This course provides an easy-to-understand overview of machine learning for anyone interested in how it works, what it can and cannot do and how it is commonly utilized in support of business goals. The course covers common algorithm types and further explains how machine learning systems work behind the scenes.
Machine learning is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence
Course Outline
The following primary topics are covered: – Machine Learning Business and Technology Drivers – Machine Learning Benefits and Challenges – Machine Learning Usage Scenarios – Datasets, Structured, Unstructured and Semi-Structured Data – Models, Algorithms, Model Training and Learning – How Machine Learning Works – Collecting and Pre-Processing Training Data – Algorithm and Model Selection – Training Models and Deploy Trained Models – Machine Learning Algorithms and Practices – Supervised Learning, Classification, Decision Tree – Regression, Ensemble Methods, Dimension Reduction – Unsupervised Learning and Clustering – Semi-Supervised and Reinforcement Learning – Machine Learning Best Practices – How Machine Learning Systems Work – Common Machine Learning Mechanisms – How Mechanisms Are Used in Model Training – Machine Learning and Deep Learning, Artificial Intelligence (AI)