關於此 專業證書
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100% 在線課程

立即開始,按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

完成時間大約為2 個月

建議 12 小時/週

英語(English)

字幕:英語(English)

您將獲得的技能

Data ScienceDeep LearningArtificial Intelligence (AI)Machine LearningApache Spark
學習Professional Certificate的學生是
  • Data Scientists
  • Machine Learning Engineers
  • Data Analysts
  • Risk Managers
  • Data Engineers

100% 在線課程

立即開始,按照自己的計劃學習。

靈活的計劃

設置並保持靈活的截止日期。

中級

完成時間大約為2 個月

建議 12 小時/週

英語(English)

字幕:英語(English)

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當完後計劃中的所有課程後,您將獲得一張證書。您可以將其在專業網絡上分享,並獲得使用職業支持資源的權限,這能夠為您開啟職業生涯提供助力。許多招聘合作夥伴認可我們的許多專業證書,並且我們還有許多合作夥伴可以幫助您準備認證考試。您可以在適用的各個專業證書頁面上找到更多信息。

how it works

此專業證書包含 6 門課程

課程1

使用 Python 进行机器学习

4.7
3,588 個評分
449 個審閱
課程2

Scalable Machine Learning on Big Data using Apache Spark

4.4
18 個評分
1 個審閱
課程3

Introduction to Deep Learning & Neural Networks with Keras

4.9
9 個評分
2 個審閱
課程4

Deep Neural Networks with PyTorch

講師

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SAEED AGHABOZORGI

Ph.D., Sr. Data Scientist
IBM Developer Skills Network
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Romeo Kienzler

Chief Data Scientist, Course Lead
IBM Watson IoT
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Alex Aklson

Ph.D., Data Scientist
IBM Developer Skills Network
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Joseph Santarcangelo

Ph.D., Data Scientist at IBM
IBM Developer Skills Network

關於 IBM

IBM offers a wide range of technology and consulting services; a broad portfolio of middleware for collaboration, predictive analytics, software development and systems management; and the world's most advanced servers and supercomputers. Utilizing its business consulting, technology and R&D expertise, IBM helps clients become "smarter" as the planet becomes more digitally interconnected. IBM invests more than $6 billion a year in R&D, just completing its 21st year of patent leadership. IBM Research has received recognition beyond any commercial technology research organization and is home to 5 Nobel Laureates, 9 US National Medals of Technology, 5 US National Medals of Science, 6 Turing Awards, and 10 Inductees in US Inventors Hall of Fame....

常見問題

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  • 此课程完全在线学习,无需到教室现场上课。您可以通过网络或移动设备随时随地访问课程视频、阅读材料和作业。

  • This Professional Certificate consists of 6 self-paced courses. Effort required to complete each course is 4-5 weeks if spending 2-4 hours per week. At this rate the entire specialization can be completed in 3-6 months.

  • It is highly recommended to complete the courses in the suggested order.

  • At this time there is no university credit for completing courses in this specialization.

  • Upon completing this Professional Certificate you will be able to:

    • Describe what is Machine Learning (ML), Deep Learning (DL) & Neural Networks
    • Explain ML algorithms including Classification, Regression, Clustering, and Dimensional Reduction
    • Implement Supervised and Unsupervised ML models using scipy and scikitlearn
    • Express how Apache Spark works and how to perform Machine Learning on Big Data
    • Deploy ML Algorithms and Pipelines on Apache Spark
    • Demonstrate an understanding of Deep Learning models such as autoencoders, restricted Boltzmann machines,  convolutional networks, recursive neural networks, and recurrent networks
    • Build deep learning models and neural networks using the Keras library
    • Utilize the PyTorch library for Deep Learning applications and build Deep Neural Networks
    • Explain foundational TensorFlow concepts like main functions, operations & execution pipelines
    • Apply deep learning using TensorFlow and perform backpropagation to tune the weights and biases
    • Determine what kind of deep learning method to use in which situation and build a deep learning model to solve a real problem
    • Demonstrate ability to present and communicate outcomes of deep learning projects

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