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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra for machine learning Data visualization for machine learning Uncertainty quantification Bias variance tradeoff Bayesian Statistics Singular value decomposition Overview Feature selection Feature extraction Vector embedding Latent space Principal component analysis Linear discriminant analysis Upsampling Downsampling Synthetic data Data leakage Overview Linear regression Lasso regression Ridge regression State space model Time series Autoregressive model Overview Decision trees K-nearest neighbors (KNNs)Naive bayes Random forest Support vector machine Logistic regression Overview Boosting Bagging Gradient boosting Gradient boosting classifier Overview Transfer learning Overview Overview K means clustering Hierarchical clustering A priori algorithm Gaussian mixture model Anomaly detection Overview Collaborative filtering Content based filtering Overview Reinforcement learning human feedback Deep reinforcement learning Overview Overview Backpropagation Encoder-decoder model Recurrent neural networks Long short-term memory (LSTM)Convolutional neural networks Overview Attention mechanism Grouped query attention Positional encoding Autoencoder Mamba model Graph neural network Overview Generative model Generative AI vs. predictive AI Overview Reasoning models Small language models Instruction tuning LLM parameters LLM temperature LLM benchmarks LLM customization LLM alignment Tutorial: Multilingual LLM agent Diffusion models Variational autoencoder (VAE)Generative adversarial networks (GANs)Overview Vision language models Tutorial: Build an AI stylist Tutorial: Multimodal AI queries using Llama Tutorial: Multimodal AI queries using Pixtral Tutorial: Automatic podcast transcription with Granite Tutorial: PPT AI image analysis answering system Overview GraphRAG Tutorial: Build a multimodal RAG system with Docling and Granite Tutorial: Evaluate RAG pipline using Ragas Tutorial: RAG chunking strategies Tutorial: Graph RAG using knowledge graphs Tutorial: Inference scaling to improve multimodal RAG Overview Vibe coding Visit the 2025 Guide to AI Agents Overview LLM training Loss function Training data Model parameters Overview Gradient descent Stochastic gradient descent Hyperparameter tuning Learning rate Overview Parameter efficient fine tuning (PEFT)LoRA Tutorial: Fine tuning Granite model with LoRA Regularization Foundation models Overfitting Underfitting Few shot learning Zero shot learning Knowledge distillation Meta learning Data augmentation Catastrophic forgetting Overview Scikit-learn XGboost PyTorch Overview AI lifecyle AI inference Model deployment Machine learning pipeline Data labeling Model risk management Model drift AutoML Model selection Federated learning Distributed machine learning AI stack Overview Natural language understanding Overview Sentiment analysis Tutorial: Spam text classifier with PyTorch Machine translation Overview Information retrieval Information extraction Topic modeling Latent semantic analysis Latent Dirichlet Allocation Named entity recognition Word embeddings Bag of words Intelligent search Speech recognition Stemming and lemmatization Text summarization Conversational AI Conversational analytics Natural language generation Overview Image classification Object detection Instance segmentation Semantic segmentation Optical character recognition Image recognition Visual inspection Ivan Belcic Cole Stryker Natural language processing structured data and unstructured data IBM Privacy Statement information retrieval artificial intelligence retrieval-augmented generation (RAG)large language models question-answering Data pipelines Machine learning algorithms text summarization Watch all episodes of Mixture of Experts Named entity recognition Sentiment analysis machine learning supervised learning classification deep learning neural networks Transformer models GPTs Report Beyond the hype - How AI assistants drive real business value Explore top use cases for leveraging AI assistants, understand the potential impact of Gen AI and automation technology on your business, and learn how to get started. Read the report AI 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 Granite Explainer Beginner's guide to NLP Discover how natural language processing (NLP) can help you to converse more naturally with computers. Learn NLP Report IBM is named leader in data science and machine learning Learn why IBM has been recognized as a Leader in the 2025 Gartner® Magic Quadrant™ for data science and machine learning platforms. Read the report Training Hands-on with generative AI Learn fundamental concepts and build your skills with hands-on labs, courses, guided projects, trials and more. Learn generative AI Explore watsonx Orchestrate Explore NLP solutions Explore AI services Explore watsonx Orchestrate Explore NLP solutions Intelligent Document Processing Market Size, Share & Trends Analysis Report By Component (Solution, Services), By Technology (ML, NLP), By Deployment, By Organization Size, By End-use (Manufacturing, Retail), By Region, And Segment Forecasts, 2025-2030

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