Introduction to AI from edX

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 turbulent history of AI and its evolution into today’s powerful technology
How AI, machine learning, and data science fit together , including their definitions, examples, and interrelationship
Current and potential future applications of AI in various industries
Fundamental machine learning concepts , including classifiers, linear regression, and neural networks
Training, validation, and test data : how to prepare and evaluate machine learning models
Optimizers and loss functions : building blocks for fine-tuning your models
Ethical and social considerations : exploring AI’s benefits, challenges, and the importance of responsible development
Power consumption vs. sustainability : balancing performance and efficiency with environmental impact
Practical frameworks , such as PyTorch, for implementing and training ML models
AI in the cloud and on the edge : deploying AI across diverse platforms and computing environments

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.

What's inside

Learning objectives

You will:
Define ai, machine learning, and data science, as well as build knowledge of examples and uses of each.
Explore the interrelationships between ai, machine learning, and data science.
Build understanding of the benefits and challenges of ai, including the ethical and social issues involved.

Examine a range of neural networks, ml models, and ml frameworks, and explore their applications (including training).
Explore the discussion around balancing power consumption and sustainability.
Apply the skills and knowledge you have gained across the course to build, train, and run your own ml classification model.

You will:
Define ai, machine learning, and data science, as well as build knowledge of examples and uses of each.
Explore the interrelationships between ai, machine learning, and data science.
Build understanding of the benefits and challenges of ai, including the ethical and social issues involved.
Examine a range of neural networks, ml models, and ml frameworks, and explore their applications (including training).
Explore the discussion around balancing power consumption and sustainability.
Apply the skills and knowledge you have gained across the course to build, train, and run your own ml classification model.

Syllabus

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.

Good to know

Know what's good

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Offers a hands-on capstone project using the PyTorch framework and the CIFAR-10 dataset, allowing learners to apply newly acquired skills to a real-world image classification challenge

Presented by engineers from Arm, which is known for its work in computer architecture and embedded systems, providing learners with insights grounded in real-world engineering practices

Explores the ethical, social, and environmental dilemmas of AI, which is crucial for responsible development and deployment in today's world, and helps learners think critically about its impact

Uses the PyTorch framework, which is widely adopted in both research and industry, providing learners with practical skills applicable to real-world AI projects and further study

Examines a range of neural networks, ML models, and ML frameworks, and explores their applications, including training, which is essential for building and deploying effective AI systems

Addresses the pressing issue of energy consumption in AI, which is increasingly important for sustainable development and responsible innovation in the field of artificial intelligence

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Introduction to AI with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, which are foundational for many machine learning algorithms.

Browse courses on Linear Algebra

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Review key concepts like vectors, matrices, and matrix operations.
Practice solving linear equations and eigenvalue problems.

Create a Glossary of AI Terms

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Consolidate your understanding of AI terminology by creating a glossary.

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Identify key terms and concepts from the course materials.
Write clear and concise definitions for each term.
Organize the glossary alphabetically or by topic.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow'

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Gain practical experience with machine learning frameworks and techniques covered in the course.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters relevant to the course syllabus.
Experiment with the code examples provided in the book.

Four other activities

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Practice PyTorch Tutorials

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Solidify your understanding of PyTorch by working through official tutorials.

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Complete the PyTorch tutorials on the official website.
Focus on tutorials related to neural networks and image classification.

Implement a Simple Neural Network from Scratch

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Deepen your understanding of neural network architecture and training by building one from scratch.

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Design the architecture of a simple neural network.
Implement the forward and backward propagation algorithms.
Train the network on a small dataset like MNIST.
Evaluate the performance of the network.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Expand your knowledge of deep learning theory and algorithms.

View Deep Learning on Amazon

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Read the chapters relevant to neural networks and deep learning.
Focus on the mathematical foundations and theoretical concepts.

Create a Blog Post on AI Ethics

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Reflect on the ethical and social implications of AI by writing a blog post.

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Research current ethical issues in AI development.
Write a blog post summarizing your findings and offering your perspective.
Share your blog post on social media or relevant online forums.

Career center

Learners who complete Introduction to AI will develop knowledge and skills that may be useful to these careers:

Machine Learning Engineer

A Machine Learning Engineer designs, builds, and deploys machine learning models and systems. This role requires a strong understanding of algorithms, statistical modeling, and software engineering principles. This course helps build a foundation in the core concepts of machine learning, including classifiers, linear regression, neural networks, and practical frameworks like PyTorch. The hands-on capstone project using the CIFAR-10 dataset allows aspiring Machine Learning Engineers to gain practical experience in applying these concepts to real-world challenges, like image classification. Learning about training, validation, and test data ensures a successful career as a Machine Learning Engineer.

See salaries and explore the career path for Machine Learning Engineer

Data Scientist

A Data Scientist analyzes large datasets to extract meaningful insights and develop data-driven solutions. To be successful, they use statistical modeling, machine learning, and data visualization techniques. This course helps build a foundation in the fundamentals of data science, machine learning, and AI. By exploring the relationships between these fields and understanding key algorithms and models, learners gain skills in data analysis and interpretation. The course's focus on ethical considerations and the balance between performance and sustainability also prepares future Data Scientists to develop socially responsible and environmentally conscious solutions. A hands-on capstone project will be valuable to a Data Scientist.

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AI Application Developer

An AI Application Developer focuses on integrating AI capabilities into software applications. This involves working with machine learning models, APIs, and various software development tools. This course provides a strong understanding of AI concepts and frameworks, such as PyTorch, which are essential for developing AI-powered applications. The curriculum's coverage of AI in the cloud and on the edge prepares developers to deploy AI solutions across diverse platforms. Furthermore, the hands-on experience gained through the capstone project helps solidify the skills needed to build and implement AI functionalities within software applications. One who wishes to become an AI Application Developer would find deep value in this course.

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Computer Vision Engineer

A Computer Vision Engineer specializes in developing algorithms and systems that enable computers to "see" and interpret images. This role involves working with image processing techniques, machine learning models, and computer vision frameworks. This course introduces you to the fundamentals of machine learning and neural networks, which are crucial for computer vision tasks. The capstone project, which focuses on image classification using the CIFAR-10 dataset, provides hands-on experience directly relevant to computer vision applications. Learning about Convolutional Neural Networks helps one to understand how to approach image-related problems as a Computer Vision Engineer.

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Software Engineer

A Software Engineer designs, develops, and maintains software systems. As AI becomes increasingly integrated into software applications, a solid understanding of AI concepts and tools is valuable. This course helps build a foundation in the fundamentals of AI and machine learning, enabling software engineers to incorporate AI capabilities into their projects. By learning about frameworks like PyTorch, software engineers can develop and deploy AI models within their software applications. This course is therefore highly valuable for a Software Engineer who wishes to learn more about AI.

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AI Product Manager

An AI Product Manager defines the strategy, roadmap, and features for AI-powered products. Success requires a strong understanding of AI technologies, market trends, and customer needs. This course helps build a foundation in the fundamentals of AI, machine learning, and data science, enabling product managers to make informed decisions about AI product development. By exploring the ethical and social considerations of AI, product managers can develop responsible and user-centric AI products. The AI Product Manager would find significant value in exposure to the technologies and considerations around AI.

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AI Consultant

An AI Consultant advises organizations on how to leverage AI technologies to improve their business processes and outcomes. A solid understanding of AI capabilities, limitations, and ethical considerations is needed in this role. This course helps build a foundation in the fundamentals of AI, machine learning, and data science, enabling consultants to provide informed recommendations to clients. By exploring the ethical and social issues surrounding AI, consultants can guide organizations in implementing responsible and ethical AI solutions. An AI Consultant would benefit by understanding the key applications of AI.

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Technical Writer

A Technical Writer creates documentation, guides, and tutorials that explain complex technical concepts. This course helps build a foundation in the fundamentals of AI, machine learning, and data science, enabling technical writers to accurately and effectively communicate these concepts to a broader audience. By understanding the interrelationships between these fields and the key algorithms and models used, technical writers can produce clear and concise documentation for AI tools and technologies. A Technical Writer working in the field of AI will benefit from the knowledge gained in this course.

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Robotics Engineer

A Robotics Engineer designs, builds, and programs robots for various applications in manufacturing, healthcare, and exploration. Many robotics applications now employ AI. This course helps build a foundation in the AI and machine learning concepts used in modern robotics. By understanding how AI models are trained and deployed, Robotics Engineers can integrate AI capabilities into robotic systems. The course's coverage of AI on the edge is particularly relevant as robots often operate in environments with limited connectivity. This combination of skills is valuable for creating intelligent and autonomous robots.

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Data Analyst

A Data Analyst collects, processes, and analyzes data to identify trends, patterns, and insights that can inform business decisions. This course helps build a foundation in the core concepts of machine learning and data analysis. By understanding how AI and machine learning models work, data analysts can leverage these tools to extract more valuable insights from data. The course emphasizes the interrelationships between AI, machine learning, and data science. A Data Analyst would therefore understand how these fields complement each other in the data analysis process.

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AI Hardware Engineer

An AI Hardware Engineer designs and develops specialized hardware to accelerate AI workloads. This course helps build a foundation in the understanding of AI algorithms and models, enabling hardware engineers to optimize hardware architectures for AI applications. The curriculum's exploration of power consumption versus sustainability is particularly relevant as hardware engineers strive to create energy-efficient AI hardware. An AI Hardware Engineer will find the insights gained from this course to be quite valuable.

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AI Research Scientist

An AI Research Scientist conducts research to advance the field of artificial intelligence. This role involves developing new algorithms, models, and techniques, often requiring an advanced degree. This course may be useful because it introduces the fundamental concepts and frameworks used in AI research. Exploring the historical evolution of AI, current capabilities, and potential future developments provides context for innovative research. The course's coverage of advanced topics like Convolutional Neural Networks and Recurrent Neural Networks helps build a foundation for conducting research in specialized areas of AI. An AI Research Scientist may especially benefit from this course.

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AI Trainer

An AI Trainer is responsible for teaching and training artificial intelligence models using various techniques and datasets. They play a crucial role in optimizing model performance. This course may be useful, as it covers fundamental machine learning concepts, including classifiers, linear regression, and neural networks, all essential for effective AI training. Moreover, the course's focus on training, validation, and test data, along with the exploration of optimizers and loss functions, provides valuable insights into the nuances of model training. The role of the AI Trainer requires an understanding of these principles.

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Cloud Architect

A Cloud Architect designs and oversees the implementation of cloud computing solutions. This course may be useful because it discusses AI in the cloud and AI at the edge, exploring the benefits, challenges, and uses of each. This knowledge is directly applicable to designing cloud-based AI solutions. Additionally, understanding frameworks like PyTorch, covered in the course, enables architects to evaluate and implement AI models within cloud environments. An understanding of machine learning will be quite useful for current and future Cloud Architects.

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Natural Language Processing Engineer

A Natural Language Processing Engineer builds systems that enable computers to understand, interpret, and generate human language. This requires a strong understanding of machine learning, linguistics, and software engineering. This course may be useful by introducing the foundational concepts of AI and machine learning that underpin NLP techniques. The course's coverage of models like BERT and GPT provides insight into state-of-the-art language models. This background may enable a Natural Language Processing Engineer to develop more sophisticated NLP applications and gain a firmer understanding of the field.

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Introduction to AI

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