// 78 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.
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
All levels
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
All levels
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
All levels
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
All levels
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
All levels
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
All levels
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
All levels
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
All levels
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
All levels
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
All levels
AI Infrastructure: Cloud TPUs
Welcome to the Cloud TPUs course. We'll explore the advantages and disadvantages of TPUs in various scenarios and compare different TPU accelerators to help you choose the right fit. You'll learn strategies to maximize performance and efficiency for your AI models and understand the significance of GPU/TPU interoperability for flexible machine learning workflows. Through engaging content and practical demos, we'll guide you step-by-step in leveraging TPUs effectively.
All levels
Apply SOLID Design to Optimize Java ML
"Tired of ""God Classes"" and spaghetti code in your Java ML projects? This course, ""Enhance Java ML Design with SOLID Principles,"" is for senior developers and architects ready to build resilient software. The secret to reliable systems is accepting that requirements always evolve. Master the S.O.L.I.D. principles to write code that embraces future changes with minimal impact. This course is designed for senior Java developers and architects with at least 6 months of hands-on experience in Java programming and basic knowledge of machine learning. If you're ready to tackle "God Classes" and
All levels
Machine Learning: Classification
Case Studies: Analyzing Sentiment & Loan Default Prediction In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank. These tasks are an examples of classification, one of the most widely used areas of machine learning, with a broad array of applications, including ad targeting, spam detection, medi
All levels
Databricks ML in Action
This course offers practical skills to build and deploy machine learning solutions on the Databricks platform, covering the entire ML lifecycle from data ingestion to model deployment. You’ll gain hands-on experience with key tools such as MLflow, Vector Search, and AutoML, while mastering the Databricks Lakehouse architecture. This course will equip you with real-world skills to tackle data science challenges using Databricks' state-of-the-art technologies. The course guides learners through hands-on projects that include tasks like streaming, forecasting, image classification, and retrieval
All levels
Classify Images of Cats and Dogs using Transfer Learning
This is a self-paced lab that takes place in the Google Cloud console. TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML powered applications. This lab uses transfer learning to train your machine. In transfer learning, when you build a new model to classify your original dataset, you reuse the feature extraction part and re-train the classification part with your dataset. This method uses l
All levels
Microsoft Azure Security, Governance & Cloud Operations
Welcome to Microsoft Azure Security, Governance & Cloud Operations, a practical course designed for cloud learners, IT professionals, Azure administrators, cloud engineers, and technology professionals who want to understand how organizations manage, monitor, optimize, and modernize Azure environments. This course builds on foundational Azure concepts and introduces the operational capabilities required to manage cloud resources effectively. You’ll explore Azure migration, DevOps, AI and machine learning services, governance, cost management, monitoring, application services, and database sol
All levels
No-Code Data Science with KNIME
This course gives you hands-on access to KNIME, a free, open-source, no-code data science platform used to build real machine learning and generative AI solutions without writing a single line of code. You will learn to install KNIME, navigate its visual Workflow Editor, and connect nodes to read, clean, and transform data, building the same core data preparation skills used in professional analytics teams. From there, you will build and evaluate classification models using decision trees, confusion matrices, and ROC curves, all through drag-and-drop workflows. You will compare multiple model
All levels
Deep Learning and Modern AI Architectures
Build practical deep learning skills that help you design, train, troubleshoot, and improve modern neural network models for vision, sequence, and generative tasks. In this course, you’ll develop hands-on experience used in roles such as machine learning engineer, deep learning engineer, AI engineer, data scientist, and applied scientist. You’ll work with feedforward neural networks, convolutional neural networks, transfer learning, and model optimization techniques, while building a stronger understanding of how modern architectures are applied to real machine learning problems. This is a no
All levels
Unlocking AI and ML : Your Path to Smart Business Solutions
Step into the transformative world of Artificial Intelligence (AI) and Machine Learning (ML) with this beginner-friendly course. You’ll start by discovering how to identify the right data, prepare it for AI applications, and extract insights that create strategic impact. You’ll also explore foundational AI and ML concepts, including Generative AI, neural networks, and responsible AI practices, all explained in a practical, business-focused context. No coding experience is required, just curiosity and a desire to understand how AI can shape smarter decisions and future-ready strategies. By the
All levels
Feature Engineering and Feature Stores for AI and ML
This course focuses on preparing AI-ready data through feature engineering, feature management, and pipeline automation. You will learn how data engineers create high-quality features, organise reusable feature assets, and automate workflows that support scalable machine learning systems. You will begin by exploring the principles of feature engineering and learn how to transform raw datasets into meaningful features for machine learning. Through practical exercises, you will create numerical, categorical, and derived features while applying techniques such as scaling, encoding, and skewness
All levels
Machine Learning and NLP Basics
The Machine Learning and NLP Basics course is a learning resource designed for individuals interested in developing foundational knowledge of machine learning (ML) and natural language processing (NLP). This course is ideal for students, data scientists, software engineers, and anyone seeking to build or strengthen their skills in machine learning and natural language processing. Whether you are starting your journey or seeking to reinforce your foundation, this course provides practical skills and real-world applications. Throughout this course, participants will gain a solid understandin
All levels
AI for Knowledge Workers
If you’re new to AI or a beginner looking to increase your use of AI in knowledge work, this class is for you! It is designed for anyone interested in how AI works and the many ways it is transforming the way we work and create. From business professionals to creatives to leaders, this course will give you the tools to understand and harness the power of Generative AI for both creative and analytical work. We’ll discuss how Machine Learning and Deep Learning are essential to AI and we’ll review how neural networks are trained. You’ll learn how to select the right AI tools, and review real-worl
All levels
Advanced Machine Learning and its Applications
The third course introduces advanced machine learning techniques and their applications. This course is built on the Applied AI Foundations Specialization, which introduced the fundamentals of machine learning, and Course 1, which developed LLM-empowered Python programming skills for AI. In this course, advanced data preprocessing, machine learning outcome evaluation, neural network design and optimization, deep learning, and generative artificial intelligence are introduced. Since these techniques underpin essential modern AI systems in science and engineering, after completing this course, y
All levels
Machine Learning Introduction for Everyone
This three-module course introduces machine learning and data science for everyone with a foundational understanding of machine learning models. You’ll learn about the history of machine learning, applications of machine learning, the machine learning model lifecycle, and tools for machine learning. You’ll also learn about supervised versus unsupervised learning, classification, regression, evaluating machine learning models, and more. Our labs give you hands-on experience with these machine learning and data science concepts. You will develop concrete machine learning skills as well as create
All levels