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Discover the fundamental concepts behind artificial intelligence (AI) and machine learning in this introductory course. Explore the various types of AI, examine ethical considerations, and delve into the key machine learning models that power modern AI systems. Whether your goal is to work directly with AI, strengthen your software development skills, or enhance your data science expertise, this course provides an essential foundation for success in the field.
Are you curious about how artificial intelligence (AI) really works? Wondering which models power these systems, and how they impact society and the environment? Presented by engineers from Arm, this course offers a comprehensive introduction to AI, machine learning, and data science—shedding light on their historical evolution, current capabilities, and potential future developments.
By exploring both the technical concepts and the broader ethical, social, and environmental dilemmas, you will gain a well-rounded understanding of AI’s potential and challenges. You’ll discover how AI, machine learning, and data science interrelate; understand the fundamental algorithms, models, and frameworks; and learn how to apply these concepts in real-world scenarios. The course also addresses the pressing issue of energy consumption in AI.
Key Topics Covered
The course culminates with a hands-on capstone project using the PyTorch framework and the CIFAR-10 dataset, allowing you to apply newly acquired skills to a real-world image classification challenge. Whether you’re a budding data scientist, a developer looking to integrate AI into your projects, or simply an AI enthusiast, this course offers both the foundational knowledge and practical skills needed to excel in the rapidly evolving world of artificial intelligence.
You will:
Module 1: Introduction to Artificial Intelligence
In this first module you will explore the history of AI, as well as current and potential future developments in the technology.
Module 2: AI and Machine Learning
In this module you will dive into the basic concepts behind machine learning, focusing on key algorithms, models, and linear regression.
Module 3: What's in the Black Box? Deep Learning and Neural Networks
During this module you will study the architecture of neural networks. You will then apply your learning to examine the MNIST dataset using an Artificial Neural Network (ANN).
Module 4: Training and Evaluating Models
In this module you will build an understanding of training, validation, and test data. You’ll learn the difference between overfitting and underfitting, as well as how to identify and address them. You’ll also explore optimizers and loss functions. You will then apply your learning to the MNIST dataset by training and evaluating a machine learning model, then adjusting parameters to classify images. The module concludes with a critical look at balancing power consumption, performance, and sustainability.
Module 5: Advanced Topics in AI
In this module you will compare and contrast Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). You will build an understanding of ‘back propagation’, ‘feed forward’, and predictions. You will also learn about BERT and GPT as examples of transformers. Finally, you’ll delve into the PyTorch framework and find out how it is used for AI and ML applications.
Module 6: Challenges and the Future of AI
In this final module you will discuss AI in the cloud and AI at the edge: the benefits and challenges of each, and their uses. The course finishes with an opportunity for you to get hands on with machine learning, by carrying out a capstone project using the PyTorch framework and the CIFAR-10 dataset.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
Who can take this course?
Unfortunately, learners residing in one or more of the following countries or regions will not be able to register for this course: Iran, Cuba and the Crimea region of Ukraine. While edX has sought licenses from the U.S. Office of Foreign Assets Control (OFAC) to offer our courses to learners in these countries and regions, the licenses we have received are not broad enough to allow us to offer this course in all locations. edX truly regrets that U.S. sanctions prevent us from offering all of our courses to everyone, no matter where they live.
