Artificial Intelligence and Machine Learning Fundamentals

Description

This course introduces the basics of artificial intelligence (AI) and machine learning (ML). Students will learn to build predictive models, understand AI algorithms, and explore real-world applications of AI in industries like healthcare, finance, and robotics. Objectives: Understand AI and ML concepts and terminology. Develop basic predictive models using Python. Explore supervised, unsupervised, and reinforcement learning. Apply AI techniques to practical problems. Target Audience: Beginners in AI/ML, students, and tech professionals seeking foundational AI skills. Prerequisites: Basic programming in Python High-school level mathematics Learning Outcomes: Implement simple machine learning algorithms. Analyze datasets and draw insights. Build and evaluate predictive models. Understand ethical considerations in AI.

Prerequisites

Basic programming in Python High-school level mathematics

Syllabus

Introduction to AI and ML Python for Machine Learning Data preprocessing and visualization Supervised learning: regression, classification Unsupervised learning: clustering, dimensionality reduction Reinforcement learning basics AI applications in real-world scenarios Capstone project: build a predictive model Assessment Methods: Quizzes Python coding exercises Mini-projects Final capstone project Resources: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow – Aurélien Géron Coursera, Kaggle, freeCodeCamp