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The rapid pace of innovation in Artificial Intelligence (AI) is creating enormous opportunity for transforming entire industries and our very existence. After competing this comprehensive 6 course Professional Certificate, you will get a practical understanding of Machine Learning and Deep Learning.

You will master fundamental concepts of Machine Learning and Deep Learning, including supervised and unsupervised learning. You will utilize popular Machine Learning and Deep Learning libraries such as SciPy, ScikitLearn, Keras, PyTorch, and Tensorflow applied to industry problems involving object recognition and Computer Vision, image and video processing, text analytics, Natural Language Processing, recommender systems, and other types of classifiers.

You will be able to scale Machine Learning on Big Data using Apache Spark. You will build, train, and deploy different types of Deep Architectures, including Convolutional Networks, Recurrent Networks, and Autoencoders.

By the end of this Professional Certificate, you will have completed several projects showcasing your proficiency in Machine Learning and Deep Learning, and become armed with skills for a career as an AI Engineer.

學生職業成果
38%
完成此 專項課程 後開始了新的職業。
18%
加薪或升職。
可分享的證書
完成後獲得證書
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8 個月
建議 3 小時/週
英語(English)
字幕:英語(English)
學生職業成果
38%
完成此 專項課程 後開始了新的職業。
18%
加薪或升職。
可分享的證書
完成後獲得證書
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8 個月
建議 3 小時/週
英語(English)
字幕:英語(English)

此專業證書包含 6 門課程

課程1

課程 1

使用 Python 进行机器学习

4.7
8,856 個評分
1,404 條評論
課程2

課程 2

Scalable Machine Learning on Big Data using Apache Spark

3.9
813 個評分
201 條評論
課程3

課程 3

Introduction to Deep Learning & Neural Networks with Keras

4.7
614 個評分
118 條評論
課程4

課程 4

Deep Neural Networks with PyTorch

4.4
563 個評分
122 條評論

提供方

IBM 徽標

IBM

常見問題

  • If you subscribed, you get a 7-day free trial during which you can cancel at no penalty. After that, we don’t give refunds, but you can cancel your subscription at any time. See our full refund policy.

  • Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate, or you can audit it to view the course materials for free. When you subscribe to a course that is part of a Certificate, you’re automatically subscribed to the full Certificate. Visit your learner dashboard to track your progress.

  • This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

  • 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.

  • This Professional Certificate pre-requisties 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 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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