Manager, you would be responsible for the development and launch of new products. This course would help you understand the potential of machine learning, and how to use it to improve products. You will learn how to identify opportunities for machine learning, and how to work with engineers to develop and implement machine learning solutions.
For more career information including salaries, visit:
OpenCourser.com/course/3q14zz/machine
Reading list
We've selected 13 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
Machine Learning con Python. Nivel intermedio.
Provides a comprehensive introduction to machine learning and pattern recognition.
It provides a comprehensive overview of machine learning techniques and their implementation in Python.
Provides hands-on examples and covers core concepts and techniques used in machine learning.
Provides a comprehensive overview of deep learning and its implementation in Python.
Save
It provides a theoretical and mathematical foundation for deep learning, which key area in machine learning.
It adds more breadth to the course. It provides practical recipes for implementing key machine learning algorithms in Python.
It provides a probabilistic perspective on machine learning, which can provide additional insights into the topic.
Save
Provides a practical introduction to machine learning using the R programming language.
Provides a solid mathematical foundation for machine learning concepts.
Provides practical examples and case studies of machine learning applications.
Provides a comprehensive overview of computer vision techniques and their implementation in Python.
Provides a comprehensive overview of natural language processing techniques and their implementation in Python.
Provides an accessible introduction to machine learning concepts and techniques.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/3q14zz/machine
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Time
2 hours
Via
Coursera
Institution
Coursera Project Network
Instructor
Leire Ahedo
Good to know
Focuses on using Machine Learning with Python, a crucial skillset for data scientists and programmers
Provides hands-on practice in developing and evaluating Machine Learning models
Guides learners through the entire process of building a Machine Learning project from scratch
Taught by industry experts who have extensive experience in Machine Learning
Suitable for individuals with a basic understanding of programming and data analysis
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