// 54 courses indexed
The course directory
Every course we track across 10+ providers, in one searchable index. Want a shortlist picked for your goals? Run a free skill gap analysis.
Building and Scaling ML Pipelines
This course provides an intermediate-level exploration of MLOps, focusing on how machine learning systems are scaled, productionized, and managed across feature engineering, training, orchestration, serving, and deployment. You will examine how modern ML solutions use feature stores, Kubernetes, Kubeflow, distributed training, advanced serving frameworks, progressive release strategies, and inference optimization techniques. Through hands-on demonstrations and practical exercises, you will gain experience building reliable and scalable workflows with industry-standard technologies such as Fe
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SPSS: Apply & Evaluate Cluster Analysis Techniques
Learn how to apply and evaluate cluster analysis using SPSS in this hands-on introduction to unsupervised machine learning. This course provides a practical foundation in clustering techniques, helping you understand how to group similar data, interpret clustering results, and make informed decisions in data segmentation tasks. Designed for learners who want to build analytical skills with SPSS, the course combines core concepts with guided practice. You'll begin by exploring the principles of cluster analysis, comparing hierarchical clustering, K-means clustering, and their applications. You
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Problem-Dependent Resampling Techniques
This course is designed for data scientists, machine learning practitioners, and researchers who want to understand how resampling techniques must be adapted to the structure of the problem at hand. You will learn how standard validation methods such as cross-validation can fail when applied blindly, and how to design problem-dependent resampling strategies for spatial data, pair-input data, and other dependent observation structures. The course also covers spatial cross-validation, dependency-aware evaluation design, and statistical testing methods to assess whether performance estimates are
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Machine Learning Operations with Vertex AI: Model Evaluation
This course equips machine learning practitioners with the essential tools, techniques, and best practices for evaluating both generative and predictive AI models. Model evaluation is a critical discipline for ensuring that ML systems deliver reliable, accurate, and high-performing results in production. Participants will gain a deep understanding of various evaluation metrics, methodologies, and their appropriate application across different model types and tasks. The course will emphasize the unique challenges posed by generative AI models and provide strategies for tackling them effectively
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Introduction to Image Generation
This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.
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H2O Gen AI Ecosystem Overview - Level 2
Expand your understanding of H2O's GenAI platform with the Ecosystem Overview - Level 2 course, presented by H2O's very own Sanyam Bhutani! This course provides an in-depth comprehensive exploration of the tools, applications, and methodologies tailored for artificial intelligence (AI) and machine learning (ML) within the H2O ecosystem. You will delve deeper into efficient data preparation techniques, advanced model training, deployment strategies, and real-time monitoring solutions, covering the full spectrum of all the capabilities available. Through engaging modules, you will soon master t
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Planning a Machine Learning Project
The Planning a Machine Learning Project course introduces requirements to determine if ML is the appropriate solution to a business problem. This course focuses on business leaders and other decision-makers currently or potentially involved in ML projects.
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Foundations of Artificial Intelligence
This course introduces learners to the core principles of artificial intelligence, including its history, definitions, and the role of data in AI. You’ll explore algorithms, specialized hardware, and delve into machine learning and deep learning fundamentals. Ethical considerations around AI, especially generative AI (GenAI), will also be discussed, ensuring you understand the broader impact AI has on society and the workforce. With clear explanations and real-world examples, the course ensures you not only learn theoretical concepts but also understand their practical implications. You’
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Generative AI in Software Development
The course provides a comprehensive exploration of how generative AI is reshaping software development by accelerating coding, improving debugging, and enhancing automation. It is designed for aspiring software engineers, developers, and professionals who want to integrate AI into modern development workflows to build efficient, scalable, and error-free applications. You will explore the role of large language models (LLMs) like GPT, Gemini, and LLaMA in coding tasks, software testing, and project automation. The course begins with foundational AI concepts—machine learning, deep learning, and
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GitHub: Evaluating and Integrating AI Models
Learn to evaluate, select, and integrate AI models using GitHub Models — a service that provides ready-to-use, off-the-shelf machine learning models directly within the GitHub platform. You will navigate the GitHub Models marketplace to compare models by provider, capability, and rate limits, then test them interactively using the built-in playground with system prompts and temperature controls. This course covers the practical skills needed to move from model evaluation to production integration. You will understand how rate limits work across different models, learn strategies for scaling b
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Analyze, Create, and Secure Data with Zero Trust
Ever wondered why data breaches keep happening despite massive security investments? The answer lies in moving beyond perimeter defense to a comprehensive zero-trust approach that assumes breach and verifies everything. This Short Course was created to help Machine Learning and AI professionals accomplish enterprise-grade data security that protects against both external threats and insider risks. By completing this course, you'll master the critical investigative skills to identify why breaches occur, architect security systems that never trust by default, and systematically evaluate your de
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Big Data, Artificial Intelligence, and Ethics
This course gives you context and first-hand experience with the two major catalyzers of the computational science revolution: big data and artificial intelligence. With more than 99% of all mediated information in digital format and with 98% of the world population using digital technology, humanity produces an impressive digital footprint. In theory, this provides unprecedented opportunities to understand and shape society. In practice, the only way this information deluge can be processed is through using the same digital technologies that produced it. Data is the fuel, but machine learning
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Graduate Admission Prediction with Pyspark ML
In this 1 hour long project-based course, you will learn to build a linear regression model using Pyspark ML to predict students' admission at the university. We will use the graduate admission 2 data set from Kaggle. Our goal is to use a Simple Linear Regression Machine Learning Algorithm from the Pyspark Machine learning library to predict the chances of getting admission. We will be carrying out the entire project on the Google Colab environment with the installation of Pyspark. You will need a free Gmail account to complete this project. Please be aware of the fact that the dataset and th
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Device-based Models with TensorFlow Lite
Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. This second course teaches you how to run your machine learning models in mobile applications. You’ll learn how to prepare models for a lower-powered, battery-operated devices, then execute models on both Android and iOS platforms. Finally, you’ll explore how to deploy on embedded systems using TensorFlow on Raspberry Pi and microcontrollers. This Specialization builds u
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ChatGPT and Prompt Engineering With Advanced Data Analysis
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. This course offers an in-depth journey into the world of ChatGPT, prompt engineering, and advanced data analysis techniques. You will explore how to leverage AI tools like ChatGPT in business, programming, social media, and machine learning, while gaining hands-on experience in crafting effective prompts, using AI for data analysis, and generating creative outputs. By t
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Data Management for Analytics Part 1
This course will offer you an opportunity to learn the fundamental concepts and emerging technologies in database design and modeling and database systems. It presents a balanced theory-practice focus and covers entity relationship model and UML model, relational model, and relational databases. By the end of this part 1 course on data analytics, you will have a foundational understanding of the theory and applications of database management to support data analytics, data mining, machine learning, and artificial intelligence.
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Introduction to Computer Vision
In the first course of the Computer Vision for Engineering and Science specialization, you’ll be introduced to computer vision. You'll learn and use the most common algorithms for feature detection, extraction, and matching to align satellite images and stitch images together to create a single image of a larger scene. Features are used in applications like motion estimation, object tracking, and machine learning. You’ll use features to estimate geometric transformations between images and perform image registration. Registration is important whenever you need to compare images of the same s
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Clinical Decision Support Systems
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.
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AI and Content Recommendation
Build your subject-matter expertise This course is part of the AI in Media specialisation. When you enrol in this course; you'll also be enrolled in this Specialisation. ● Learn new concepts from industry experts ● Gain a foundational understanding of modern strategic tools ● Develop job-relevant skills with hands-on scenario-based projects ● Earn a shareable career certificate About this Course In a world where content is tailored to users, understanding recommendation algorithms is vital for any media professional. This course explores how organisations use machine learning to build 'mode
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Deploying and Debugging ML Microservices
Deploying machine learning models into production systems requires more than training a model—it requires reliable deployment, monitoring, and debugging practices. In this course, you'll learn how to deploy machine learning models as scalable services and maintain them within real software architectures. You’ll begin by learning how to package and deploy machine learning models using containerization and orchestration technologies. You’ll apply tools such as Docker and Kubernetes to manage application deployment and ensure that models run consistently across environments. Next, you’ll design
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AI for Healthcare
Healthcare professionals today manage increasing volumes of patient data, rising diagnostic demands, and the pressure to make fast, accurate decisions. AI for Healthcare is a beginner-friendly, practical course designed to help you apply AI confidently in clinical workflows, improve diagnostics, and enhance patient outcomes—without any coding experience. Instead of focusing on theory, this course explores real hospital use cases. You’ll see how healthcare teams use machine learning, predictive analytics, and medical imaging AI to identify patterns, support clinical decisions, and reduce manua
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Introduction to Digital health
This course introduces the field of digital health and the key concepts and definitions in this emerging field. The key topics include Learning Health Systems and Electronic Health Records and various types of digital health technologies to include mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These technologies are assessed in terms of the key opportunities and challenges to their use and the evidence of their effectiveness in the field of digital health in relation to public health and
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Amazon Kinesis Video Streams - Getting Started
With Amazon Kinesis Video Streams, you can build media streaming applications for Internet of Things (IoT) video devices and real-time computer vision machine learning (ML) applications. In this course, you will learn the benefits and technical concepts of Kinesis Video Streams. If you are new to the service, you will learn how to start using Kinesis Video Streams through a demonstration using the AWS Management Console. You will learn about the native architecture and how the built-in features can help you simplify image extraction through APIs or automated image extraction from metadata tag
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Debug Audio Models: Performance and Root Cause
Unlock the critical skills needed to diagnose and resolve audio model failures in production environments. This course empowers ML and AI professionals to move beyond surface-level metrics and develop systematic approaches to audio model debugging that drive real business impact. This Short Course was created to help machine learning and artificial intelligence professionals accomplish comprehensive audio model performance evaluation and root cause analysis. By completing this course, you'll be able to calculate industry-standard performance metrics like Word Error Rate and F1-scores, perfor
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