This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.
This course is part of the IBM Machine Learning Professional Certificate
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About this Course
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Try Coursera for BusinessSkills you will gain
- Deep Learning
- Artificial Neural Network
- Machine Learning
- Reinforcement Learning
- keras
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Syllabus - What you will learn from this course
Introduction to Neural Networks
Back Propagation Training and Keras
Neural Network Optimizers
Convolutional Neural Networks
Reviews
- 5 stars72.80%
- 4 stars16%
- 3 stars7.20%
- 2 stars1.60%
- 1 star2.40%
TOP REVIEWS FROM DEEP LEARNING AND REINFORCEMENT LEARNING
Hello, thank you again for the course. My congrats, once more, to the instructor on the videos!
The concepts were clearly explained in lectures. The assignments were very helpful to gain a practical insight of the skills learned in the course.
The core concepts of Deep Learning are explained well in this course.
Reinforcement Learning part needs to be a separate course and more details in it
About the IBM Machine Learning Professional Certificate

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