Course list

Artificial Intelligence (AI) is no longer a futuristic concept; it’s a reality that is transforming our world. This course uncovers the true essence of AI, cutting through the hype to reveal its practical applications and real-world value. You’ll explore AI’s role in creating significant business opportunities and learn to apply a value-driven framework to identify these opportunities. Through interactive exercises and hands-on data analytics, you’ll gain the skills to characterize the business potential of AI products.

The course will cover the spectrum of AI technologies, from traditional rule-based systems to cutting-edge neural networks and generative AI. You’ll focus on practical applications, learning not only how to leverage AI’s power but also how to address its risks and ethical implications. By the end of this course, you’ll be able to distinguish between hype and reality, formulate AI product ideas with actionable value, and effectively utilize AI algorithms in product development to drive impactful results.

Artificial Intelligence (AI) has advanced to simulate human intelligence, enabling computers to perform tasks such as understanding natural language, learning from experience, problem-solving, and making decisions. This course introduces you to both traditional and modern AI approaches, starting with Good Old-Fashioned AI (GOFAI) which relies on symbolic logic. You’ll learn how to teach computers to make predictions and decisions using machine learning techniques, focusing on practical applications that solve real-world problems and create business value.

Through hands-on exercises, you will design and refine machine learning models, including logistic regression and decision trees. You’ll develop the skills to build AI systems that can predict outcomes and improve over time. By the end of this course, you will be able to create AI solutions that effectively leverage data to meet specific business objectives.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Creating Business Value with AI

In the world of Artificial Intelligence (AI), data is the lifeblood of effective models. The adage “garbage in, garbage out” highlights the necessity of using high-quality data to train AI systems. This course is designed to equip you with the skills to define data requirements and acquire necessary data through web scraping techniques. You will learn to categorize and analyze data for relevance and insights, ensuring its quality through meticulous cleaning and preprocessing.

We will examine various aspects of data preparation, including handling missing values, identifying and addressing outliers, and ensuring data consistency. The course also covers the critical issues of data bias, privacy, and ethical considerations, providing strategies to mitigate these challenges. You will explore how to build resilient AI models and understand the influence of data on different features of a business model. By the end of this course, you will be able to leverage data for strategic competitive advantage and create AI models that drive meaningful business outcomes.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Creating Business Value with AI
  • Exploring Good Old-Fashioned AI (GOFAI)

Good Old-Fashioned AI (GOFAI) is effective for many tasks but has limitations when dealing with complex data patterns. Neural networks (NN), inspired by the human brain, offer a powerful alternative by learning from data patterns and relationships. This course will introduce you to the foundations and architectures of neural networks, enabling you to train, evaluate, and optimize these models to improve performance. You will explore the impact of data volume and model complexity on predictions, ensuring you select the most suitable NN models for various business scenarios. Additionally, the course addresses ethical considerations, such as model bias and data privacy, to ensure responsible AI implementation.

By examining real-world applications and engaging in hands-on exercises, you will develop practical skills in configuring neural networks and evaluating their performance. This course will equip you with the knowledge to apply these techniques effectively and create an action plan to address ethical concerns, ensuring your AI projects are both effective and responsible.

You are required to have completed the following courses or have equivalent experience before taking this course:

    • Creating Business Value with AI
    • Exploring Good Old-Fashioned AI (GOFAI)
    • Leveraging Data for AI Solutions

Generative AI is revolutionizing how we create and innovate, offering new possibilities for product development and user engagement. This course delves into the current uses of generative AI and explores future innovations. You will learn to leverage generative AI to test and refine your value-creation ideas, considering the sociocultural implications of your product concepts. The course will guide you through the process of using generative AI techniques to bring a specific AI product idea to life, with a strong emphasis on ethical considerations, such as model bias and data privacy.

Through hands-on exercises and real-world examples, you will develop practical skills in crafting business ideas using generative AI. You will learn to enhance AI-driven user interfaces and experiences, create effective prompting strategies for AI text generation, and develop strategic plans for ethical AI implementation. By the end of this course, you will be equipped to use generative AI to drive innovation and create value in a responsible and impactful way.

You are required to have completed the following courses or have equivalent experience before taking this course:

      • Creating Business Value with AI
      • Exploring Good Old-Fashioned AI (GOFAI)
      • Leveraging Data for AI Solutions
      • Expanding AI Power and Value Through Neural Networks

By some estimates, 90% of the data that has ever existed has been created in the last two years. This is a staggering figure and has given rise to new challenges and opportunities in almost every industry: what kind of data do you need to collect to compete, and how can you make sense of it once you have collected it? As technology evolves and the volume of data increases, how can you make the best use of all this information? How can you use the data to help drive your decision-making? How can you make data work for you? How can you ensure your data accurately reflects the population in which you're interested?

In this course, you will determine the types of engineering and business questions you can answer, the kinds of problems you can solve, and the decisions you can make, all through using data analytics. You will explore best practices for collecting information so that you can make informed predictions, develop insights, and better inform organizational decision-making. You will see real-world examples that demonstrate how those tools work. Additionally, you will have a chance to apply some of the concepts to your own work. You will explore best practices for sampling and examine how different types of sampling are each suited for different situations. Finally, you will see real-world examples that demonstrate how those tools work and have a chance to practice sampling techniques in some case study scenarios.

Visualization is one of the most simple and effective ways to find patterns in data. These patterns include: What is the general range and shape of the data set? Are there any clusters of observations? Which variables correlate with each other? Are there any obvious outliers?

As your data set grows in terms of the number of data points and variables, however, it becomes increasingly difficult to visualize all this information at once. At most, you can plot data points on a three-dimensional axis and add further distinctions of size, color, shape, and so on. Yet this can easily become too busy and difficult to read. How, then, do we find patterns in really big data sets?

In this course, you will explore several powerful and commonly utilized techniques for distilling patterns from data. You will implement each of these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible for you in your own work.

You are required to have completed the following course or have equivalent experience before taking this course:

  • Understanding Data Analytics

When you have large groups of objects, it is often helpful to split them into meaningful groups or clusters. One example of this would be to identify different types of customers so that a company can more efficiently route their calls to a helpline. As a second example, suppose an automobile manufacturer wanted to segment their market to target the ads more carefully. One approach might be to take a database of recent car sales, including the social demographics associated with each customer, and segment the population purchasing each type of automobile into meaningful groups.

Specialized approaches exist if your data contains information that relates to time and geography. You can use this additional information to identify geographical and temporal hotspots. Hotspots are regions of high activity or a high value of a particular variable. These results can help you focus your attention on a particular region where a problem is occurring more than usual, such as the incidence of asthma in a large city. In both cluster and hotspot analysis, the results can help you discover new and interesting features, problems, and red flags regarding the data being analyzed.

In this course, you will explore several powerful and commonly utilized techniques for performing both cluster and hotspot analysis. You will implement these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible and applicable to your work.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Understanding Data Analytics
  • Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis

A story can play an important role in understanding data. It can help distill complex information into something manageable- something we can think about easily, relate to, and use to make decisions. For many problems that we encounter globally, however, a story that describes what already happened is not enough precision for the job we want to perform. Often, we would like to use available data to make numerically accurate predictions about what might happen in the future. This task requires the construction of mathematical models that are well suited to our real-world problems.

In this course, you will explore several types of statistical models used with data to make predictions. These models bring with them a whole batch of important concerns, such as estimation and validation, that make the entire process into both an art and a science. You will implement each of these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible for you in your own work.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Understanding Data Analytics
  • Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
  • Finding Patterns in Data Using Cluster and Hotspot Analysis

Supervised learning is a general term for any machine learning technique that attempts to discover the relationship between a data set and some associated labels for prediction. In regression, the labels are continuous numbers. This course will focus on classification, where the labels are taken from a finite set of numbers or characters. The prototypical and perhaps most well-known example of classification is image recognition. The goal is to take an image (represented by its pixel values) and determine what objects are in the image. Is it a dog? A grapefruit? A stop sign?

There are many practical classification tasks, such as determining whether an individual's financial history makes them high risk for a loan, whether there is a defect in a material based on some sensor readings, or whether a new email is spam or not. These problems share the same basic form and can be solved with many different types of mathematical, statistical, and probabilistic models developed by the machine learning community.

In this course, you will explore several powerful and commonly utilized techniques for supervised learning. You will implement each of these techniques using the free and open-source statistical programming language R with real-world data sets. The focus will be on making these methods accessible for you in your own work.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Understanding Data Analytics
  • Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
  • Finding Patterns in Data Using Cluster and Hotspot Analysis
  • Regression Analysis and Discrete Choice Models

Neural networks, a nonlinear supervised learning modeling tool, have become hugely popular within the last two decades because they have been successfully applied to a wide range of problems, including automatic language processing, image classification, object detection, speech recognition, and pattern recognition. They are mathematical models that are loosely built up based on an analogy to the interconnected neuron in the brain. They take in a vector or matrix of input data and output either a classification value or an approximation to a functional value. The beauty is that the relationships between the inputs and outputs can be highly non-linear and complex.

In this course, you will explore the mechanics of neural networks and the intricacies involved in fitting them to data for prediction. Using packages in the free and open-source statistical programming language R with real-world data sets, you will implement these techniques. The focus will be on making these methods accessible for you in your own work.

You are required to have completed the following courses or have equivalent experience before taking this course:

  • Understanding Data Analytics
  • Finding Patterns in Data Using Association Rules, PCA, and Factor Analysis
  • Finding Patterns in Data Using Cluster and Hotspot Analysis
  • Regression Analysis and Discrete Choice Models
  • Supervised Learning Techniques
This course begins by helping you reframe real-world problems in terms of supervised machine learning. Through understanding the “ingredients” of a machine learning problem, you will investigate how to implement, evaluate, and improve machine learning algorithms. Ultimately, you will implement the k-Nearest Neighbors (k-NN) algorithm to build a face recognition system. Tools like the NumPy Python library are introduced to assist in simplifying and improving Python code.

In this course, you will use the Maximum Likelihood Estimate (MLE) to approximate distributions from data. Using the Bayes Optimal Classifier, you will learn how the assumptions you make will impact your estimations. You will then learn to apply the Naive Bayes Assumption to estimate probabilities for problems that contain a high number of dimensions. Ultimately, you will apply this understanding to implement the Naive Bayes Classifier in order to build a name classification system.

The following course is required to be completed before taking this course:

  • Problem-Solving with Machine Learning

In this course, you are introduced to and implement the Perceptron algorithm, a linear classifier that was developed at Cornell in 1957. Through the exploration of linear and logistic regression, you will learn to estimate probabilities that remain true to the problem settings. By using gradient descent, we minimize loss functions. Ultimately, you will apply these skills to build a email spam classifier.

The following courses are required to be completed before taking this course:

  • Problem-Solving with Machine Learning
  • Estimating Probability Distributions

In this course, you will be introduced to the classification and regression trees (CART) algorithm. By implementing CART, you will build decision trees for a supervised classification problem. Next, you will explore how the hyperparameters of an algorithm can be adjusted and what impact they have on the accuracy of a predictive model. Through this exploration, you will practice selecting an appropriate model for a problem and dataset. You will then load a live dataset, select a model, and train a classifier to make predictions on that data.

The following courses are required to be completed before taking this course:

  • Problem-Solving with Machine Learning
  • Estimating Probability Distributions
  • Learning with Linear Classifiers

In this course, you will investigate the underlying mechanics of a machine learning algorithm's prediction accuracy by exploring the bias variance trade-off. You will identify the causes of prediction error by recognizing high bias and variance while learning techniques to reduce the negative impacts these errors have on learning models. Working with ensemble methods, you will implement techniques that improve the results of your predictive models, creating more reliable and efficient algorithms.

These courses are required to be completed prior to starting this course:

  • Problem-Solving with Machine Learning
  • Estimating Probability Distributions
  • Learning with Linear Classifiers
  • Decision Trees and Model Selection

In this course, you will explore support-vector machines and use them to find a maximum margin classifier. You will then construct a mental model for how loss functions and regularizers are used to minimize risk and improve generalization of a learning model. Through the use of feature expansion, you will extend the capabilities of linear classifiers to find non-linear classification boundaries. Finally, you will employ kernel machines to train algorithms that can learn in infinite dimensional feature spaces.

These courses are required to be completed prior to starting this course:

  • Problem-Solving with Machine Learning
  • Estimating Probability Distributions
  • Learning with Linear Classifiers
  • Decision Trees and Model Selection
  • Debugging and Improving Machine Learning Models

In this course, you will investigate the fundamental components of machine learning that are used to build a neural network. You will then construct a neural network and train it on a simple data set to make predictions on new data. We then look at how a neural network can be adapted for image data by exploring convolutional networks. You will have the opportunity to explore a simple implementation of a convolutional neural network written in PyTorch, a deep learning platform. Finally, you will yet again adapt neural networks, this time for sequential data. Using a deep averaging network, you will implement a neural sequence model that analyzes product reviews to determine consumer sentiment.

These courses are required to be completed prior to starting this course:

  • Problem-Solving with Machine Learning
  • Estimating Probability Distributions
  • Learning with Linear Classifiers
  • Decision Trees and Model Selection
  • Debugging and Improving Machine Learning Models
  • Learning with Kernel Machines

To perform basic computations in the Machine Learning certificate program, you need the ability to solve elementary linear algebra problems in two dimensions. In this course, you will execute mathematical computations on vectors and measure the distance from a vector to a line. This course will provide you with the theory and activities to start building the linear algebra foundation needed to be successful in your Machine Learning courses.

This optional self-paced course supports the required linear algebra in the Machine Learning certificate. If you are already comfortable with the computations from the pretest, we recommend that you take the final assessment to confirm your readiness.

This Machine Learning certificate program requires you to think and solve problems in multiple dimensions. In this course, you will learn to solve linear algebra problems in three or more dimensions and perform computations with matrices.You will perform computations that focus on solving problems in high dimension; that is, multiple dimensions. This course will provide you with the theory and activities to solidify the linear algebra foundation needed to be successful in your Machine Learning courses.

This optional self-paced course supports the required linear algebra in the Machine Learning certificate. If you are already comfortable with the computations from the pretest, we recommend that you take the final assessment to confirm your readiness.

Symposium sessions feature three days of live, highly interactive virtual Zoom sessions that will explore today’s most pressing topics. The AI Symposium offers you a unique opportunity to engage in real-time conversations with peers and experts from the Cornell community and beyond. Using the context of your own experiences, you will take part in reflections and small-group discussions to build on the skills and knowledge you have gained from your courses.

Join us for the next Symposium, in which we’ll share experiences from across the industry, inspiring real-time conversations about best practices, innovation, and the future of AI. You will support your coursework by applying your knowledge and experiences to some of the most pressing topics and trends in the field. By participating in relevant and engaging discussions, you will discover a variety of perspectives and build connections with your fellow participants from across a variety of industries.

Upcoming Symposium: March 18, 19 and 20, 2025 11AM – 1PM ET

    All sessions are held on Zoom.

    Future dates are subject to change. You may participate in as many sessions as you wish. Attending Symposium sessions is not required to successfully complete any certificate program. Once enrolled in your courses, you will receive information about upcoming events. Accessibility accommodations will be available upon request.

    In this course, you will explore the foundational vocabulary of natural language processing (NLP) — and start writing code right away — by finding patterns in strings using both simple functions and regular expressions. This will prepare you for an important component of NLP work, which is preprocessing text to reduce the size of the vocabulary being analyzed: The fewer total words that need to be analyzed, the more computationally efficient your work will be. You will then tag sentences so that you will be able to relate keywords to one another. You will also gain extensive hands-on experience writing Python, first by practicing on individual sentences then working up to a larger body of text. Overall, your understanding of and skill in NLP with Python will support you as you continue through your career and meet your goals in this area and beyond.

    If you want to compare two large bodies of text with each other, you can do that by making comparisons with the text itself: Turn the text into tokens then compare the overlap in tokens. Sometimes, however, you don't just want to know that two texts are different (a binary comparison), but you want to know how different, which is a fuzzy comparison. In this course, you will transform text into numeric vectors, which allows us to perform arithmetic operations on textual information to calculate similarity. This is a classical natural language processing (NLP) technique, and it begins by creating different kinds of vectors. You will create both sparse and dense vectors, and you will compare vectors of different sizes to see how information is captured. Finally, you will measure similarity among document vectors, which is the real power of turning text into vectors. The ability to determine how similar two or more documents are is a common use of NLP, and you will practice this technique through hands-on exercises and projects.

    You are required to have completed the following course or have equivalent experience before taking this course:

    • Natural Language Processing Fundamentals

    In this course, you will start to use machine learning methods to further your exploration of document term matrices (DTM). You will use a DTM to create train and test sets with the scikit-learn package in Python — an important first step in categorizing different documents. You will also examine different models, determining how to select the most appropriate model for your particular natural language processing task. Finally, after you have chosen a model, trained it, and tested it, you will work with several evaluation metrics to measure how well your model performed. The technical skills and evaluation processes you study in the course will provide valuable experience for the workplace and beyond.

    You are required to have completed the following courses or have equivalent experience before taking this course:

    • Natural Language Processing Fundamentals
    • Transforming Text Into Numeric Vectors

    Can a computer tell the difference between an article on “jaguar” the animal and “Jaguar” the car? It can if we teach it how. In this course, you will extract key phrases or words from a document, which is a key step in the process of text summarization. Part of what makes natural language processing (NLP) so powerful is that it processes text at scale, when a human would simply take too long to perform the same task given the sheer number of text documents to be read and processed. A classic use of NLP, then, is to summarize long documents, whether they are articles or books, in order to create a more easily readable abstract, or summary.

    Extracting keywords or key phrases is a first step in this direction, which is where you will start in this course. Once you train a computer what the most important words in a document might be, you have to train it to identify the most important sentences. This is the second step in extracting information from a document to help create an abstract, and you will perform this step on larger text documents as well. Finally, you will calculate and interpret similarity metrics to compute the degree of similarity among documents that are possibly related to one another. The techniques you use throughout this course will prove useful in specific situations at work and beyond as you support your team or achieve your personal goals.

    You are required to have completed the following courses or have equivalent experience before taking this course:

    • Natural Language Processing Fundamentals
    • Transforming Text Into Numeric Vectors
    • Classifying Documents With Supervised Machine Learning

    In this course, you will focus on measuring distance — the dissimilarity of various documents. The goal is to discover how alike or unlike various groups of text documents are to one another. At scale, this is a problem you might encounter if you need to group thousands of products together purely by using their product description or if you would like to recommend a movie to someone based on whether they liked a different movie. You will work with several different data sets and use both hierarchical and k-means clustering to create clusters, and you will practice with several distance measures to analyze document similarity. Finally, you will create visualizations that help to convey similarity in powerful ways so stakeholders can easily understand the key takeaways of any clustering or distance measure that you create.

    You are required to have completed the following courses or have equivalent experience before taking this course:

    • Natural Language Processing Fundamentals
    • Transforming Text Into Numeric Vectors
    • Classifying Documents With Supervised Machine Learning
    • Topic Modeling With Unsupervised Machine Learning

    We have all been misunderstood when sending a text message or email, as tone often does not translate well in written communication. Similarly, computers can have a hard time discerning the meaning of words if they are being used sarcastically, such as when we say “Great weather” when it's raining. If you are automatically processing reviews of your product, a negative review will have many of the same key words as a positive one, so you will need to be able to train a model to distinguish between a good review and a bad review. This is where semantic and sentiment analysis come in.

    In this course, you will examine many kinds of semantic relationships that words can have (such as hypernyms, hyponyms, or meronyms), which go a long way toward extracting the meaning of documents at scale. You will also implement named entity recognition to identify proper nouns within a document and use several techniques to determine the sentiment of text: Is the tone positive or negative? These invaluable skills can easily turn the tide in a difficult project for your team at work or on the path toward achieving your personal goals.

    You are required to have completed the following courses or have equivalent experience before taking this course:

    • Natural Language Processing Fundamentals
    • Transforming Text Into Numeric Vectors
    • Classifying Documents With Supervised Machine Learning
    • Topic Modeling With Unsupervised Machine Learning
    • Clustering Documents With Unsupervised Machine Learning

    As new digital technologies become embedded in business operations, there are more questions than answers for many leaders. How can you harness the power of digital technologies to help your business thrive without falling into pitfalls along the way?

    In this course, you will gain a foundation in a number of technological advances. You will start by discussing ways that technologies can improve your personal and professional life, including ways to produce work more smoothly, quickly, and effectively. You will also explore ways that technologies fail to deliver on promised results and access methods to prepare your team to benefit from digital technologies. By the end of this course, you will understand ways to harness the opportunities and avoid the pitfalls of digital technologies.

    Now more than ever, businesses are recognizing the common pitfalls of digital technologies. Yet leaders who adopt and implement these technologies without a critical eye will likely be held back and have trouble thriving in the strategic marketplace. It is crucial to strike a balance between adopting new technologies for innovation and strategically assessing and building new technologies into your organizational strategy. But how can you better understand the options presented by these new technologies?

    In this course, you will examine a variety of new technologies and the roles they could play in your industry and community. You will also assess the usefulness and suitability of these digital technologies for your own organization. Finally, you will discuss the large negative externalities that these innovations place on society. By the end of this course, you will better understand digital technologies, allowing you to strategically adopt them with purpose.

    In a competitive business marketplace, being on the cutting-edge sets businesses apart for consumers, stakeholders, and workers. How can technology innovation improve business operations, and how can you make this happen in your organization?

    In this course, you will explore ways to leverage new advances in digital technology to reinvent how things get done in your industry. To do so, you will identify opportunities for improving processes and work in your organization or society using digital technologies. You will also assess key metrics for work and processes in your organization, supporting your strategy and helping you communicate your vision and goals. Finally, you will explore the idea of translating or expanding your process improvement ideas into a consulting business or standalone product for companies or individuals. By the end of this course, you will have the necessary foundation to strategize a vision for the future of work powered by digital technologies at your organization and beyond.

    With so much variety across industries and strategies, it can be difficult to ensure that your business model will support the growth and success of your business. When adding innovative digital technologies to the mix, the situation can become even more complicated. How can you create an informed, successful strategy?

    In this course, you will explore the ways that new technologies can enable innovative business and operating models for organizations like yours. You will analyze the existing structures in your organization and evaluate ways that technology can affect key factors such as the optimal scope, the revenue model, the timing of the processes, and the players involved. Finally, you will identify and pitch several opportunities for reinventing business models, gaining practical experience to bring back to your organization.

    Having a great idea for a new digital product is just the beginning. Ensuring there is an effective culture behind the team bringing the idea to life is key to long-term success.

    In this course, you will discover how to create a digital builder culture. You will first identify common pitfalls and strategize on how to avoid them. You will then explore the values that make leaders and managers successful in this area, including a focus on exploration and fast, intelligent experimentation. Finally, you will establish a prioritization scheme to support you and your team as you sequence your learnings about any unknowns affecting your product. By the end of this course, you will have the foundation necessary to move your organization from a know-it-all culture to a learn-it-all culture.

    Even though all companies work with data, not all leaders understand how to harness the power of the right data for the right situation. Whether you are assessing operations or discussing data-based evidence with stakeholders, business leaders must be prepared to make informed decisions that support the success of their businesses.

    In this course, you will discover how to harness the value of accurate data for your organization. You will examine how to use data to both monitor and improve existing business operations and test and implement new initiatives. You will also identify ways that data can support decision making in the C-suite at your organization and beyond. Importantly, you will identify several societal concerns in the use of data in organizations. Finally, you will practice interpreting data-based evidence for a business, gaining practical skills to bring back to your organization.

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