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74 courses for 'Artificial Intelligence'
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AI Ethics and Accountability 2h 31m • 54 Lessons • Intermediate This course on AI Ethics and Accountability will provide a thorough overview of ethical considerations and principles around accountability in AI systems. You will learn about principles that apply only to AI systems, like explainability, as well as broader principles, like the principle of equal access to technology, services, and opportunities. You will learn about the intersection of ethical AI and human rights, governance, and the legal system.
AI Ethics and Accountability: Modern Artificial Intelligence Development 24m • 7 Lessons • Intermediate These lessons on Modern Artificial Intelligence Development will cover the history and background of artificial intelligence and the fundamental aspects and concepts of artificial intelligence, including how AI works as a decision system. We’ll also explore how AI is used in everyday life, machine learning as an automated system that learns from information, and the unique considerations about big data used in AI systems.
AI Ethics and Accountability: Unique Challenges of AI and Machine Learning 16m • 5 Lessons • Intermediate In these lessons on AI and Machine Learning’s Unique Challenges, you’ll explore the types of machine learning, including supervised, which uses labeled data to make predictions; unsupervised, a series of techniques to understand the structure of data without an outcome variable; and reinforcement learning, one of the oldest styles of machine learning that uses a goal-based method of learning. You’ll also understand ethical considerations around neural networks and generative AI.
AI Ethics and Accountability: Introduction to Ethical Artificial Intelligence 21m • 7 Lessons • Intermediate In these lessons, you’ll learn about the governance landscape around AI. You will learn about AI alignment with human values and external impacts that are indirectly related to the creation and deployment of any AI system, such as environmental impacts. You will also learn about AI systems that do deliberate harm and dark patterns, deceptive design patterns that are used to influence individuals to perform an action that may not be in their best interest.
AI Ethics and Accountability: Accountability in AI 8m • 4 Lessons • Intermediate Accountability is a critical piece of AI ethics, and it is relevant in all stages of the lifecycle of an AI system, including during design, after deployment, as well as when any harm occurs because of the system. In these lessons on Accountability in AI, you’ll learn how AI systems can be assessed using principles of accountability, along with regulatory reforms and legal frameworks and their intersection with accountability.
AI Ethics and Accountability: Privacy, Safety, and Security in Artificial Intelligence 13m • 6 Lessons • Intermediate These lessons on Privacy, Safety, and Security in Artificial Intelligence will discuss General Data Protection Regulation, consent, data portability, the ability to restrict processing, right to rectification, and right to erasure. You will learn important concepts involving reliability and safety and discover how monitoring can detect potential risks and quickly mitigate harm if the system is not functioning as desired. These lessons will also cover how traceability is important to safety.
AI Ethics and Accountability: Transparency and Explainability in Artificial Intelligence 14m • 5 Lessons • Intermediate In these lessons on Transparency and Explainability in Artificial Intelligence, you’ll learn about explainability and interpretability and its intersection with the concepts of black box and glass box AI. You’ll review the advantages and disadvantages of open-source software in the development of an AI system. You’ll also learn about the principles of notification of decision and interaction, and their importance with systems using natural language or facial recognition.
AI Ethics and Accountability: Bias, Inclusivity, and Fairness in Artificial Intelligence 8m • 4 Lessons • Intermediate These lessons on Bias, Inclusivity, and Fairness in Artificial Intelligence will explore why bias is one of the most discussed concepts involving AI ethics and why preventing bias should be a part of the governance of every AI system. Additionally, these lessons will review the concepts of fairness, representativeness, and the principle of inclusive impact.
AI Ethics and Accountability: Professional Responsibility and Human Control of AI Technology 8m • 5 Lessons • Intermediate In these lessons on Professional Responsibility and Human Control of AI, you will learn about the broad principle of human control of technology and how it intersects with AI system development. You will also see how the designers’ responsibility is linked to AI system governance and multistakeholder collaboration to enable valuable feedback about the system design and its impact on various stakeholder groups.
AI Ethics and Accountability: Artificial Intelligence and Sociotechnical Systems 17m • 6 Lessons • Intermediate In these lessons on AI and Sociotechnical System Impact on Society and Human Values, you’ll learn how human rights may be impacted by AI and understand the intersection of civil rights and civil liberties with AI. You’ll learn about the principle of equal access to technology, services, and opportunities and how this broad principle will change as AI develops further.
AI Ethics and Accountability: Critical Artificial Intelligence Incidents 14m • 3 Lessons • Intermediate These lessons on Critical Artificial Intelligence Incidents looks at significant AI incidents through a series of real-world examples of negative outcomes in AI, how they were resolved, and how they could have been prevented. Specifically, you’ll work through case studies focusing on issues of identity and discrimination, offensive content, and legal implications.
Artificial Intelligence Basics 1h • 17 Lessons • Beginner In this course on Artificial Intelligence (AI) Basics, you’ll explore what AI is, how it is changing the way people live and work, its current and potential real world applications, the key attributes of AI, and the ethical challenges it presents.
What is Artificial Intelligence? 13m • 4 Lessons • Beginner Welcome to this course on What Is Artificial Intelligence? In these lessons, you’ll explore what artificial intelligence means, the differences between natural and artificial intelligence, and learn how AI allows humans to create intelligent systems such as chatbots and smart cars that can think just like a human brain.
AI Theory 15m • 4 Lessons • Beginner In these lessons on AI Theory, you’ll be introduced to theoretical concepts related to artificial intelligence and explore those theories along with AI testing.
AI and Other Technologies 15m • 5 Lessons • Beginner In this course on AI and Other Technologies, you’ll explore artificial intelligence and the two types of machine learning solutions, as well as AI and the use of robotics.
AI Trends and Uses 15m • 4 Lessons • Beginner These lessons on AI Trends and Uses will cover how artificial intelligence is currently being used in the world and its potential uses in the future from the storing of personal information and automated coding to diagnostics and resource utilization.
Algorithms in Applied Machine Learning 1h 16m • 29 Lessons • Intermediate In this course, we’ll cover the types of machine learning algorithms and what problems they can be applied to, including supervised machine learning and unsupervised machine learning. You’ll discover how to assess the effectiveness of a machine learning model. We’ll also address best practices to make decisions between data-collection methods, preprocessing steps, and algorithms.
Types of Machine Learning Algorithms 8m • 4 Lessons • Intermediate These lessons will provide an overview of machine learning. You’ll also explore the different types of machine learning. We’ll review the importance of data collection and provide a discussion of the typical machine-learning pipeline.
Supervised Machine Learning Algorithms 21m • 6 Lessons • Intermediate These lessons will discuss the most important applications of supervised machine learning including classification, prediction, and forecasting. You’ll also explore artificial neural networks, one of the most common methods used for supervised machine learning.
Semi-Supervised and Unsupervised Machine Learning Algorithms 12m • 5 Lessons • Intermediate Unsupervised machine learning allows a practitioner to surface emergent patterns in data and can be used in applications from customer segmentation to defining the optimal genre for a new radio station. These lessons will review clustering and dimension reduction, as well as how unsupervised machine learning can be used in feature engineering. You’ll also explore semi-supervised machine learning and its applications.
Boosting vs. Bagging in Machine Learning Algorithms 4m • 3 Lessons • Intermediate These lessons will cover two important methods to help practitioners build effective machine-learning models: bagging and boosting. You’ll also discover the applications of both. In addition, we’ll explore the advantages of dealing with common data problems.
Real-World Applications of Machine Learning Algorithms 11m • 3 Lessons • Intermediate These lessons will cover several real-world applications of machine learning. You’ll also discover how supervised machine learning can be used to model language and predict whether or not an email is spam, using unsupervised learning to create a customer segmentation, and neural-network based image classification.
Choosing the Most Appropriate Algorithm 15m • 6 Lessons • Intermediate In these lessons, we’ll address trade-offs between a variety of factors to build an effective model, including performance and ethical considerations. You will discover common model validation methods, model quality metrics and common challenges. We’ll also cover using pre-trained models.
Machine Learning 55m • 20 Lessons • Beginner In this course, you’ll learn the basics of machine learning. You’ll be introduced to machine learning and some of the related concepts and topics. You’ll understand the various types and categories of machine learning.
Introducing Machine Learning 13m • 6 Lessons • Beginner This chapter will introduce machine learning and its two main categories—supervised and unsupervised. You’ll learn about what they are and how they are used. You’ll also learn about how machine learning is related to artificial intelligence.
Unsupervised Machine Learning Methods 14m • 3 Lessons • Beginner This chapter will introduce unsupervised machine learning concepts and techniques. It will cover Gaussian mixture models. It will also cover various types of clustering. This chapter will also introduce the concept of manifold learning and the difference between various types.
Supervised Machine Learning Methods 15m • 4 Lessons • Beginner In this chapter you’ll learn about supervised learning concepts and techniques. It will cover linear and logistic regression. You will learn about support vector machines and their uses. This chapter also covers decision trees, random forests, ensemble learning and how they are all related. Neural networks are also discussed in this chapter.
Machine Learning Challenges and Applications 10m • 5 Lessons • Beginner This chapter will cover challenges and applications in the field of machine learning. It will cover model selection for machine learning, as well as ethical and other key considerations. Specific machine learning challenges—as well as some of its more popular applications—will also be covered.
Classification Modeling 1h 56m • 32 Lessons • Intermediate
Approved by
International Institute of Business Analysis (IIBA®)
In this course, you will learn the basics of classification modeling using machine learning. You will examine the typical classification model pipeline and the applicable problems classification can be applied to. By the end of this course, you should be able to design, analyze, validate, and implement a classification model, as well as understand the implications of its use.
Machine Learning and Classification 26m • 6 Lessons • Intermediate These lessons will introduce classification modeling and its problem space within supervised machine learning. This will include learning and recognizing the difference between classification and prediction. You will be introduced to the typical classification model pipeline, and learn practical tips on how to manage data collection and preprocessing for a classification model.
Classification Problems 27m • 6 Lessons • Intermediate These lessons will explain the typical model-building process and introduce binary and multi-class classification problems. You will learn how to use feature engineering to figure out the best data inputs for your classification models. You will learn to assess the quality of a classification model, and how to validate a classification model before its deployment.
Classification Algorithms/Models 34m • 8 Lessons • Intermediate In these lessons, you will learn the most commonly used classification algorithms and what problems they typically apply to. You will also learn their relative strengths and weaknesses, and the trade-offs between them. This chapter will also cover an introduction to neural networks and their use in common classification problems.
Challenges 16m • 6 Lessons • Intermediate In these lessons, you will learn how to choose the right model by considering the trade-offs between them. You will also learn how to deal with common challenges faced in classification models, including bias, fairness, and data collection problems. Finally, you will learn about emerging challenges in classification modeling, including adversarial attacks.
Classification Modeling: Applications 10m • 4 Lessons • Intermediate These lessons will take you through the various applications of classification modeling in real-life scenarios, including medicine, customer behavior, and image classification. You will also explore what is on the horizon with classification modeling and what the future possibilities are.
Neural Networks 1h 25m • 24 Lessons • Beginner In this course, you’ll learn the basics of neural networks. You’ll receive an introduction to neural networks and closely related concepts and topics. You’ll be able to understand the various design aspects of neural networks and how to go about making selections for them.
Introducing Neural Networks 17m • 5 Lessons • Beginner This chapter will introduce neural networks. You’ll learn about what they are and how they are used. You’ll also learn about the most basic type of neural network. Inputs and outputs will be discussed and explained in this chapter. This chapter will also explain the difference between classification and regression for neural networks.
Neural Network Design 29m • 7 Lessons • Beginner This chapter will introduce neural network design concepts. It will cover the importance of data in both the performance and selection of neural networks. It will also cover what activation functions, loss functions, and optimizers are as well as how to select which ones to use. This chapter will also introduce you to training, testing, and validation for neural networks.
Neural Networks Implementation 28m • 6 Lessons • Beginner In this chapter you’ll learn about neural network implementation. Various languages capable of being used to implement neural networks will be discussed. You will learn why python is recommended as the programming language to start with for neural network implementation. This chapter also covers various frameworks for implementing neural networks in python.
Practical Applications for Neural Networks 7m • 4 Lessons • Beginner This chapter will cover practical uses and applications for neural networks. It will cover how neural networks can be used for cybersecurity, automation, anomaly detection, and predictive systems.
Clustering in Machine Learning 2h 39m • 37 Lessons • Intermediate
Approved by
International Institute of Business Analysis (IIBA®)
In this course, you’ll learn the basics of a variety of areas of machine learning, including clustering, anomaly detection, and association modeling. You’ll understand the most common method of clustering, k-means, and how it can be applied to survey data in order to create a robust customer segmentation, including the creation of personas, and a typing tool. By the end of this course, you’ll know how to perform a number of different clustering and analysis techniques.
Analyzing Data Clusters 36m • 7 Lessons • Intermediate These lessons will introduce clustering and its problem space within unsupervised machine learning. You’ll be introduced to the pairings of clustering with other modeling steps like dimension reduction, feature engineering, and prediction.
K-means Clustering 52m • 8 Lessons • Intermediate These lessons introduce the most common style of clustering, k-means, and work through a detailed use case of a customer segmentation. You’ll learn how to choose input variables, select the number of clusters, and interpret the centroids. You’ll learn how to assess the quality of a cluster analysis model, and how to address computational challenges that come with clustering. Finally, we’ll examine how to create personas and methods for classifying new data.
Clustering Algorithms 24m • 6 Lessons • Intermediate In these lessons, you’ll learn the most commonly used clustering algorithms and what problems they typically apply to. We’ll cover and assess their relative strengths and weaknesses, and the tradeoffs between them.
Machine Learning: Clustering Association Rules 16m • 4 Lessons • Intermediate In these lessons, you’ll learn about association rules and how they can apply to retail stores, e-commerce websites and media platforms. In turn, we’ll explore the three most important measures--support, confidence, and lift--as well as how to interpret each.
Anomalies 16m • 5 Lessons • Intermediate These lessons will take you through various methods of anomaly detection and their real-world applications. You’ll learn how various styles of machine learning can be used in anomaly detection, and which specific clustering methods work for anomaly detection. In turn, we’ll explore isolation forests, and how they might apply to the real-life application of fraud detection.
Machine Learning: Clustering Uses/Applications 10m • 5 Lessons • Intermediate These lessons will take you through various use cases and real-world applications of the methods we’ve covered in the course. Use cases will cover various scenarios, including medicine, retail, and marketing scenarios.
OpenCV Python Development 3h 29m • 46 Lessons • Intermediate In this course on OpenCV Python Development, you will learn how to create Python development projects using OpenCV to detect objects and features. We will explore data structures for images and videos in OpenCV, processing images and videos, and detecting objects and features. By the end of this course, you will be able to construct and deconstruct images and videos in OpenCV and detect objects and features of interest.
OpenCV Python Development: Getting Started 13m • 4 Lessons • Intermediate In these lessons, you will learn how to create a Python development project with a virtual environment to install OpenCV. We will explore the Python installation process, virtual environments, and installing OpenCV. By the end of these lessons, you will be able to install Python and OpenCV.
OpenCV Python Development: Data Types and Structures 36m • 8 Lessons • Intermediate In these lessons on OpenCV Python Development, you will learn how to construct images and videos in OpenCV using NumPy. We will explore NumPy arrays and their operations and structures. By the end of these lessons, you will be able to construct images and videos in OpenCV.
OpenCV Python Development: Working with Images 59m • 12 Lessons • Intermediate In these lessons on OpenCV Python Development, you will learn how to read and process images and videos in OpenCV. We will explore color channels, pixels, and geometric and algebraic transformations of images in OpenCV. By the end of these lessons, you will be able to construct and deconstruct images and videos in OpenCV.
OpenCV Python Development: Detecting Objects 53m • 11 Lessons • Intermediate In these lessons, you will learn how to detect objects in images and video frames using OpenCV. We will explore binary and grayscale thresholding techniques, edge detection, and contour analysis. By the end of these lessons, you will be able to build a skin detector and describe the objects detected in an image.
OpenCV Python Development: Detecting Faces and Features 44m • 9 Lessons • Intermediate In these lessons on face and feature detection, you will learn how to detect particular objects in images and video frames in OpenCV. We will explore template matching, corner detection, more advanced feature detection and matching, and classical and machine learning approaches. By the end of these lessons, you will be able to detect faces and a variety of objects by their features in OpenCV.
TensorFlow Intermediate 2h 20m • 27 Lessons • Intermediate In this course on TensorFlow Intermediate, you will learn how to build advanced machine-learning models using TensorFlow. We will explore sequences and time series forecasting, recurrent neural networks, natural language processing, and recommender systems. We’ll also begin an intro to TensorFlow Lite. By the end of this course, you will be able to confidently create and deploy machine-learning models using TensorFlow for a variety of applications.
TensorFlow Intermediate: Sequences and Time Series Forecasting 29m • 5 Lessons • Intermediate In this course, you will explore the world of time series forecasting using TensorFlow, a powerful open-source library for deep learning. Discover the techniques and tools to preprocess and prepare time series data, build sequential models, and make accurate predictions. From understanding the importance of data preprocessing to implementing state-of-the-art models, this course equips you with the skills to excel in the field of time series forecasting.
TensorFlow Intermediate: Recurrent Neural Networks 27m • 5 Lessons • Intermediate In this intermediate-level TensorFlow course, we’ll cover deep learning and the applications of recurrent neural networks (RNNs). You’ll gain a comprehensive understanding of TensorFlow's capabilities in handling sequential data. You’ll also preprocess time series and text data, build and train RNN models, and unlock the potential of deep learning for various domains.
TensorFlow Intermediate: Natural Language Processing 27m • 5 Lessons • Intermediate In this intermediate-level TensorFlow course, we’ll explore the applications of natural language processing (NLP) and learn to build text classification models, generate language using recurrent neural networks, and apply advanced NLP techniques. We’ll learn about TensorFlow's capabilities for NLP and unlock the potential of deep learning in processing textual data.
TensorFlow Intermediate: Recommender Systems 27m • 5 Lessons • Intermediate In this course on TensorFlow Intermediate: Recommender Systems, you will learn how to build effective recommender systems using collaborative filtering and content-based filtering techniques. By the end of this course, you will be equipped with the knowledge and skills to create personalized and accurate recommendations using TensorFlow.
TensorFlow Intermediate: Intro to TensorFlow Lite 27m • 5 Lessons • Intermediate In this intermediate-level course, you will learn about TensorFlow Lite and understand its capabilities for deep learning on resource-constrained devices. You’ll explore model conversion, optimization techniques, and deployment strategies to harness the power of deep learning on mobile and edge platforms.
TensorFlow Beginner 1h 49m • 22 Lessons • Beginner Explore real-world applications of machine learning, discover the different add-ons available for TensorFlow, and learn how to use transfer learning to enhance the performance of your machine learning models.
TensorFlow Beginner: What is TensorFlow? 15m • 3 Lessons • Beginner In these lessons, you will learn about the most common training/validation split, the preprocessing of images in TensorFlow, and the use of shuffled data in training. Additionally, you will explore the flatten and dense layers in TensorFlow, and learn about the model class used for deep learning.
TensorFlow Beginner: Deep Learning with TensorFlow 20m • 3 Lessons • Beginner In these lessons, you will learn about the different deep learning methods used in the field, including Convolutional Neural Networks (CNNs) and Multilayer Perceptrons (MLPs). You will also explore Autoencoders, and their applications in learning features in images.
TensorFlow Beginner: Neural Networks 19m • 3 Lessons • Beginner In these lessons, you will learn about the foundational concepts of neural networks, including Artificial Neural Networks (ANNs) and the Least Squares method. You will also explore the different layers used in Convolutional Neural Networks (CNNs) and their primary uses in image processing.
TensorFlow Beginner: Transfer Learning 20m • 4 Lessons • Beginner In these lessons, you will learn about transfer learning and how to use pretrained models to improve the performance of your machine learning projects. You will explore the different benefits of transfer learning and how to fine-tune your model using validation datasets and confusion matrices.
TensorFlow Beginner: Add-Ons for TensorFlow 13m • 3 Lessons • Beginner In these lessons, you’ll explore the different add-ons available for TensorFlow that can enhance the performance and capabilities of your machine learning projects. Discover tools such as TensorFlow Hub, TensorFlow Probability API, and TensorFlow Lite, and learn how to use them effectively to improve your machine learning models.
TensorFlow Beginner: Real World Applications 18m • 4 Lessons • Beginner In these lessons, you’ll learn how to apply machine learning to tackle real-world challenges in different domains, such as finance and biology. We’ll cover how to use classification models, Pandas for data processing, and Seaborn for data visualization. You’ll also learn to create training and testing datasets using train_test_split() function and evaluate model performance using accuracy and confusion matrices.
Python for Data Science Advanced 3h 32m • 29 Lessons • Advanced Equip yourself for the evolving world of data science and analytics with KnowledgeCity’s advanced course, Python for Data Science. In this course, we’ll explore machine learning fundamentals and the history of various Python modules. We’ll discuss supervised learning from linear regression to support vector machines, as well as unsupervised learning, including principal component analysis and K-means clustering.
Python for Data Science Advanced: Fundamentals of Machine Learning 59m • 9 Lessons • Advanced In this KnowledgeCity course on Python for Data Science Advanced: Fundamentals of Machine Learning, you’ll learn about the fundamentals of machine learning, its history, and the different types. You’ll also learn about the machine learning process and data properties. Then we’ll cover some of Python's popular machine learning modules like SciKit, PyTorch, and TensorFlow.
Python for Data Science Advanced: Supervised Learning 49m • 6 Lessons • Advanced In this KnowledgeCity course on Python for Data Science Advanced: Supervised Learning, we’ll explore different types of supervised learning algorithms, including linear and logistic regression, ridge and lasso regression, polynomial and Bayesian regression, and linear and nonlinear support vectors. We’ll also discuss linear support vector machines, or SVMs, and supervised machine learning using SVMs, a popular technique for classification and regression tasks.
Python for Data Science Advanced: Unsupervised Learning 34m • 4 Lessons • Advanced In this KnowledgeCity course on Python for Data Science Advanced: Unsupervised Learning, you’ll learn about unsupervised machine learning by studying principals, component analysis, and clustering techniques. We’ll go over principal component analysis, and how to cluster data using K-means in Python. You’ll also learn about the DBSCAN unsupervised machine learning algorithm, and we’ll spend time covering affinity propagation in the realm of machine learning.
Python for Data Science Advanced: Reinforcement Learning 29m • 3 Lessons • Advanced In this KnowledgeCity course on Python for Data Science Advanced: Reinforcement Learning, you’ll learn about reinforcement learning techniques that apply to machine learning. We’ll introduce you to OpenAI Gym, a machine learning subfield that focuses on getting an agent to learn optimal decision-making strategies by interacting with an environment. The agent takes actions in a specific state to achieve a goal while receiving feedback in the form of rewards. We’ll also explore Q-Learning,
Python for Data Science Advanced: More Machine Learning Libraries 38m • 5 Lessons • Advanced In this KnowledgeCity course on Python for Data Science Advanced: More Machine Learning Libraries, we’ll explore a variety of machine learning algorithms. We’ll look at Keras, a high-level neural network API written in Python. You’ll also learn about CatBoost, which provides a gradient boosting framework. Then we’ll delve into convolutional neural networks, an open-source numerical computation library designed for deep learning and other machine learning tasks.
Understanding Generative Artificial Intelligence and Cybersecurity 13m • 5 Lessons • Essential In this Understanding Generative Artificial Intelligence and Cybersecurity course, you’ll explore how generative AI is reshaping cybersecurity. You’ll learn the fundamentals of generative AI, how it differs from traditional AI, and its applications across industries. We’ll cover the CIA triad and AI's role in creating threats and enhancing security. By the end of this course, you’ll be equipped to adapt your cybersecurity strategies to address emerging AI-driven risks.
Using Generative AI in Cybersecurity 12m • 5 Lessons • Essential In this Using Generative AI in Cybersecurity course, you’ll learn how to integrate AI into your security frameworks to predict and counter advanced cyber threats. You’ll explore upgrading your infrastructure, training AI models on diverse threats, and implementing AI-driven security measures like zero-day exploit detection and enhanced phishing prevention. We’ll also cover the ethical responsibilities of deploying AI, ensuring privacy, fairness, and transparency in cybersecurity practices.
Responding to Generative AI Cybersecurity Threats 13m • 5 Lessons • Essential In this Responding to Generative AI Cybersecurity Threats course, you'll learn how to identify and manage the risks posed by generative AI. We’ll explore how AI can create phishing attacks, deepfakes, and automated exploits, while exploring machine learning’s role in combating these threats. By the end of this course, you’ll be equipped to strengthen your company’s defenses using AI-driven security strategies.
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