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Advanced Machine Learning and Signal Processing

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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

Digital Signal Processing in Machine Learning

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

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Strengthen the foundation for understanding ML algorithms.

Browse courses on Linear Algebra

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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

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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

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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

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Enhance understanding through collaboration and peer support.

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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

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Enhance understanding of ML concepts through clear definitions.

Browse courses on Machine Learning

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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

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Reinforce understanding of ML algorithms through repetition and problem-solving.

Browse courses on Coding

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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

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Gain exposure to cutting-edge ML techniques and industry best practices.

Browse courses on Machine Learning

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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

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Develop practical skills in applying ML models to large datasets.

Browse courses on Machine Learning

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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

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Gain practical experience and learn from the ML community.

Browse courses on Machine Learning

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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 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.

See salaries and explore the career path for Machine Learning Engineer

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.

See salaries and explore the career path for Data Scientist

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.

See salaries and explore the career path for Operations Research Analyst

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.

See salaries and explore the career path for Statistician

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.

See salaries and explore the career path for Data 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.

See salaries and explore the career path for Quantitative Analyst

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.

See salaries and explore the career path for Researcher

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.

See salaries and explore the career path for Business Analyst

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.

See salaries and explore the career path for Project Manager

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.

See salaries and explore the career path for Software Engineer

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.

See salaries and explore the career path for Product Manager

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.

See salaries and explore the career path for Consultant

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.

See salaries and explore the career path for Teacher

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.

See salaries and explore the career path for Technical Writer

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.

See salaries and explore the career path for Sales Engineer

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.

Probabilistic Machine Learning

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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.

Probabilistic Machine Learning: Advanced Topics...

Hardcover

$$$$

Causal Factor Investing

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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.

Advanced Machine Learning and Signal Processing

What's inside

Syllabus

Good to know

Save this course

Reviews summary

Advanced machine learning and signal processing

Activities

Career center

Reading list

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