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Model Selection in Machine Learning | IBM

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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 IBM Privacy Statement AI model machine learning machine learning pipeline dataset Watch the series machine learning algorithms Time series forecasting Time series models Classification training set supervised learning Clustering unsupervised learning Data scientists Hyperparameter tuning generative AI fine-tuning Overfitting Underfitting feature selection synthetic data Interpretability intelligent automation AI agents RAG question-answering chatbots text generation Natural language processing (NLP)GPT GPT-4o Claude Gemini reasoning API Ebook 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. Read the ebook Techsplainers Podcast MLOps explained 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. Listen now 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 Ebook 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. Read the ebook Ebook How to choose the right foundation model Learn how to select the most suitable AI foundation model for your use case. Read the ebook Explainer 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. Read the article Guide 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 guide Explore watsonx.ai Explore AI solutions Explore AI services Explore watsonx.ai Explore AI solutions

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