Machine Learning Fundamentals
Session with George Anadiotis (Founder, Principal Consultant, Linked Data Orchestration)
About this Session
Demystifying AI's Core Technology
Unlock the technology powering today's AI revolution through this accessible introduction to machine learning. This session bridges the gap between traditional analytics and modern AI applications, breaking down complex concepts into understandable frameworks.
Discover the key differences between supervised, unsupervised, and reinforcement learning, and get a clear explanation of neural networks that drive today's most powerful systems.
Learn the essential concepts data scientists use daily — from feature engineering to managing bias — explained through real-world examples. We introduce common algorithms like linear regression and decision trees, showing how these building blocks combine to create sophisticated AI solutions.
- Outline
- Introduction: From analytics to machine learning
- Types of Machine Learning
- Supervised Machine Learning
- Unsupervised Machine Learning
- Reinforcement Learning
- Neural Networks and Deep Learning
- Machine Learning Concepts
- Workflow
- Datasets
- Feature Engineering
- Embeddings
- Bias & Variance
- Precision & Accuracy
- Machine Learning Algorithms:
- Linear Regression
- Decision Trees
- Format
- This session is lecture-based.
- Level
- Beginner
- Prerequisite Knowledge
- Data Science and Statistical Analysis
- Exploratory Data Analysis and Visualization
- Learning Outcomes
- Knowledge of machine learning key concepts and algorithms
