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driftmodel governancePythonPyTorchTensorFlowmachine learning librariesScikit-learnXGBoostMatplotlibtutorialsSpotifyApple PodcastsFind more episodesDave BergmannEbook Data science and MLOps for data leaders Join forces with other leaders to drive the three essential pillars of MLOps and trustworthy AI: trust in data, trust in models and trust in processes. Read the ebookTraining Level up your ML expertise Learn fundamental concepts and build your skills with hands-on labs, courses, guided projects, trials and more. Explore ML coursesEbook Unlock the power of generative AI + ML Learn how to confidently incorporate generative AI and machine learning into your business. Read the ebookTechsplainers Podcast Machine learning explained Techsplainers by IBM breaks down the essentials of machine learning, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast. Listen nowGuide Put AI to work: Driving ROI with gen AI Want to get a better return on your AI investments? Learn how scaling gen AI in key areas drives change by helping your best minds build and deliver innovative new solutions. Read the guideEbook How to choose the right foundation model Learn how to select the most suitable AI foundation model for your use case. Read the ebookAI models Explore IBM Granite IBM® Granite® is our family of open, performant and trusted AI models, tailored for business and optimized to scale your AI applications. Explore language, code, time series and guardrail options. Meet GraniteGuide How to thrive in this new era of AI with trust and confidence Dive into the 3 critical elements of a strong AI strategy: creating a competitive edge, scaling AI across the business and advancing trustworthy AI. Read the guideExplore watsonx OrchestrateExplore AI development toolsExplore AI servicesExplore watsonx OrchestrateExplore watsonx.ai"Some Studies in Machine Learning Using the Game of Checkers,""Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19,""Kolmogorov's Mapping Neural Network Existence Theorem,""Multilayer Feedforward Networks with a Non-Polynomial Activation Function Can Approximate Any Function,"

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