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- Level Expert
- المدة 28 ساعات hours
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>>> 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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Unlock advanced insights in machine learning and signal processing with this expert-level course, part of the IBM Advanced Data Science Specialization. Led by IBM professionals, it covers key topics like Linear Algebra, Scikit-Learn, and SparkML, emphasizing scalability and practical application using IoT examples. Over 1680 minutes, learners will engage in hands-on projects, including creating and analyzing their own sensor data. Ideal for data science enthusiasts looking to deepen their expertise, the course offers a certificate and IBM digital badge upon completion. Available through Coursera with a Starter subscription.