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AIWorkflow: Machine Learning, Visual Recognition and NLP

AIWorkflow: Machine Learning, Visual Recognition and NLP

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


Course Highlights

  • Enroll for free | Pay only for getting a verified certificate

  • Earn a certificate of learning on course completion 

  • This course is offered by IBM

  • SKILLS YOU WILL GAIN - Data Science,Information Engineering, Artificial Intelligence (AI), Machine Learning, Python Programming

Duration: 14 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
  • This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones

  • Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regression metrics, classification metrics, and multi-class metrics, which you will use to select the best model for your business challenge. The next topics cover best practices for different types of models including linear models, tree-based models, and neural networks. Out-of-the-box Watson models for natural language understanding and visual recognition will be used. There will be case studies focusing on natural language processing and on image analysis to provide realistic context for the model pipelines.

  • 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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