Advanced Machine Learning and Signal Processing

开始时间: 04/04/2020 持续时间: Unknown

所在平台: Coursera

课程类别: 其他类别

大学或机构: CourseraNew



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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

高级机器学习和信号处理:&gt;&gt;通过注册此课程,您同意FAQ中列出的最终用户许可协议。注册后,您可以在“资源”区域中&lt;&lt;&lt; 本课程(高级机器学习和信号处理)是IBM当前正在创建的IBM Advanced Data Science Specialization的一部分,使您可以轻松地访问许多领域相关学科的专家对监督和非监督机器学习模型的宝贵见解。我们将学习线性代数的基础知识,以了解机器学习模式的工作原理。然后,我们介绍针对python Scikit-Learn和SparkML的最受欢迎的机器学习框架。由于可伸缩性是解决性能瓶颈的关键,因此SparkML构成了本课程的最大部分。我们学习如何通过并行评估数百种不同的参数组合来并行调整模型。我们将继续使用IoT(物联网)中的真实示例,以举例说明不同的算法。为了通过课程,您甚至需要使用智能手机中的加速度传感器创建自己的振动传感器数据。因此,您实际上在整个课程中都在处理一个自行创建的真实数据集。 如果您选择参加本课程并获得Coursera课程证书,那么您还将获得IBM数字徽章。要查找有关IBM数字徽章的更多信息,请访问链接。





>>> By enrolling in this course you agree to the End User License Agreement as set out in the FAQ.