Machine Learning Basics

Machine Learning Basics

At its core, Machine Learning Basics is about teaching computers to “learn” from data. Instead of coding every rule by hand, we provide data and algorithms that allow systems to spot patterns and improve over time. For instance, an app predicting house prices or suggesting your next favorite movie works through machine learning. These systems adapt and get smarter as more data becomes available.

Gaining knowledge of machine learning fundamentals gives you the ability to understand how machines make predictions and classifications. It explains essential techniques like training and testing models, preparing datasets, and interpreting outcomes. By learning these basics, individuals build a strong foundation to explore artificial intelligence, big data, and advanced predictive analytics across multiple fields.

Who should take the Exam?

This exam is ideal for:

• Students exploring artificial intelligence and data science
• Beginners in programming who want to branch into AI
• Professionals in business, marketing, or finance who want to apply ML to their work
• IT professionals looking to upskill in AI-driven technologies
• Data analysts and software developers
• Entrepreneurs building AI-powered products
• Educators and trainers introducing AI concepts

Skills Required

• Basic understanding of mathematics (algebra, probability, statistics)
• Familiarity with Python or another programming language
• Analytical and logical thinking
• Curiosity about data-driven decision-making
• Problem-solving mindset

Course Outline

Domain 1 - Introduction to Machine Learning

Domain 2 - Types of Machine Learning

Domain 3 - Mathematical Foundations

Domain 4 - Data in Machine Learning

Domain 5 - Machine Learning Algorithms

Domain 6 - Model Training and Evaluation

Domain 7 - Machine Learning Tools and Libraries

Domain 8 - Applications of Machine Learning