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Intro to TensorFlow for Deep Learning

Intro to TensorFlow for Deep Learning

Upskilling is a better roadmap to success. Enroll in this course to learn critical principles of Machine Learning through real-life case studies & examples

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Course Highlights

  • Start instantly and learn at your own schedule.

  • Earn a certificate of learning on course completion 

  • Taught by Industry Pros

  • Rich Learning Content

Duration: 60 Days

Mode of learning: Online self study

Course Overview

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
  • Learn how to build deep learning applications with TensorFlow. This course was developed by the TensorFlow team and Udacity as a practical approach to deep learning for software developers. You'll get hands-on experience building your own state-of-the-art image classifiers and other deep learning models. You'll also use your TensorFlow models in the real world on mobile devices, in the cloud, and in browsers. Finally, you'll use advanced techniques and algorithms to work with large datasets. By the end of this course, you'll have all the skills necessary to start creating your own AI applications.

Eligibility
  • It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization and you have a solid understanding of the following topics prior to starting this course: Fundamental understanding of Linear Algebra; Understand sampling, probability theory, and probability distributions; Knowledge of descriptive and inferential statistical concepts; General understanding of machine learning techniques and best practices; Practiced understanding of Python and the packages commonly used in data science: NumPy, Pandas, matplotlib, scikit-learn; Familiarity with IBM Watson Studio; Familiarity with the design thinking process.

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