Introduction to Artificial Intelligence (2026)

Introduction to Artificial Intelligence (2026)

Master Artificial Intelligence Foundations with Machine Learning, Generative AI, LLMs, and Prompt Engineering

What you’ll learn
-Understand the fundamentals of Artificial Intelligence, including AI types, machine learning, deep learning, major AI architectures, and real-world applications
-Prepare, analyze, and work with data for AI applications, including data preprocessing, exploratory data analysis (EDA), feature engineering, and model evaluati
-Build and evaluate basic Machine Learning and Neural Network models using Python-based tools such as Scikit-learn, TensorFlow, and Keras.
-Apply core AI technologies such as Natural Language Processing, Computer Vision, and Speech Recognition using standard libraries and pretrained models.
-Understand Large Language Models (LLMs) and Generative AI, including transformers, tokenization, embeddings, text and image generation, and common GenAI applica
-Use prompt engineering techniques effectively to create, refine, and evaluate prompts for text generation, summarization, classification, coding, and other AI t
-Select suitable AI algorithms and evaluation metrics for common classification and regression problems and interpret model performance.
-Recognize important ethical and responsible AI considerations, including bias, hallucinations, privacy, safety risks, and appropriate mitigation practices.

Requirements
-Basic computer literacy and computer skills are required to comfortably follow the course.
-No prior knowledge of Artificial Intelligence or Machine Learning is required, as the course introduces AI from the foundational level.
-Familiarity with Python programming is recommended for completing the hands-on and practical portions of the course.
-Learners should have an interest in understanding Artificial Intelligence and its real-world applications across different domains.
-A willingness to participate in hands-on activities involving data, Machine Learning, Neural Networks, NLP, Computer Vision, Generative AI, and Prompt Engineering will help learners get the most from the course.

Description

This course contains the use of artificial intelligence.
Artificial Intelligence is transforming the way we learn, work, communicate, analyze information, and solve problems.Introduction to Artificial Intelligence (Foundations) is designed to provide learners with a strong foundation in AI while introducing them to the technologies, concepts, and practical applications shaping today’s AI-driven world.

The course begins with thefundamentals of Artificial Intelligence, including the evolution and types of AI, differences between AI, Machine Learning, and Data Science, and real-world AI applications across industries and professions. Learners will explore the major components of AI systems, including learning, reasoning, perception, language, action, and knowledge.

The course introduces the essentialmathematical and algorithmic foundations of AI, followed by practical concepts in Data Science andMachine Learning. Learners will explore data collection, cleaning, preprocessing, exploratory data analysis (EDA), feature engineering, model training, and evaluation. They will also gain an understanding of supervised, unsupervised, and reinforcement learning and work with common Machine Learning algorithms.

Moving further, learners will exploreNeural Networks and Deep Learning, including neurons, weights, activation functions, forward propagation, backpropagation, and the process of building a basic neural network using Keras and TensorFlow.

The course also introduces important AI domains such asNatural Language Processing (NLP), Large Language Models (LLMs), Speech Recognition, and Computer Vision. Learners will explore practical concepts such as text preprocessing, sentiment analysis, transformers, tokenization, embeddings, image processing, image classification, and speech-to-text systems.

A dedicated focus onGenerative AI and Prompt Engineering helps learners understand modern generative models, text and image generation, effective prompting techniques, prompt refinement, and practical GenAI applications.

Throughout the course, learners are also introduced toresponsible and ethical AI practices, including awareness of bias, hallucinations, privacy, and safety concerns.

By the end of this course, learners will have developed a broad foundation across key AI technologies and will be better prepared to explore advanced AI topics, build small AI projects, and apply AI concepts to real-world problems.

Who this course is for
-Beginners and intermediate learners who want to build a strong foundation in Artificial Intelligence and understand how AI technologies work.
-Students and aspiring professionals interested in exploring AI, Machine Learning, Neural Networks, Natural Language Processing, Computer Vision, and Generative AI.
-Learners with basic computer skills who want to begin their AI learning journey without requiring advanced prior knowledge.
-Professionals from different fields who want to understand AI concepts and explore how Artificial Intelligence is being applied across industries and job roles.
-Learners interested in practical AI applications who want exposure to data preprocessing, exploratory data analysis, Machine Learning models, NLP, Computer Vision, and Speech Recognition.
-Anyone interested in Generative AI and Large Language Models (LLMs) who wants to understand transformers, prompting, pretrained models, and common GenAI applications.
-Learners interested in Prompt Engineering who want to develop practical skills for creating and refining prompts for AI-powered applications.
-Learners interested in responsible AI who want to understand important issues such as bias, hallucinations, privacy, ethics, and AI safety.

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