Deep Learningwith Tensorflow at edX Overview
Deep Learningwith Tensorflow
at edX
Upskilling is a better roadmap to success. Enroll in this course to learn critical principles of Machine Learning through real-life case studies & examples
Duration | 29 hours |
Total fee | Free |
Mode of learning | Online |
Schedule type | Self paced |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Deep Learningwith Tensorflow
Table of contents
- Overview
- Highlights
- Course Details
- Curriculum
- Entry Requirements
- + 1 more item
Deep Learningwith Tensorflow at edX Highlights
Deep Learningwith Tensorflow
at edX
- Earn a certificate of learning on course completion
- Add a Verified Certificate for ? 7,568
- This course is offered by IBM
Deep Learningwith Tensorflow at edX Course details
Deep Learningwith Tensorflow
at edX
Skills you will learn
Who should do this course?
- This course targets existing data science practitioners that have expertise building machine learning models, who want to deepen their skills on building and deploying AI in large enterprises. If you are an aspiring Data Scientist, this course is NOT for you as you need real world expertise to benefit from the content of these courses.
What are the course deliverables?
- Discuss common regression, classification, and multilabel classification metrics
- Explain the use of linear and logistic regression in supervised learning applications
- Describe common strategies for grid searching and cross-validation
- Employ evaluation metrics to select models for production use
- Explain the use of tree-based algorithms in supervised learning applications
- Explain the use of Neural Networks in supervised learning applications
- Discuss the major variants of neural networks and recent advances
- Create a neural net model in Tensorflow
- Create and test an instance of Watson Visual Recognition
- Create and test an instance of Watson NLU
More about this course
- Traditional neural networks rely on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kind of nets are capable of discovering hidden structures withinunlabeled and unstructured data (i.e. images, sound, and text), which consitutes the vast majority of data in the world
- TensorFlow is one of the best libraries to implement deep learning. TensorFlow is a software library for numerical computation of mathematical expressional, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Deep Learningwith Tensorflow at edX Curriculum
Deep Learningwith Tensorflow
at edX
Model Evaluation and Performance Metrics
Building Machine Learning and Deep Learning Models
Deep Learningwith Tensorflow at edX Entry Requirements
Deep Learningwith Tensorflow
at edX
Eligibility criteria
- Basic understanding of machine learning techniques, Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn.
Conditional Offer
- Not mentioned
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Deep Learningwith Tensorflow
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