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IBM 高級數據科學專項課程

Advanced Data Science with IBM

Expert in Data Science, Machine Learning and AI. Become an IBM-approved Expert in Data Science, Machine Learning and Artificial Intelligence.

IBM

Coursera

計算機

難(高級)

2 個月

  • 英語, 法語
  • 448

課程概況

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You’ll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability.

If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.

包含課程

課程1
Fundamentals of Scalable Data Science

The value of IoT can be found within the analysis of data gathered from the system under observation, where insights gained can have direct impact on business and operational transformation. Through analysis data correlation, patterns, trends, and other insight are discovered. Insight leads to better communication between stakeholders, or actionable insights, which can be used to raise alerts or send commands, back to IoT devices. With a focus on the topic of Exploratory Data Analysis, the course provides an in-depth look at mathematical foundations of basic statistical measures, and how they can be used in conjunction with advanced charting libraries to make use of the world’s best pattern recognition system – the human brain. Learn how to work with the data, and depict it in ways that support visual inspections, and derive to inferences about the data. Identify interesting characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. The goal is that you are able to implement end-to-end analytic workflows at scale, from data acquisition to actionable insights. Through a series of lectures and exercises students get the needed skills to perform such analysis on any data, although we clearly focus on IoT Sensor Event Data. 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. After completing this course, you will be able to: ? Describe how basic statistical measures, are used to reveal patterns within the data ? Recognize data characteristics, patterns, trends, deviations or inconsistencies, and potential outliers. ? Identify useful techniques for working with big data such as dimension reduction and feature selection methods ? Use advanced tools and charting libraries to: o Automatically store data from IoT device(s) o improve efficiency of analysis of big-data with partitioning and parallel analysis o Visualize the data in an number of 2D and 3D formats (Box Plot, Run Chart, Scatter Plot, Pareto Chart, and Multidimensional Scaling) For successful completion of the course, the following prerequisites are recommended: ? Basic programming skills in any programming language (python preferred) ? A good grasp of basic algebra and algebraic equations ? (optional) “A developer's guide to the Internet of Things (IoT)” - a Coursera course ? Basic SQL is a plus In order to complete this course, the following technologies will be used: (These technologies are introduced in the course as necessary so no previous knowledge is required.) ? IBM Watson IoT Platform (MQTT Message Broker as a Service, Device Management and Operational Rule Engine) ? IBM Bluemix (Open Standard Platform Cloud) ? Node-Red ? Cloudant NoSQL (Apache CouchDB) ? ApacheSpark ? Languages: R, Scala and Python (focus on Python) This course takes four weeks, 4-6h per week

課程2
Advanced Machine Learning and Signal Processing

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.

課程3
Applied AI with DeepLearning

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, Applied Artificial Intelligence with DeepLearning, is part of the IBM Advanced Data Science Certificate which IBM is currently creating and gives you easy access to the invaluable insights into Deep Learning models used by experts in Natural Language Processing, Computer Vision, Time Series Analysis, and many other disciplines. We’ll learn about the fundamentals of Linear Algebra and Neural Networks. Then we introduce the most popular DeepLearning Frameworks like Keras, TensorFlow, PyTorch, DeepLearning4J and Apache SystemML. Keras and TensorFlow are making up the greatest portion of this course. We learn about Anomaly Detection, Time Series Forecasting, Image Recognition and Natural Language Processing by building up models using Keras one real-life examples from IoT (Internet of Things), Financial Marked Data, Literature or Image Databases. Finally, we learn how to scale those artificial brains using Kubernetes, Apache Spark and GPUs. IMPORTANT: THIS COURSE ALONE IS NOT SUFFICIENT TO OBTAIN THE "IBM Watson IoT Certified Data Scientist certificate". You need to take three other courses where two of them are currently built. The Specialization will be ready late spring, early summer 2018 Using these approaches, no matter what your skill levels in topics you would like to master, you can change your thinking and change your life. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. If you’re struggling, you’ll see a structured treasure trove of practical techniques that walk you through what you need to do to get on track. If you’ve ever wanted to become better at anything, this course will help serve as your guide. Prerequisites: Some coding skills are necessary. Preferably python, but any other programming language will do fine. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. 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.

課程4
Advanced Data Science Capstone

This project completer has proven a deep understanding on massive parallel data processing, data exploration and visualization, advanced machine learning and deep learning and how to apply his knowledge in a real-world practical use case where he justifies architectural decisions, proves understanding the characteristics of different algorithms, frameworks and technologies and how they impact model performance and scalability.

常見問題

退款政策是如何規定的?

如果訂閱,您可以獲得 7 天免費試聽,在此期間,您可以取消課程,無需支付任何罰金。在此之后,我們不會退款,但您可以隨時取消訂閱。請閱讀我們完整的退款政策。

我可以只注冊一門課程嗎?

可以!點擊您感興趣的課程卡開始注冊即可。注冊并完成課程后,您可以獲得可共享的證書,或者您也可以旁聽該課程免費查看課程資料。如果您訂閱的課程是某專項課程的一部分,系統會自動為您訂閱完整的專項課程。訪問您的學生面板,跟蹤您的進度。

有助學金嗎?

是的,Coursera 可以為無法承擔費用的學生提供助學金。通過點擊左側“注冊”按鈕下的“助學金”鏈接可以申請助學金。您可以根據屏幕提示完成申請,申請獲批后會收到通知。您需要針對專項課程中的每一門課程完成上述步驟,包括畢業項目。了解更多。

我可以免費學習課程嗎?

完成注冊課程后,您可以學習專項課程中的所有課程,并且完成作業后可以獲得證書。如果您只想閱讀和查看課程內容,可以免費旁聽該課程。如果您無法承擔課程費用,可以申請助學金。

此課程是 100% 在線學習嗎?是否需要現場參加課程?

此課程完全在線學習,無需到教室現場上課。您可以通過網絡或移動設備隨時隨地訪問課程視頻、閱讀材料和作業。

完成專項課程后我會獲得大學學分嗎?

此專項課程不提供大學學分,但部分大學可能會選擇接受專項課程證書作為學分。查看您的合作院校了解詳情。

完成專項課程需要多長時間?

16 weeks

What background knowledge is necessary?

Fundamentals in python programming is recommended. Basic understanding of machine learning is a plus.

Do I need to take the courses in a specific order?

Yes, please take the fundamentals course first

What will I be able to do upon completing the Specialization?

You will be able to perform as a Lead Data Scientist or Architect

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