#
Machine learning
Resource Information
The concept ** Machine learning** represents the subject, aboutness, idea or notion of resources found in **University of Missouri Libraries**.

The Resource
Machine learning
Resource Information

The concept

**Machine learning**represents the subject, aboutness, idea or notion of resources found in**University of Missouri Libraries**.- Label
- Machine learning

#### A sample of Items that share the Concept Machine learning See All

## Context

Context of Machine learning#### Subject of

No resources found

No enriched resources found

- 2015 International Conference on Machine Learning and Cybernetics (ICMLC)
- 3D hand pose estimation via a lightweight deep learning model
- 3D neural network visualization with TensorSpace
- 3D shape analysis : fundamentals, theory, and applications
- 5 questions on artificial intelligence
- 5 questions on artificial intelligence
- 6 Trends Framing the State of AI and ML
- A Data Science Approach to Extracting Insights About Cities and Zones Using Open Government Data
- A deep learning method for protein model quality assessment
- A learning receiver for communication in three-component multipath channels
- A machine learning approach to phishing detection and defense
- A machine-learning decision-support tool for travel-demand modeling
- A primer on machine learning for materials and its relevance to Army challenges
- AI and Machine Learning for Coders
- AI and machine learning for healthcare : an overview of tools and challenges for building a health-tech data pipeline
- AI and the index management problem
- AI as a Service
- AI for finance
- AI for marketing and product innovation : powerful new tools for predicting trends, connecting with customers, and closing sales
- Accelerate deep learning on Raspberry Pi
- Accessibility of big data imagery for next generation machine learning applications
- Achieving real business outcomes from artificial intelligence : enterprise considerations for AI initiatives
- Adaptive blind signal and image processing : learning algorithms and applications
- Adaptive temporal difference learning of spatial memory in the water maze task
- Advanced NLP projects with TensorFlow 2.0
- Advanced R statistical programming and data models : analysis, machine learning, and visualization
- Advanced applied deep learning : convolutional neural networks and object detection
- Advanced computer vision with TensorFlow
- Advanced data analytics using Python : with machine learning, deep learning and NLP examples
- Advanced deep learning with Keras
- Advanced deep learning with Keras : apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
- Advanced deep learning with Python
- Advanced deep learning with R : become an expert at designing, building, and improving advanced neural network models using R
- Advanced machine learning
- Advanced machine learning with Python : solve challenging data science problems by mastering cutting-edge machine learning techniques in Python
- Advanced machine learning with scikit-learn : tools and techniques for predictive analytics in Python
- Advanced statistics and data mining for data science
- Advanced structured prediction
- Advances in financial machine learning
- Advances in financial machine learning
- Agile machine learning : effective machine learning inspired by the agile manifesto
- Algorithmic learning
- Algorithmic recommendations at The New York Times
- Algorithms for reinforcement learning
- Algorithms for reinforcement learning
- Amazon machine learning
- An introduction to Bayesian networks
- An introduction to machine learning interpretability : an applied perspective on fairness, accountability, transparency, and explainable AI
- An introduction to machine learning models in production : how to transition from one-off models to reproducible pipelines
- Analysis of flame images in gas-fired furnaces
- Analyzing and visualizing data with F#
- Apache Spark 2 data processing and real-time analytics : master complex big data processing, stream analytics, and machine learning with Apache
- Apache Spark 2.x machine learning cookbook : over 100 recipes to simplify machine learning model implementations with Spark
- Apache Spark deep learning cookbook : over 80 recipes that streamline deep learning in a distributed environment with Apache Spark
- Apache Spark machine learning blueprints : develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide
- Apache Spark quick start guide : quickly learn the art of writing efficient big data applications with Apache Spark
- Applications of embeddings and deep learning at Groupon
- Applied Machine Learning for Health and Fitness : A Practical Guide to Machine Learning with Deep Vision, Sensors and IoT
- Applied analytics through case studies using SAS and R : implementing predictive models and machine learning techniques
- Applied data science with Python and Jupyter
- Applied deep learning : a case-based approach to understanding neural networks
- Applied deep learning and computer vision for self-driving cars : build autonomous vehicles using deep neural networks and behavior-cloning techniques
- Applied machine learning and deep learning with R
- Applied machine learning for healthcare
- Applied machine learning for spreading financial statements
- Applied machine learning with R
- Applied natural language processing with Python : implementing machine learning and deep learning algorithms for natural language processing
- Applied text analysis with Python : enabling language-aware data products with machine learning
- Applied unsupervised learning with Python : discover hidden patterns and relationships in unstructured data with Python
- Applied unsupervised learning with R
- Architecting system of systems: artificial life analysis of financial market behavior
- Artificial Intelligence Business : How you can profit from AI
- Artificial Intelligence By Example - Second Edition
- Artificial intelligence : the simplest way
- Artificial intelligence and intelligent systems : the implications
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning fundamentals
- Artificial intelligence and machine learning in industry : perspectives from leading practitioners
- Artificial intelligence and machine learning in libraries
- Artificial intelligence basics : a non-technical introduction
- Artificial intelligence in 3 hours
- Artificial intelligence now : current perspectives from O'Reilly Media
- Artificial intelligence on human behavior : new insights into customer segmentation
- Artificial neural networks : learning algorithms, performance evaluation, and applications
- Automated prediction of hepatic arterial stenosis
- Automatic learning techniques in power systems
- Automating DevOps for machine learning
- Autonomous cars : deep learning and computer vision in Python
- Autonomous learning systems : from data streams to knowledge in real-time
- Avoiding the pitfalls of deep learning : solving model overfitting with regularization and dropout
- Azure cognitive services for developers
- Azure masterclass : manage Azure cloud with ARM templates
- Basic data analysis with Java
- Bayesian learning for neural networks
- Bayesian networks and decision graphs
- Bayesian networks and decision graphs
- Beginning AI bot frameworks : getting started with bot development
- Beginning MATLAB and Simulink : from novice to professional
- Beginning application development with TensorFlow and Keras
- Beginning application development with TensorFlow and Keras : learn to design, develop, train, and deploy TensorFlow and Keras models as real-world applications
- Beginning artificial intelligence with the Raspberry Pi
- Beginning data science with Python and Jupyter
- Beginning machine learning in iOS : CoreML framework
- Beginning machine learning with AWS
- Best practices for bringing AI to the enterprise
- Big data analysis : new algorithms for a new society
- Big data analytics for intelligent healthcare management
- Big data analytics using Apache Spark
- Big data and machine learning in quantitative investment
- Bioinformatics : the machine learning approach
- Bioinformatics : the machine learning approach
- Bringing data to life : combining machine learning and art to tell a data story
- Build more inclusive TensorFlow pipelines with fairness indicators
- Building Cognitive Applications with IBM Watson Services : Volume 2 Conversation
- Building Recommender systems with machine learning and AI
- Building a big data analytics stack
- Building a recommendation engine with Scala : learn to use Scala to build a recommendation engine from scratch and empower your website users
- Building a recommendation system with R : learn the art of building robust and powerful recommendation engines using R
- Building advanced OpenCV 3 projects with Python
- Building enterprise data products
- Building intelligent cloud applications : develop scalable models using serverless architectures with Azure
- Building machine learning and deep learning models on Google Cloud Platform : a comprehensive guide for beginners
- Building machine learning powered applications : going from idea to product
- Building machine learning projects with TensorFlow : engaging projects that will teach you how complex data can be exploited to gain the most insight
- Building machine learning systems with Python : explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow
- Building machine learning systems with TensorFlow
- Business data science : combining machine learning and economics to optimize, automate, and accelerate business decisions
- C# machine learning projects : nine real-world projects to build robust and high-performing machine learning models with C#
- C4.5 : programs for machine learning
- CLASSMATE : Computerized Learning Agent Solving Simultaneous MAThematical Equations
- Can data science help us find what makes a hit television show
- Challenges and applications for implementing machine learning in computer vision
- Challenges in machine learning from model building to deployment at scale
- Class Representative Projection for Text-based Zero-Shot Learning
- Classification and learning using genetic algorithms : applications in bioinformatics and web intelligence
- Classification of human postural and gestural movements using center of pressure parameters derived from force platforms
- Clojure for data science : statistics, big data, and machine learning for Clojure programmers
- Clustering & classification with machine learning in R : harness the power of machine learning for unsupervised & supervised learning in R
- Clustering : Methodology, hybrid systems, visualization, validation and implementation
- Clustering and unsupervised learning, Part 4, Introduction to real-world machine learning
- Cognitive carpentry : a blueprint for how to build a person
- Cognitive computing recipes : artificial intelligence solutions using Microsoft cognitive services and Tensorflow
- Cognitive computing with IBM Watson : build smart applications using artificial intelligence as a service
- Computational intelligence in business analytics : concepts, methods, and tools for big data applications
- Computational learning theory : an introduction
- Computational methods of feature selection
- Computational trust models and machine learning
- Computer vision and machine learning with RGB-D sensors
- Computer vision and machine learning with RGB-D sensors
- Computer vision projects with OpenCV and Python 3 : six end-to-end projects build using machine learning with OpenCV, Python, and TensorFlow
- Concept data analysis : theory and applications
- Concurrent learning and information processing : a neuro-computing system that learns during monitoring, forecasting, and control
- Conformal prediction for reliable machine learning : theory, adaptations and applications
- Conformal prediction for reliable machine learning : theory, adaptations, and applications
- Connectionist models and their implications : readings from cognitive science
- Considering TensorFlow for the enterprise : an overview of the deep learning ecosystem
- Customizing state-of-the-art deep learning models for new computer vision solutions
- DL-DI : a deep learning framework for distributed, incremental image classification
- DMLA : a dynamic model-based Lambda Architecture for learning and recognition of features in Big Data
- Dan Van boxel's deep learning with TensorFlow
- Data Mining and Machine Learning in Cybersecurity
- Data Science Programming All-In-One for Dummies
- Data analysis with Python : a modern approach
- Data analytics and machine learning fundamentals : LiveLessons
- Data and social good : using data science to improve lives, fight injustice, and support democracy
- Data fusion by using machine learning and computational intelligence techniques for medical image analysis and classification
- Data science algorithms in a week : data analysis, machine learning, and more
- Data science algorithms in a week : top 7 algorithms for scientific computing, data analysis, and machine learning
- Data science and engineering at enterprise scale : notebook-driven results and analysis
- Data science and machine learning with Python - hands on!
- Data science and machine learning with Python--Hands on!
- Data science fundamentals, Part 1, Learning basic concepts, data wrangling, and databases with Python
- Data science fundamentals, Part 2, Machine learning and statistical analysis
- Data science in the cloud with Microsoft Azure machine learning and Python
- Data science in the cloud with Microsoft Azure machine learning and R : 2015 update
- Data science isn't just another job
- Data science projects with Python : a case study approach to successful data science projects using Python, pandas, and scikcit-learn
- Data science with Microsoft Azure and R
- Data science with Python : combine Python with machine learning principles to discover hidden patterns in raw data
- Data statistics with full stack Python
- Data visualization recipes in Python
- Data-driven security assessment of power grids based on machine learning approach : preprint
- Dealing with real-world data, Part 1, Introduction to real-world machine learning
- Deep Learning - Grundlagen und Implementierung : Neuronale Netze mit Python und PyTorch programmieren
- Deep Learning for Beginners
- Deep Learning for Natural Language Processing : Solve Your Natural Language Processing Problems with Smart Deep Neural Networks
- Deep Learning for the Life Sciences : Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
- Deep Learning fÃ¼r die Biowissenschaften : Einsatz von Deep Learning in Genomik, Biophysik, Mikroskopie und medizinischer Analyse
- Deep Learning illustriert
- Deep Learning mit Python und Keras : Das Praxis-Handbuch vom Entwickler der Keras-Bibliothek
- Deep Learning with TensorFlow
- Deep Learning with TensorFlow : Explore neural networks and build intelligent systems with Python, 2nd Edition
- Deep Open Representative Learning for Image and Text Classification
- Deep learning
- Deep learning
- Deep learning : a practitioner's approach
- Deep learning : das umfassende Handbuch : Grundlagen, aktuelle Verfahren und Algorithmen, neue ForschungsansÃ¤tze
- Deep learning : moving toward artificial intelligence with neural networks and machine learning
- Deep learning : practical neural networks with Java : build and run intelligent applications by leveraging key Java machine learning libraries : a course in three modules
- Deep learning Kochbuch : Praxisrezepte fÃ¼r einen schnellen Einstieg
- Deep learning and neural networks using Python - Keras : the complete beginners guide
- Deep learning and the game of Go
- Deep learning architecture for building artificial neural networks
- Deep learning by example : a hands-on guide to implementing advanced machine learning algorithms and neural networks
- Deep learning cookbook : practical recipes to get started quickly
- Deep learning crash course
- Deep learning for coders with fastai and PyTorch : AI applications without a PhD
- Deep learning for computer vision with SAS : an introduction
- Deep learning for dummies
- Deep learning for natural language processing : applications of deep neural networks to machine learning tasks
- Deep learning for natural language processing : creating neural networks with Python
- Deep learning for numerical applications with SAS
- Deep learning for recommender systems, or How to compare pears with apples
- Deep learning for search
- Deep learning for strategic decision makers : understanding deep learning and how it produces business value
- Deep learning for time series data
- Deep learning from scratch : building with Python from first principles
- Deep learning illustrated : a visual, interactive guide to artificial intelligence
- Deep learning mit R und Keras : Das Praxis-Handbuch : von Entwicklern von Keras und RStudio
- Deep learning pipeline : building a deep learning model with TensorFlow
- Deep learning projects using TensorFlow 2 : neural network development with Python and Keras
- Deep learning receptury
- Deep learning techniques for biomedical and health informatics
- Deep learning through sparse and low-rank modeling
- Deep learning using OpenPose : learn Pose estimation models and build 5 AI apps
- Deep learning with Apache Spark
- Deep learning with Keras : implement neural networks with Keras on Theano and TensorFlow
- Deep learning with Microsoft Cognitive Toolkit quick start guide : a practical guide to building neural networks using Microsoft's open source deep learning framework
- Deep learning with PyTorch
- Deep learning with PyTorch : a practical approach to building neural network models using PyTorch
- Deep learning with PyTorch quick start guide : learn to train and deploy neural network models in Python
- Deep learning with Python
- Deep learning with Python
- Deep learning with Python : a hands-on introduction
- Deep learning with Python video edition
- Deep learning with R
- Deep learning with R cookbook : over 45 unique recipes to delve into neural network techniques using R 3.5x
- Deep learning with R for beginners : design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
- Deep learning with R in motion
- Deep learning with TensorFlow
- Deep learning with TensorFlow
- Deep learning with TensorFlow 2 and Keras : regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API
- Deep learning with TensorFlow : take your machine learning knowledge to the next level with the power of TensorFlow
- Deep learning with applications using Python : chatbots and face, object, and speech recognition with TensorFlow and Keras
- Deep reinforcement learning and GANS Livelessons
- Deep reinforcement learning hands-on : apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and more
- Deep reinforcement learning hands-on : apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
- DeepSampling : Image Sampling Technique for Cost-Effective Deep Learning
- Demand-driven associative classification
- Deploying Spark ML pipelines in production on AWS : how to publish pipeline artifacts and run pipelines in production
- Deploying machine learning models as microservices using Docker : a REST-based architecture for serving ML model outputs at scale
- Developing an image classifier using TensorFlow : convolutional neural networks
- Discrete sequence prediction and its applications
- Distributed Collaborative Framework for Deep Learning in Object Detection
- Distributed deep learning with Apache Spark
- Dynamic model generation and semantic search for open source projects using big data analytics
- Dynamic neural network programming with PyTorch
- END-TO-END DATA SCIENCE WITH SAS : a hands-on programming guide;a hands-on programming guide
- Effective Amazon Machine Learning
- Effective Amazon machine learning : machine learning in the Cloud
- Effective enterprise architecture
- Efficient learning machines : theories, concepts, and applications for engineers and system Designers
- EinfÃ¼hrung in Machine Learning mit Python : Praxiswissen Data Science
- EinfÃ¼hrung in TensorFlow : Deep-Learning-Systeme programmieren, trainieren, skalieren und deployen
- Enhance recommendations in Uber Eats with graph convolutional networks
- Ensemble machine learning cookbook : over 35 practical recipes to explore ensemble machine learning techniques using Python
- Ensemble machine learning techniques
- Ethics and data science
- Evaluating Learning Algorithms : A Classification Perspective
- Evaluating machine learning models : a beginner's guide to key concepts and pitfalls
- Executive briefing : usable machine learning - lessons from Stanford and beyond
- Executive briefing : why machine-learned models crash and burn in production and what to do about it
- Exemplar-based knowledge acquisition : a unified approach to concept representation, classification, and learning
- Fast learning and invariant object recognition : the sixth generation breakthrough
- Feature discovery through error-correction learning
- Feature engineering for machine learning : principles and techniques for data scientists
- Feature engineering made easy : identify unique features from your dataset in order to build powerful machine learning systems
- Feature-based analysis for open source using big data analytics
- Foundations of deep reinforcement learning : theory and practice in Python
- Foundations of inductive logic programming
- Foundations of inductive logic programming
- Foundations of machine learning
- Foundations of rule learning
- Fraud detection without feature engineering
- From 0 to 1 : Machine learning, NLP & Python : cut to the chase
- Fundamentals of deep learning : designing next-generation machine intelligence algorithms
- Fundamentals of statistical modeling and machine learning techniques
- Fuzzy adaptive resonance theory: Applications and extensions
- Fuzzy mathematical approach to pattern recognition
- Fuzzy rule-based expert systems and genetic machine learning
- GANs mit PyTorch selbst programmieren
- Game engines and machine learning
- Generative adversarial networks cookbook : over 100 recipes to build generative models using Python, TensorFlow, and Keras
- Generative adversarial networks projects : build next-generation generative models using TensorFlow and Keras
- Generative deep learning : teaching machines to paint, write, compose, and play
- Generatives Deep Learning : Maschinen das Malen, Schreiben und Komponieren beibringen
- Genetic algorithms and machine learning for programmers : create AI models and evolve solutions
- Genetic algorithms in search, optimization, and machine learning
- Genetic learning for adaptive image segmentation
- Getting involved in the TensorFlow community
- Getting started with SAS Enterprise Miner for machine learning : learning to perform segmentation and predictive modeling
- Getting started with TensorFlow
- Getting started with TensorFlow for deep learning
- Getting started with artificial intelligence : a practical guide to building enterprise applications
- Getting started with deep learning
- Getting started with machine learning in Python
- Getting started with machine learning in R
- Getting started with machine learning in the cloud : using cloud-based platforms to discover new business insights
- Getting started with tensorflow : get up and running with the latest numerical computing library by Google and dive deeper into your data!
- Go machine learning projects : eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
- Google BigQuery : the definitive guide : data warehousing, analytics, and machine learning at scale
- Graph-theoretic techniques for web content mining
- Grokking deep learning
- Grokking deep learning in motion
- Hand gesture data collection procedure using Myo armband for machine learning
- Hands-On Artificial Intelligence for Banking
- Hands-On Data Science and Python Machine Learning
- Hands-On Machine Learning with C++ : Build, Train, and Deploy End-To-end Machine Learning and Deep Learning Pipelines
- Hands-On Neural Networks with TensorFlow 2.0
- Hands-on AI with Python and Keras
- Hands-on Java deep learning for computer vision : implement machine learning and neural network methodologies to perform computer vision-related tasks
- Hands-on OpenCV 4 with Python
- Hands-on Q-learning with Python : practical Q-learning with OpenAI Gym, Keras, and TensorFlow
- Hands-on Scikit-Learn for machine learning applications : data science fundamentals with Python
- Hands-on TensorFlow Lite for intelligent mobile apps
- Hands-on artificial intelligence for IoT : expert machine learning and deep learning techniques for developing smarter IoT systems
- Hands-on artificial intelligence for beginners : an introduction to AI concepts, algorithms, and their implementation
- Hands-on artificial intelligence for cybersecurity : implement smart AI systems for preventing cyber attacks and detecting threats and network anomalies
- Hands-on artificial intelligence for search : building intelligent applications and perform enterprise searches
- Hands-on artificial intelligence on Amazon Web Services : decrease the time to market for AI and ML applications with the power of AWS
- Hands-on artificial intelligence with Java for beginners : build intelligent apps using machine learning and deep learning with Deeplearning4j
- Hands-on automated machine learning : a beginner's guide to building automated machine learning systems using AutoML and Python
- Hands-on convolutional neural networks with TensorFlow : solve computer vision problems with modeling in TensorFlow and Python
- Hands-on data analysis with Pandas : efficiently perform data collection, wrangling, analysis, and visualization using Python
- Hands-on data analytics for beginners with Google Colaboratory
- Hands-on data analytics with R
- Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark
- Hands-on data science for marketing : improve your marketing strategies with machine learning using Python and R
- Hands-on data science with Anaconda : utilize the right mix of tools to create high-performance data science applications
- Hands-on deep Q-Learning
- Hands-on deep learning for games : leverage the power of neural networks and reinforcement learning to build intelligent games
- Hands-on deep learning for images with TensorFlow : build intelligent computer vision applications using TensorFlow and Keras
- Hands-on deep learning with Apache Spark : build and deploy distributed deep learning applications on Apache Spark
- Hands-on deep learning with Caffe2
- Hands-on deep learning with Go : a practical guide to building and implementing neural network models using Go
- Hands-on deep learning with TensorFlow
- Hands-on deep learning with TensorFlow : uncover what is underneath your data!
- Hands-on ensemble learning with Python : build highly optimized ensemble machine learning models using scikit-learn and Keras
- Hands-on ensemble learning with R : a beginner's guide to combining the power of machine learning algorithms using ensemble techniques
- Hands-on generative adversarial networks with Keras : your guide to implementing next-generation generative adversarial networks
- Hands-on image processing with Python : expert techniques for advanced image analysis and effective interpretation of image data
- Hands-on intelligent agents with OpenAI Gym : a step-by-step guide to develop AI agents using deep reinforcement learning
- Hands-on machine learning for algorithmic trading : design and implement investment strategies based on smart algorithms that learn from data using Python
- Hands-on machine learning for cybersecurity : safeguard your system by making your machines intelligent using the Python ecosystem
- Hands-on machine learning for data mining
- Hands-on machine learning on Google cloud platform : implementing smart and efficient analytics using Cloud ML Engine