WelcomeOverviewMachine learning typesMachine learning algorithmsStatistical machine learningLinear algebra for machine learningData visualization for machine learningUncertainty quantificationBias variance tradeoffBayesian StatisticsSingular value decompositionOverviewFeature selectionFeature extractionVector embeddingLatent spacePrincipal component analysisLinear discriminant analysisUpsamplingDownsamplingSynthetic dataData leakageOverviewLinear regressionLasso regressionRidge regressionState space modelTime seriesAutoregressive modelOverviewDecision treesK-nearest neighbors (KNNs)Naive bayesRandom forestSupport vector machineLogistic regressionOverviewBoostingBaggingGradient boostingGradient boosting classifierOverviewTransfer learningOverviewOverviewK means clusteringHierarchical clusteringA priori algorithmGaussian mixture modelAnomaly detectionOverviewCollaborative filteringContent based filteringOverviewReinforcement learning human feedbackDeep reinforcement learningOverviewOverviewBackpropagationEncoder-decoder modelRecurrent neural networksLong short-term memory (LSTM)Convolutional neural networksOverviewAttention mechanismGrouped query attentionPositional encodingAutoencoderMamba modelGraph neural networkOverviewGenerative modelGenerative AI vs. predictive AIOverviewReasoning modelsSmall language modelsInstruction tuningLLM parametersLLM temperatureLLM benchmarksLLM customizationLLM alignmentTutorial: Multilingual LLM agentDiffusion modelsVariational autoencoder (VAE)Generative adversarial networks (GANs)OverviewVision language modelsTutorial: Build an AI stylistTutorial: Multimodal AI queries using LlamaTutorial: Multimodal AI queries using PixtralTutorial: Automatic podcast transcription with GraniteTutorial: PPT AI image analysis answering systemOverviewGraphRAGTutorial: Build a multimodal RAG system with Docling and GraniteTutorial: Evaluate RAG pipline using RagasTutorial: RAG chunking strategiesTutorial: Graph RAG using knowledge graphsTutorial: Inference scaling to improve multimodal RAGOverviewVibe codingVisit the 2025 Guide to AI AgentsOverviewLLM trainingLoss functionTraining dataModel parametersOverviewGradient descentStochastic gradient descentHyperparameter tuningLearning rateOverviewParameter efficient fine tuning (PEFT)LoRATutorial: Fine tuning Granite model with LoRARegularizationFoundation modelsOverfittingUnderfittingFew shot learningZero shot learningKnowledge distillationMeta learningData augmentationCatastrophic forgettingOverviewScikit-learnXGboostPyTorchOverviewAI lifecyleAI inferenceModel deploymentMachine learning pipelineData labelingModel risk managementModel driftAutoMLModel selectionFederated learningDistributed machine learningAI stackOverviewNatural language understandingOverviewSentiment analysisTutorial: Spam text classifier with PyTorchMachine translationOverviewInformation retrievalInformation extractionTopic modelingLatent semantic analysisLatent Dirichlet AllocationNamed entity recognitionWord embeddingsBag of wordsIntelligent searchSpeech recognitionStemming and lemmatizationText summarizationConversational AIConversational analyticsNatural language generationOverviewImage classificationObject detectionInstance segmentationSemantic segmentationOptical character recognitionImage recognitionVisual inspectionmachine learningIBM Privacy Statementhuman-in-the-loop (HITL)what is the differencesupervised learningunsupervised learningWatch the seriesartificial intelligenceground truthMaximo Visual Inspectionspeech recognitionSpotifyApple PodcastsFind more episodesEbook
Data science and MLOps for data leaders
Align with other leaders on the 3 key goals of MLOps and trustworthy AI: trust in data, trust in models and trust in processes.
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Techsplainers by IBM breaks down the essentials of MLOps, from key concepts to real‑world use cases. Clear, quick episodes help you learn the fundamentals fast.
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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.
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Unlock the power of generative AI and ML
Learn how to incorporate generative AI, machine learning and foundation models into your business operations for improved performance.
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How to choose the right foundation model
Learn how to select the most suitable AI foundation model for your use case.
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What is machine learning?
Machine learning is a branch of AI and computer science that focuses on using data and algorithms to enable AI to imitate the way that humans learn.
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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.
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