AI Workflow: Machine Learning, Visual Recognition and NLP

It is assumed that you have completed Courses 1 through 3 of the IBM AI Enterprise Workflow specialization

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

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

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