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Romeo Kienzler
and
Nikolay Manchev
>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<
Read more
>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<
Read more
>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ. Once enrolled you can access the license in the Resources area <<<
This course, Advanced Machine Learning and Signal Processing, is part of the IBM Advanced Data Science Specialization which IBM is currently creating and gives you easy access to the invaluable insights into Supervised and Unsupervised Machine Learning Models used by experts in many field relevant disciplines. We’ll learn about the fundamentals of Linear Algebra to understand how machine learning modes work. Then we introduce the most popular Machine Learning Frameworks for python Scikit-Learn and SparkML. SparkML is making up the greatest portion of this course since scalability is key to address performance bottlenecks. We learn how to tune the models in parallel by evaluating hundreds of different parameter-combinations in parallel. We’ll continuously use a real-life example from IoT (Internet of Things), for exemplifying the different algorithms. For passing the course you are even required to create your own vibration sensor data using the accelerometer sensors in your smartphone. So you are actually working on a self-created, real dataset throughout the course.
If you choose to take this course and earn the Coursera course certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
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What's inside
Syllabus
Setting the stage
Supervised Machine Learning
Unsupervised Machine Learning
Read more
Syllabus
Setting the stage
Supervised Machine Learning
Unsupervised Machine Learning
Read more
Digital Signal Processing in Machine Learning
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
This course is perfect for those interested in data science and want to learn more about machine learning
Teaches data science fundamentals like linear algebra and popular frameworks like Scikit-Learn and SparkML, which are highly relevant in many fields
Provides practical, hands-on experience through real-world examples from the internet of things (IoT)
Offers an opportunity to earn an IBM digital badge, adding value to your professional credentials
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Reviews summary
Advanced machine learning and signal processing
Students say this course gives a very good overview of advanced machine learning and signal processing concepts while providing engaging and clear video content and hands-on practical examples. However, don't expect too much of a challenge, as the assignments are often described as too easy for an advanced level course.
Overall, instructors are seen as clear and knowledgeable.
"Thanks a lot to my motivated, sympathic and well prepared teachers"
"The instructors of this course are very good about explaining the intuition and the code."
"Both the instructors are quite good at explaining things and also provide a little insight as to why they're choosing to do something at any given moment."
Course assumes students have a foundational understanding and could benefit from more introductory content.
"This course is really spectacular."
"This course gave me an understanding implementation of signal processing with machine learning."
"This gives an introduction to handling IBM Watson and coding assignment."
"You get a clear cut on machine learning implementation through this course."
Students have mixed opinions on the depth of the course.
"It's a good summary of machine learning, but way too quick."
"Only skims the surface and I doubt anyone could come away from this course with a good understanding of the material."
"The treatment of the mathematical content was superficial; there was not enough time devoted to mathematical formulations of the problems and their solutions."
"Definitely worth the time, with good practical examples and a ton of maths behind Fourier Transform analysis and applying machine learning pipelines to Apache Spark."
Assignments often described as much too easy.
"The assignments are quite bad, and the course name ,advanced ML and signal processing, is misleading"
"Assignments are too easy, and not cover every lecture that I have learned for advanced ML, also some of the lectures are quite short."
"I honestly expected so much better from IBM."
"The idea of the course is great, it covers interesting topics about distributed machine learning, how to perform it with huge amount of data and how to solve scalability problems.The idea is great, but it's really poorly executed."
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 Advanced Machine Learning and Signal Processing with these
activities:
Review linear algebra concepts
Show steps
Strengthen the foundation for understanding ML algorithms.
Browse courses on
Linear Algebra
Show steps
-
Review notes or textbooks on linear algebra
-
Solve practice problems and exercises
-
Use online resources or tutorials for additional support
Read Foundations of Machine Learning
Show steps
Build a stronger foundational understanding of the theoretical underpinnings of the algorithms and models used in Machine Learning.
View
Foundations of Machine Learning, second edition
on Amazon
Show steps
-
Review summary of chapters 1 and 2
-
Read chapter 3 and take notes
-
Do the end of chapter exercises for chapter 3
Join a study group with peers
Show steps
Enhance understanding through collaboration and peer support.
Show steps
-
Find a group of classmates or online peers
-
Set regular meeting times and stick to them
-
Discuss course concepts, assignments, and projects
-
Help each other with understanding and problem-solving
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a glossary of ML terminology
Show steps
Enhance understanding of ML concepts through clear definitions.
Browse courses on
Machine Learning
Show steps
-
Review course materials and identify key terms
-
Search for definitions and explanations online
-
Organize the terms into a structured glossary
-
Share the glossary with peers for feedback
Solve coding challenges on LeetCode
Show steps
Reinforce understanding of ML algorithms through repetition and problem-solving.
Browse courses on
Coding
Show steps
-
Create a LeetCode account
-
Start with 'easy' level problems related to ML
-
Attempt a few problems daily
-
Review solutions and discuss with peers
Attend a workshop on Advanced Machine Learning
Show steps
Gain exposure to cutting-edge ML techniques and industry best practices.
Browse courses on
Machine Learning
Show steps
-
Research and identify relevant workshops
-
Register and attend the workshop
-
Take detailed notes and ask questions
-
Follow up with the workshop organizers or speakers
Build a machine learning model using SparkML
Show steps
Develop practical skills in applying ML models to large datasets.
Browse courses on
Machine Learning
Show steps
-
Choose a dataset and define the problem
-
Clean and preprocess the data
-
Select and train a SparkML model
-
Evaluate the model's performance
-
Deploy the model into production
Contribute to an open-source ML project
Show steps
Gain practical experience and learn from the ML community.
Browse courses on
Machine Learning
Show steps
-
Identify an open-source ML project to contribute to
-
Review the project's documentation and codebase
-
Find a suitable issue or feature to work on
-
Submit a pull request with your contribution
Review linear algebra concepts
Show steps
Strengthen the foundation for understanding ML algorithms.
Browse courses on
Linear Algebra
Show steps
Show steps
Show steps
- Review notes or textbooks on linear algebra
- Solve practice problems and exercises
- Use online resources or tutorials for additional support
Read Foundations of Machine Learning
Show steps
Build a stronger foundational understanding of the theoretical underpinnings of the algorithms and models used in Machine Learning.
View
Foundations of Machine Learning, second edition
on Amazon
Show steps
Show steps
Show steps
- Review summary of chapters 1 and 2
- Read chapter 3 and take notes
- Do the end of chapter exercises for chapter 3
Join a study group with peers
Show steps
Enhance understanding through collaboration and peer support.
Show steps
Show steps
Show steps
- Find a group of classmates or online peers
- Set regular meeting times and stick to them
- Discuss course concepts, assignments, and projects
- Help each other with understanding and problem-solving
Five other activities
Expand to see all activities and additional details
Show all eight activities
Compile a glossary of ML terminology
Show steps
Enhance understanding of ML concepts through clear definitions.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Review course materials and identify key terms
- Search for definitions and explanations online
- Organize the terms into a structured glossary
- Share the glossary with peers for feedback
Solve coding challenges on LeetCode
Show steps
Reinforce understanding of ML algorithms through repetition and problem-solving.
Browse courses on
Coding
Show steps
Show steps
Show steps
- Create a LeetCode account
- Start with 'easy' level problems related to ML
- Attempt a few problems daily
- Review solutions and discuss with peers
Attend a workshop on Advanced Machine Learning
Show steps
Gain exposure to cutting-edge ML techniques and industry best practices.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Research and identify relevant workshops
- Register and attend the workshop
- Take detailed notes and ask questions
- Follow up with the workshop organizers or speakers
Build a machine learning model using SparkML
Show steps
Develop practical skills in applying ML models to large datasets.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Choose a dataset and define the problem
- Clean and preprocess the data
- Select and train a SparkML model
- Evaluate the model's performance
- Deploy the model into production
Contribute to an open-source ML project
Show steps
Gain practical experience and learn from the ML community.
Browse courses on
Machine Learning
Show steps
Show steps
Show steps
- Identify an open-source ML project to contribute to
- Review the project's documentation and codebase
- Find a suitable issue or feature to work on
- Submit a pull request with your contribution
Career center
Learners who complete Advanced Machine Learning and Signal Processing will develop knowledge and skills
that may be useful to these careers:
Machine Learning Engineer
Machine Learning Engineer
Machine learning engineers design and implement machine learning systems. They work with data scientists to develop and deploy machine learning models. This course can help you develop the skills you need to be a successful machine learning engineer. You will learn about the fundamentals of machine learning, including supervised and unsupervised learning. You will also gain experience with popular machine learning frameworks, such as Scikit-Learn and SparkML.
Data Scientist
Data Scientist
Data scientists use data to solve business problems. They work with data engineers to collect and clean data, and then use machine learning and other techniques to analyze data and extract insights. This course can help you develop the skills you need to be a successful data scientist. You will learn about supervised and unsupervised machine learning models, as well as how to use linear algebra to understand how machine learning models work.
Operations Research Analyst
Operations Research Analyst
Operations research analysts use mathematical and statistical techniques to solve business problems. They work with businesses to improve efficiency and productivity. This course can help you develop the skills you need to be a successful operations research analyst. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to build and interpret mathematical and statistical models.
Statistician
Statistician
Statisticians apply mathematical and statistical techniques to collect, analyze, interpret, and present data. They work in a variety of industries, including finance, healthcare, and government. This course can help you develop the skills you need to be a successful statistician. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to build and interpret mathematical and statistical models.
Data Analyst
Data Analyst
Data analysts collect, clean, and analyze data to help businesses make informed decisions. They use statistical and machine learning techniques to identify trends and patterns in data. This course can help you develop the skills you need to be a successful data analyst. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to build and interpret mathematical and statistical models.
Quantitative Analyst
Quantitative Analyst
Quantitative analysts use mathematical and statistical techniques to analyze financial data. They develop and implement models to help investment managers make informed decisions. This course can help you develop the skills you need to be a successful quantitative analyst. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to build and interpret mathematical and statistical models.
Researcher
Researcher
Researchers conduct research to advance knowledge in a particular field. They work in a variety of settings, including universities, government agencies, and private companies. This course can help you develop the skills you need to be a successful researcher. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to develop and conduct research projects.
Business Analyst
Business Analyst
Business analysts use data to help businesses make informed decisions. They work with stakeholders to identify business needs and develop solutions. This course can help you develop the skills you need to be a successful business analyst. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to build and interpret mathematical and statistical models.
Project Manager
Project Manager
Project managers are responsible for the planning, execution, and completion of projects. They work with stakeholders to define project goals and objectives and develop project plans. This course can help you develop the skills you need to be a successful project manager. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to understand how machine learning can be used to improve project management.
Software Engineer
Software Engineer
Software engineers design, develop, and maintain software systems. They work with a variety of programming languages and technologies to create software that meets the needs of users. This course can help you develop the skills you need to be a successful software engineer. You will learn about the fundamentals of machine learning, including supervised and unsupervised learning. You will also gain experience with popular machine learning frameworks, such as Scikit-Learn and SparkML.
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 products to market. This course can help you develop the skills you need to be a successful product manager. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to understand how machine learning can be used to improve products.
Consultant
Consultant
Consultants provide advice and expertise to businesses. They work with businesses to identify problems and develop solutions. This course can help you develop the skills you need to be a successful consultant. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to understand how machine learning can be used to solve business problems.
Teacher
Teacher
Teachers educate students in a variety of subjects. They work in a variety of settings, including schools, colleges, and universities. This course can help you develop the skills you need to be a successful teacher. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to develop and deliver lesson plans that are effective and engaging.
Technical Writer
Technical Writer
Technical writers create documentation for a variety of products and services. They work with engineers, designers, and other stakeholders to develop documentation that is clear, concise, and accurate. This course can help you develop the skills you need to be a successful technical writer. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to understand how machine learning can be used to improve documentation.
Sales Engineer
Sales Engineer
Sales engineers work with customers to help them understand and purchase products and services. They work with engineers, designers, and other stakeholders to develop and deliver sales presentations. This course can help you develop the skills you need to be a successful sales engineer. You will learn about supervised and unsupervised machine learning models and gain a deep understanding of linear algebra. This knowledge will help you to understand how machine learning can be used to improve sales presentations.
For more career information including salaries, visit:
OpenCourser.com/course/cdvvdk/advanced
Reading list
We've selected 24 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
Advanced Machine Learning and Signal Processing.
Provides a probabilistic perspective on machine learning, covering both the theoretical foundations and practical applications. It valuable resource for researchers and practitioners who want to understand the probabilistic foundations of machine learning.
Provides a comprehensive introduction to machine learning for finance, covering the theory and algorithms behind a wide range of machine learning techniques. It valuable resource for anyone looking to gain a deeper understanding of the use of machine learning in finance.
Save
Provides a comprehensive introduction to pattern recognition and machine learning, covering the theory and algorithms behind a wide range of machine learning techniques. It valuable resource for anyone looking to gain a deeper understanding of the theoretical foundations of machine learning.
Provides a comprehensive overview of machine learning in action, covering both the theoretical foundations and practical applications. It valuable resource for practitioners who want to learn how to use machine learning for real-world applications.
Provides a comprehensive overview of machine learning with R, covering both the theoretical foundations and practical applications. It valuable resource for practitioners who want to learn how to use R for machine learning.
Provides a comprehensive overview of advanced machine learning techniques, covering both the theoretical foundations and practical applications. It valuable resource for researchers and practitioners who want to understand the advanced machine learning techniques used in various applications.
Provides a comprehensive overview of digital signal processing using MATLAB, covering both the theoretical foundations and practical applications. It valuable resource for researchers and practitioners who want to understand the digital signal processing techniques used in machine learning.
Provides a comprehensive overview of the mathematical foundations of machine learning, covering both the theoretical foundations and practical applications. It valuable resource for researchers and practitioners who want to understand the mathematical foundations of machine learning.
Save
This comprehensive textbook provides a thorough introduction to data mining concepts, techniques, and tools. It is particularly valuable for gaining a deeper understanding of the theory and algorithms behind machine learning models.
Provides a comprehensive introduction to deep learning with Python, covering the basics of neural networks, convolutional neural networks, and recurrent neural networks. It valuable resource for anyone looking to learn about the latest advances in deep learning.
Provides a practical introduction to machine learning with Python, covering the basics of supervised and unsupervised learning algorithms. It valuable resource for anyone looking to learn how to implement machine learning algorithms in Python.
Save
This classic textbook covers the fundamentals of deep learning, providing a comprehensive overview of the field's theory, architecture, and applications. It complements the course by expanding the understanding of neural networks and deep learning.
This comprehensive textbook provides a rigorous treatment of machine learning techniques, covering topics such as Bayesian inference, graphical models, and support vector machines. It valuable reference for understanding the theoretical underpinnings of machine learning algorithms.
Provides a comprehensive overview of machine learning for dummies, covering both the theoretical foundations and practical applications. It valuable resource for dummies who want to understand the basics of machine learning.
This widely-used textbook covers a broad range of statistical learning methods, including regression, classification, and unsupervised learning. It provides a solid foundation in the statistical principles underlying machine learning.
This practical book provides a hands-on introduction to machine learning using popular Python libraries like Scikit-Learn, Keras, and TensorFlow. It complements the course by offering practical experience with implementing machine learning algorithms.
Save
This companion book to 'Deep Learning' focuses on applying deep learning techniques to real-world problems. It provides practical guidance on building and training deep learning models for various tasks, such as image recognition and natural language processing.
This comprehensive book covers machine learning concepts and techniques using Python. It provides a solid foundation in machine learning algorithms and their implementations in Python.
This beginner-friendly book provides a practical introduction to deep learning using Python and the Keras library. It offers a gentle learning curve for those new to deep learning.
This practical book provides a hands-on introduction to TensorFlow, a popular framework for building and training deep learning models. It complements the course by offering practical experience with deep learning implementation.
This accessible book provides a non-technical introduction to machine learning concepts. It is particularly useful for those with little or no background in machine learning who want to gain a basic understanding of its principles.
This concise book provides a quick introduction to the key concepts of machine learning. It is useful for those who want a high-level overview of the field without getting into technical details.
Introduces the Python programming language in the context of data analysis. It provides a foundation for using Python for data manipulation and analysis, which is useful for implementing machine learning algorithms.
Covers the practical aspects of data science in a business context. It provides insights into how machine learning and data analysis can be applied to solve real-world business problems.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/cdvvdk/advanced
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Level
Advanced
Via
Coursera
Institution
IBM
Instructors
Romeo Kienzler
Nikolay Manchev
Language
English
Transcripts
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آذربایجان دیلی
This course belongs to a
collection
called
Advanced Data Science with IBM. It's comprised of four courses:
- Fundamentals of Scalable Data Science
- Advanced Data Science Capstone
- Advanced Machine Learning and Signal Processing (viewing)
- Applied AI with DeepLearning
Good to know
This course is perfect for those interested in data science and want to learn more about machine learning
Teaches data science fundamentals like linear algebra and popular frameworks like Scikit-Learn and SparkML, which are highly relevant in many fields
Provides practical, hands-on experience through real-world examples from the internet of things (IoT)
Offers an opportunity to earn an IBM digital badge, adding value to your professional credentials
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