AI & Machine Learning

Course Description

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)

Focus: Discover the world of Artificial Intelligence (AI) and Machine Learning. Build, train,
and deploy AI models and dive into deep learning frameworks to unlock the power of
automation and intelligent systems.