关于此 专业证书

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Artificial intelligence (AI) is revolutionizing entire industries, changing the way companies across sectors leverage data to make decisions. To stay competitive, organizations need qualified AI engineers who use cutting-edge methods like machine learning algorithms and deep learning neural networks to provide data driven actionable intelligence for their businesses. This 6-course Professional Certificate is designed to equip you with the tools you need to succeed in your career as an AI or ML engineer. You’ll master fundamental concepts of machine learning and deep learning, including supervised and unsupervised learning, using programming languages like Python. You’ll apply popular machine learning and deep learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow to industry problems involving object recognition, computer vision, image and video processing, text analytics, natural language processing (NLP), recommender systems, and other types of classifiers. Through hands-on projects, you’ll gain essential data science skills scaling machine learning algorithms on big data using Apache Spark. You’ll build, train, and deploy different types of deep architectures, including convolutional neural networks, recurrent networks, and autoencoders. In addition to earning a Professional Certificate from Coursera, you will also receive a digital badge from IBM recognizing your proficiency in AI engineering.
学生职业成果
38%
完成此 专项课程 后开始了新的职业。
18%
加薪或升职。
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
完成时间大约为8 个月
建议 3 小时/周
英语(English)
字幕:英语(English)
学生职业成果
38%
完成此 专项课程 后开始了新的职业。
18%
加薪或升职。
可分享的证书
完成后获得证书
100% 在线课程
立即开始,按照自己的计划学习。
灵活的计划
设置并保持灵活的截止日期。
中级
完成时间大约为8 个月
建议 3 小时/周
英语(English)
字幕:英语(English)

此专业证书包含 6 门课程

课程1

课程 1

使用 Python 进行机器学习

4.7
9,367 个评分
1,508 条评论
课程2

课程 2

Scalable Machine Learning on Big Data using Apache Spark

3.9
912 个评分
230 条评论
课程3

课程 3

Introduction to Deep Learning & Neural Networks with Keras

4.7
684 个评分
130 条评论
课程4

课程 4

Deep Neural Networks with PyTorch

4.4
637 个评分
143 条评论

提供方

IBM 徽标

IBM

常见问题

  • 此专项课程不提供大学学分,但部分大学可能会选择接受专项课程证书作为学分。查看您的合作院校,了解详情。Coursera 上的在线学位Mastertrack™ 证书提供获得大学学分的机会。

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  • A​n understanding of artificial intelligence can be used to support many careers, but some careers specifically require a background in AI. Some examples of careers in AI include:

    -​ AI Developer

    -​ Data Analyst

    -​ Data Engineer

    -​ Data Scientist

    -​ Machine Learning Engineer

    -​ Marketing Analyst

    -​ Operations Analyst

    -​ Quantitative Analyst

    -​ Software Analyst

    -​ Software Developer

    -​ Software Engineer

    -​ User Experience Engineer

  • This Professional Certificate consists of 6 self-paced courses. Each course takes 4-5 weeks to complete if you spend 2-4 hours working through the course per week. At this rate, the entire Professional Certificate can be completed in 3-6 months. However, you are welcome to complete the program more quickly or more slowly, depending on your preference.

  • This Professional Certificate's pre-requisites includes the following skills:

    • High school mathematics or math for machine learning

    It is highly recommended that you complete either or both of the following Professional Certificates before starting this one:

  • 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 program.

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

    • Describe what machine learning (ML), deep learning (DL), and neural networks are
    • 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 back propagation 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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