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Daniel Romaniuk
In this one hour long project-based course, you will tackle a real-world prediction problem using machine learning. The dataset we are going to use comes from the Australian government. They recorded daily weather observations from a number of Australian weather stations. We will use this data to train an artificial neural network to predict whether it will rain tomorrow.
Read more
In this one hour long project-based course, you will tackle a real-world prediction problem using machine learning. The dataset we are going to use comes from the Australian government. They recorded daily weather observations from a number of Australian weather stations. We will use this data to train an artificial neural network to predict whether it will rain tomorrow.
Read more
In this one hour long project-based course, you will tackle a real-world prediction problem using machine learning. The dataset we are going to use comes from the Australian government. They recorded daily weather observations from a number of Australian weather stations. We will use this data to train an artificial neural network to predict whether it will rain tomorrow.
By the end of this project, you will have created a machine learning model using industry standard tools, including Python and sklearn.
Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Register for this course and see more details by visiting:
OpenCourser.com/course/zfhvtd/predicting
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What's inside
Syllabus
Project Overview
In this one hour long project-based course, you will tackle a real-world prediction problem using machine learning. The dataset we are going to use comes from the Australian government. They recorded daily weather observations from a number of Australian weather stations. We will use this data to train an artificial neural network to predict whether it will rain tomorrow.
By the end of this project, you will have created a machine learning model using industry standard tools, including Python and sklearn.
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Covers data analysis and prediction, which are highly relevant in many industries
Introduces industry-standard tools, such as Python and sklearn, which can enhance career prospects
Requires no prior knowledge of data analysis or programming, making it suitable for beginners
Taught by an experienced instructor, Daniel Romaniuk, who specializes in machine learning
Mainly focuses on a single project, which may not provide a comprehensive understanding of machine learning
Does not cover advanced machine learning algorithms or techniques
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Save Predicting the Weather with Artificial Neural Networks to your list so you can find it easily later:
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Reviews summary
Predictive weather with ann
According to students, the Predicting the Weather with Artificial Neural Networks course is decent but may not suit everyone. It is criticized by many students for its lack of usefulness and poor explanations. Students do say that they appreciate how well-balanced the course is.
Structuredness of course is appreciated.
"A well-balanced project. kudos!"
Avoid if you are new to subject.
"This guided project does not teach anything new, apart from what can be seen in free platforms like kaggle or youtube."
"It was extremely short for a $10 project and there was too little explanation on any concept or method."
"I completely understand everything in the project, but I learnt nothing new from it, and I don't think if I would learn anything at all if I did not already know all of the concepts and methods in the project."
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 Predicting the Weather with Artificial Neural Networks with these
activities:
Seek Guidance from Experienced Machine Learning Practitioners
Show steps
Connecting with experienced professionals can provide valuable insights, support, and guidance throughout your learning journey.
Show steps
-
Attend industry events and conferences to meet potential mentors.
-
Reach out to professionals in your network who have expertise in machine learning.
-
Request informational interviews to learn from their experiences and perspectives.
Review 'Python Machine Learning' by Sebastian Raschka
Show steps
This book provides a comprehensive introduction to machine learning with Python, familiarizing you with the core principles and techniques necessary for this course.
Show steps
-
Read the book cover-to-cover, taking notes on key concepts and techniques.
-
Complete the practice exercises provided in the book to reinforce your understanding.
Consolidate Course Materials
Show steps
Organizing your notes, assignments, and study materials will improve your ability to review and retain the knowledge gained from the course.
Show steps
-
Create a dedicated notebook or digital folder for course materials.
-
Regularly add notes, lecture slides, assignments, and other relevant resources to the repository.
-
Review the compiled materials periodically to enhance retention and recall.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a Study Group
Show steps
Collaborating with peers can enhance your learning experience, providing opportunities to exchange ideas, clarify concepts, and work through problems.
Show steps
-
Find classmates or fellow learners who are also enrolled in the course.
-
Organize regular study sessions to discuss course material, solve problems, and prepare for assessments.
Explore Hands-on Machine Learning Projects with Python from Coursera
Show steps
This guided tutorial series provides practical experience with machine learning projects, complementing the theoretical concepts covered in the course.
Show steps
-
Follow the video tutorials and complete the hands-on projects.
-
Refer to the tutorial materials when working on course assignments.
Solve Practice Problems on Kaggle
Show steps
Kaggle offers numerous practice problems and datasets that align with the topics in this course, allowing you to test and strengthen your understanding.
Browse courses on
Machine Learning Algorithms
Show steps
-
Select practice problems relevant to the course material.
-
Attempt to solve the problems on your own, referring to course notes when needed.
-
Compare your solutions to sample answers or discuss them in forums.
Develop a Machine Learning Model for a Real-World Problem
Show steps
Creating your own machine learning model will solidify your understanding of the concepts and techniques covered in the course, as well as their practical applications.
Browse courses on
Data Modeling
Show steps
-
Identify a real-world problem that can be solved using machine learning.
-
Gather and prepare a suitable dataset.
-
Choose and train a machine learning model.
-
Evaluate the model's performance.
-
Deploy and monitor the model.
Seek Guidance from Experienced Machine Learning Practitioners
Show steps
Connecting with experienced professionals can provide valuable insights, support, and guidance throughout your learning journey.
Show steps
Show steps
Show steps
- Attend industry events and conferences to meet potential mentors.
- Reach out to professionals in your network who have expertise in machine learning.
- Request informational interviews to learn from their experiences and perspectives.
Review 'Python Machine Learning' by Sebastian Raschka
Show steps
This book provides a comprehensive introduction to machine learning with Python, familiarizing you with the core principles and techniques necessary for this course.
Show steps
Show steps
Show steps
- Read the book cover-to-cover, taking notes on key concepts and techniques.
- Complete the practice exercises provided in the book to reinforce your understanding.
Consolidate Course Materials
Show steps
Organizing your notes, assignments, and study materials will improve your ability to review and retain the knowledge gained from the course.
Show steps
Show steps
Show steps
- Create a dedicated notebook or digital folder for course materials.
- Regularly add notes, lecture slides, assignments, and other relevant resources to the repository.
- Review the compiled materials periodically to enhance retention and recall.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Join a Study Group
Show steps
Collaborating with peers can enhance your learning experience, providing opportunities to exchange ideas, clarify concepts, and work through problems.
Show steps
Show steps
Show steps
- Find classmates or fellow learners who are also enrolled in the course.
- Organize regular study sessions to discuss course material, solve problems, and prepare for assessments.
Explore Hands-on Machine Learning Projects with Python from Coursera
Show steps
This guided tutorial series provides practical experience with machine learning projects, complementing the theoretical concepts covered in the course.
Show steps
Show steps
Show steps
- Follow the video tutorials and complete the hands-on projects.
- Refer to the tutorial materials when working on course assignments.
Solve Practice Problems on Kaggle
Show steps
Kaggle offers numerous practice problems and datasets that align with the topics in this course, allowing you to test and strengthen your understanding.
Browse courses on
Machine Learning Algorithms
Show steps
Show steps
Show steps
- Select practice problems relevant to the course material.
- Attempt to solve the problems on your own, referring to course notes when needed.
- Compare your solutions to sample answers or discuss them in forums.
Develop a Machine Learning Model for a Real-World Problem
Show steps
Creating your own machine learning model will solidify your understanding of the concepts and techniques covered in the course, as well as their practical applications.
Browse courses on
Data Modeling
Show steps
Show steps
Show steps
- Identify a real-world problem that can be solved using machine learning.
- Gather and prepare a suitable dataset.
- Choose and train a machine learning model.
- Evaluate the model's performance.
- Deploy and monitor the model.
Career center
Learners who complete Predicting the Weather with Artificial Neural Networks will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts apply their knowledge of data analysis and statistical modeling to solve real-world problems. They use data to find trends, patterns, and insights that can help businesses make better decisions. This course can help aspiring Data Analysts build a foundation in machine learning, which is an essential skill for this role. The course will teach students how to use Python and sklearn, which are industry-standard tools for data analysis and machine learning.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers design, develop, and deploy machine learning models. They work with data scientists to identify the business problem that needs to be solved, and then they design and implement the machine learning model that will solve the problem. This course can help aspiring Machine Learning Engineers build a foundation in machine learning, which is an essential skill for this role. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Data Scientist
Data Scientist
Data Scientists use their knowledge of data analysis, statistics, and machine learning to solve real-world problems. They use data to find trends, patterns, and insights that can help businesses make better decisions. This course can help aspiring Data Scientists build a foundation in machine learning, which is an essential skill for this role. The course will teach students how to use Python and sklearn, which are industry-standard tools for data analysis and machine learning.
Software Engineer
Software Engineer
Software Engineers design, develop, and test software applications. They work with customers to understand their needs, and then they design and implement the software that will meet those needs. This course can help aspiring Software Engineers build a foundation in machine learning, which is becoming increasingly important in software development. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Quantitative Analyst
Quantitative Analyst
Quantitative Analysts use their knowledge of mathematics, statistics, and computer science to solve problems in the financial industry. They use data to find trends, patterns, and insights that can help financial institutions make better decisions. This course can help aspiring Quantitative Analysts build a foundation in machine learning, which is an essential skill for this role. The course will teach students how to use Python and sklearn, which are industry-standard tools for data analysis and machine learning.
Statistician
Statistician
Statisticians use their knowledge of mathematics, statistics, and computer science to collect, analyze, and interpret data. They work with clients to understand their needs, and then they design and implement the statistical analysis that will meet those needs. This course can help aspiring Statisticians build a foundation in machine learning, which is becoming increasingly important in statistics. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Research Scientist
Research Scientist
Research Scientists conduct research in a variety of fields, including natural sciences, social sciences, and engineering. They use their knowledge of science and math to develop new theories and technologies. This course can help aspiring Research Scientists build a foundation in machine learning, which is becoming increasingly important in research. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Data Engineer
Data Engineer
Data Engineers design, build, and maintain the infrastructure that stores and processes data. They work with data scientists and other data professionals to ensure that data is available and accessible for analysis. This course can help aspiring Data Engineers build a foundation in machine learning, which is becoming increasingly important in data engineering. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Business Analyst
Business Analyst
Business Analysts use their knowledge of business and technology to solve problems and improve efficiency. They work with stakeholders to understand their needs, and then they design and implement solutions that will meet those needs. This course can help aspiring Business Analysts build a foundation in machine learning, which is becoming increasingly important in business analysis. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Product Manager
Product Manager
Product Managers are responsible for the development and launch of new products. They work with engineers, designers, and marketers to bring new products to market. This course can help aspiring Product Managers build a foundation in machine learning, which is becoming increasingly important in product management. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Marketing Analyst
Marketing Analyst
Marketing Analysts use their knowledge of marketing and data analysis to understand customer behavior and improve marketing campaigns. They work with marketing managers to develop and implement marketing campaigns that will reach the target audience and achieve the desired results. This course can help aspiring Marketing Analysts build a foundation in machine learning, which is becoming increasingly important in marketing analysis. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Financial Analyst
Financial Analyst
Financial Analysts use their knowledge of finance and data analysis to evaluate investment opportunities and make recommendations to clients. They work with clients to understand their financial goals, and then they analyze financial data to identify potential investment opportunities. This course can help aspiring Financial Analysts build a foundation in machine learning, which is becoming increasingly important in financial analysis. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Risk Analyst
Risk Analyst
Risk Analysts use their knowledge of risk management and data analysis to identify and assess risks. They work with businesses to understand their risk tolerance, and then they develop and implement risk management strategies. This course can help aspiring Risk Analysts build a foundation in machine learning, which is becoming increasingly important in risk analysis. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Operations Analyst
Operations Analyst
Operations Analysts use their knowledge of operations management and data analysis to improve the efficiency of operations. They work with operations managers to understand their goals, and then they analyze data to identify areas for improvement. This course can help aspiring Operations Analysts build a foundation in machine learning, which is becoming increasingly important in operations analysis. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
Data Architect
Data Architect
Data Architects design and build the systems that store and process data. They work with data engineers and other data professionals to ensure that data is available and accessible for analysis. This course can help aspiring Data Architects build a foundation in machine learning, which is becoming increasingly important in data architecture. The course will teach students how to use Python and sklearn, which are industry-standard tools for machine learning.
For more career information including salaries, visit:
OpenCourser.com/course/zfhvtd/predicting
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
Predicting the Weather with Artificial Neural Networks.
Save
A comprehensive textbook that covers the foundations of deep learning and its applications in various fields. It provides a detailed overview of deep learning architectures, algorithms, and techniques.
A practical guide to machine learning that uses Python and popular libraries such as Scikit-Learn, Keras, and TensorFlow. It provides hands-on examples and exercises to help you learn the fundamentals of machine learning.
A practical guide to machine learning that focuses on real-world applications. It provides step-by-step instructions on how to build and deploy machine learning models using popular tools and libraries.
A concise overview of machine learning that covers the basic concepts and algorithms in a clear and accessible way. It provides a good foundation for further study in machine learning.
A theoretical treatment of machine learning that provides a deep understanding of the underlying principles and algorithms. It covers topics such as supervised learning, unsupervised learning, and reinforcement learning.
Save
A textbook that provides a comprehensive overview of machine learning algorithms and their applications. It covers a wide range of topics, including supervised learning, unsupervised learning, and reinforcement learning.
A practical guide to predictive analytics that focuses on real-world applications. It provides step-by-step instructions on how to build and deploy predictive models using popular tools and libraries.
A beginner-friendly guide to machine learning that explains the basic concepts and algorithms in a clear and accessible way. It provides real-world examples and exercises to help you understand how machine learning is used in practice.
A beginner-friendly guide to data science that explains the basic concepts and algorithms in a clear and accessible way. It provides real-world examples and exercises to help you understand how data science is used in practice.
A very beginner-friendly guide to machine learning that assumes no prior knowledge of the subject. It provides a gentle introduction to the basic concepts and algorithms of machine learning.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/zfhvtd/predicting
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Effort
1 hour
Level
Intermediate
Via
Coursera
Institution
Coursera Project Network
Instructor
Daniel Romaniuk
Language
English
Good to know
Covers data analysis and prediction, which are highly relevant in many industries
Introduces industry-standard tools, such as Python and sklearn, which can enhance career prospects
Requires no prior knowledge of data analysis or programming, making it suitable for beginners
Taught by an experienced instructor, Daniel Romaniuk, who specializes in machine learning
Mainly focuses on a single project, which may not provide a comprehensive understanding of machine learning
Does not cover advanced machine learning algorithms or techniques
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