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Machine Learning with Spark™ and Python®: Essential Techniques for Predictive Analytics |
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Google BigQuery: The Definitive Guide: Data Warehousing, Analytics, and Machine Learning at Scale |
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Machine Learning and Big Data with kdb+/q |
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Practical Machine Learning With Rust: Creating Intelligent Applications In Rust |
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Machine Learning Approaches In Cyber Security Analytics |
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Learn TensorFlow 2.0: Implement Machine Learning And Deep Learning Models With Python |
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Machine Learning for Business: Using Amazon SageMaker and Jupyter` |
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Data Driven Approaches for Healthcare: Machine Learning for Identifying High Utilizers |
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Machine Learning for Future Wireless Communications |
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Algorithms In Machine Learning Paradigms |
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A Concise Introduction to Machine Learning |
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Building Machine Learning Powered Applications: Going From Idea to Product |
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Non-Tuned Machine Learning Approach for Predicting the Compressive Strength of High-Performance Concrete |
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An Autoencoder and Machine Learning Model to Predict Suicidal Ideation with Brain Structural Imaging |
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Machine Learning Approach to Personality Type Prediction Based on the Myers–Briggs Type Indicator |
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Radiomics and Machine Learning in Anal Squamous Cell Carcinoma: A New Step for Personalized Medicine? |
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Machine Learning Approaches for Ship Speed Prediction towards Energy Efficient Shipping |
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Bearing Fault Diagnosis of Induction Motors Using a Genetic Algorithm and Machine Learning Classifiers |
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How We Learn: Why Brains Learn Better Than Any Machine ... for Now |
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Introducing Machine Learning |
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Building Machine Learning Powered Applications: Going from Idea to Product |
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Machine Learning with the Raspberry Pi: Experiments with Data and Computer Vision |
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Industrial Machine Learning: Using Artificial Intelligence as a Transformational Disruptor |
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Machine Learning Paradigms: Advances in Learning Analytics |
2020 |
The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) |
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Hybrid Imaging and Visualization: Employing Machine Learning with Mathematica - Python |
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Machine Learning and Artificial Intelligence |
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Nature-Inspired Computation in Data Mining and Machine Learning |
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The NeurIPS '18 Competition: From Machine Learning to Intelligent Conversations |
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Advances in Neural Computation, Machine Learning, and Cognitive Research III: Selected Papers from the XXI International Conference on Neuroinformatics, October 7-11, 2019, Dolgoprudny, Moscow Region, Russia |
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Machine Learning Approaches to Non-Intrusive Load Monitoring |
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The Demand for Life Insurance: Dynamic Ecological Systemic Theory Using Machine Learning Techniques |
2020 |
Machine Learning Systems for Multimodal Affect Recognition |
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment |
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Optimization in Machine Learning and Applications |
2020 |
Evolutionary Machine Learning Techniques: Algorithms and Applications |
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Machine Learning: Hands-On for Developers and Technical Professionals |
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Statistics, data mining, and machine learning in astronomy : a practical Python guide for the analysis of survey data |
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Artificial Intelligence By Example: Acquire Advanced AI, Machine Learning and Deep Learning design skills |
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Artificial Intelligence By Example: Acquire Advanced AI, Machine Learning and Deep Learning design skills |
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Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning |
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Machine Learning for iOS Developers |
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Machine Learning - Hands-On for Developers and Technical Professionals (Java). |
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Text Mining with Machine Learning: Principles and Techniques |
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Machine Learning Refined: Foundations, Algorithms, and Applications |
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An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine |
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Applied Machine Learning with Python |
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A Novel Machine Learning Aided Antenna Selection Scheme for MIMO Internet of Things |
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Practical Rust Projects: Building Game, Physical Computing, and Machine Learning Applications |
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Lie Group Cohomology and (Multi)Symplectic Integrators: New Geometric Tools for Lie Group Machine Learning Based on Souriau Geometric Statistical Mechanics |
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Self-Service Data Science in Healthcare with Automated Machine Learning |
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Exploring West African Folk Narrative Texts Using Machine Learning |
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Claim Models - Granular Forms and Machine Learning Forms |
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CRICUT FOR BEGINNERS: THE ULTIMATE PRACTICAL GUIDE TO LEARN HOW TO START CRICUT STEP BY STEP AND BECOME A SEASONED MASTER MACHINE WITH ILLUSTRATED PRATICAL EXAMPLES |
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Machine Learning-based Natural Scene Recognition for Mobile Robot Localization in An Unknown Environment |
2020 |
Machine Learning and Its Applications |
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Machine Learning and Its Applications |
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Mastering Machine Learning Algorithms - Second Edition |
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction |
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Linear Algebra and Optimization for Machine Learning: A Textbook |
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First-Order and Stochastic Optimization Methods for Machine Learning |
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Data Mining and Machine Learning: Fundamental Concepts and Algorithms |
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Practical Machine Learning in R |
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Machine Learning With Python For Everyone |
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Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Applications |
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Machine Learning: A Bayesian and Optimization Perspective |
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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction (Wind Energy Engineering) |
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Machine Learning: Hands-On for Developers and Technical Professionals |
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Mathematics for Machine Learning |
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Python 3 for Machine Learning |
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Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More |
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Machine Learning for Business: Using Amazon Sagemaker and Jupyter |
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Handbook of Research on Machine and Deep Learning Applications for Cyber Security (Advances in Information Security, Privacy, and Ethics) |
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Math for Programmers: 3D graphics, machine learning, and simulations with Python MEAP V10 |
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New Age Analytics: Transforming the Internet through Machine Learning, IoT, and Trust Modeling |
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Math for Programmers - 3D graphics, machine learning, and simulations with Python |
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Hands-On Machine Learning with C++ |
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Tinyml: Machine Learning with Tensorflow Lite on Arduino and Ultra-Low-Power Microcontrollers |
2020 |
Machine Learning for Future Wireless Communications (Wiley - IEEE) |
2020 |
Machine Learning: 2 Books in 1: An Introduction Math Guide for Beginners to Understand Data Science Through the Business Applications |
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Document Processing Using Machine Learning |
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A Primer on Machine Learning Applications in Civil Engineering |
2020 |
The Art of Feature Engineering: Essentials for Machine Learning |
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The Art of Feature Engineering: Essentials for Machine Learning |
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Machine Learning for Asset Managers |
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Machine Learning With Health Care Perspective: Machine Learning and Healthcare |
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Machine Learning Refined: Foundations, Algorithms, and Applications |
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On The Path To AI: Law’s Prophecies And The Conceptual Foundations Of The Machine Learning Age |
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Python WebApp: Learn how to serve a Machine Learning Model predicting car prices (Full stack Book 1) |
2020 |
Python WebApp: Learn how to serve a Machine Learning Model predicting car prices (Full stack Book 1) |
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PYTHON FOR BEGINNERS: A Crash Course Guide for Machine Learning and Web Programming. Learn a Computer Language in Easy Steps with Coding Exercises. |
2020 |
PYTHON FOR BEGINNERS: A Crash Course Guide for Machine Learning and Web Programming. Learn a Computer Language in Easy Steps with Coding Exercises. |
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Grokking Machine Learning MEAP V07 |
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Accelerated Optimization for Machine Learning: First-Order Algorithms |
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Machine Learning for Beginners: The Ultimate Guide to Learn and Understand Machine Learning – A Practical Approach to Master Machine Learning to Improve and Increase Business Results |
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Machine Learning for Beginners: The Ultimate Guide to Learn and Understand Machine Learning – A Practical Approach to Master Machine Learning to Improve and Increase Business Results |
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Python Machine Learning: Machine Learning And Deep Learning From Scratch Illustrated With Python, Scikit-Learn, Keras, Theano And Tensorflow |
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Python Machine Learning: Machine Learning And Deep Learning From Scratch Illustrated With Python, Scikit-Learn, Keras, Theano And Tensorflow |
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Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML |
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Mastering Azure Machine Learning: Perform large-scale end-to-end advanced machine learning on the cloud with Microsoft Azure ML |
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Hands-On Machine Learning with R |
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Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills, 2nd Edition |
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Hackers guide to machine learning with python |
2020 |
Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines |
2020 |
Hands-On Machine Learning with ML.NET: Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C# |
2020 |
Hands-On Machine Learning with ML.NET: Getting started with Microsoft ML.NET to implement popular machine learning algorithms in C# |
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Just Enough R!: An Interactive Approach to Machine Learning and Analytics |
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Learning OpenCV 4 Computer Vision with Python 3: Get to grips with tools, techniques, and algorithms for computer vision and machine learning, 3rd Edition |
2020 |
Machine Learning for IOS Developers |
2020 |
MACHINE LEARNING WITH PYTHON: Step by Step methods to master Machine Learning with Python |
2020 |
Machine Learning with R, Tidyverse, and Mlr |
2020 |
Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques |
2020 |
Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques |
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Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More |
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Artificial Intelligence By Example: Acquire advanced AI, machine learning, and deep learning design skills, 2nd Edition. Code |
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Hands-On Machine Learning with C++: Build, train, and deploy end-to-end machine learning and deep learning pipelines. Code |
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Mastering Computer Vision with TensorFlow 2.x: Build advanced computer vision applications using machine learning and deep learning techniques. Code |
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Learn Data Mining Through Excel: A Step-by-step Approach for Understanding Machine Learning Methods |
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Learn Data Mining Through Excel: A Step-by-Step Approach for Understanding Machine Learning Methods |
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Practical Machine Learning for Data Analysis Using Python |
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Machine Learning in Medicine – A Complete Overview |
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Data Science and Machine Learning: Mathematical and Statistical Methods |
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MATLAB Statistics and Machine Learning Toolbox™ User's Guide |
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Advanced Multi-industry Applications of Big Data Clustering and Machine Learning |
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PYTHON FOR DATA SCIENCE: Guide to computer programming and web coding. Learn machine learning, artificial intelligence, NumPy and Pandas packages for data analysis. Step-by-step exercises included. |
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Machine Learning in Finance: From Theory to Practice |
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Artificial Intelligence and Machine Learning: AI Superpowers and Human+Machine a Visionary Revolution in Finance, Medicine and Business. Find out Top Influent People of the Era with a Modern Approach |
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Machine Learning for Speaker Recognition |
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Approaching (Almost) Any Machine Learning Problem |
2020 |
Artificial Intelligence: The Ultimate Guide to AI, The Internet of Things, Machine Learning, Deep Learning + a Comprehensive Guide to Robotics |
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TensorFlow: Deep Learning and Artificial Intelligence (Machine Learning) |
2020 |
Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications using machine learning techniques |
2020 |
Hands-On Artificial Intelligence for Banking: A practical guide to building intelligent financial applications using machine learning techniques |
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Statistical Machine Learning for Human Behaviour Analysis |
2020 |
AI Self-Driving Cars Divulgement: Practical Advances In Artificial Intelligence And Machine Learning |
2020 |
AI Self-Driving Cars Divulgement: Practical Advances In Artificial Intelligence And Machine Learning |
2020 |
Machine Learning and Cognitive Computing for Mobile Communications and Wireless Networks |
2020 |
Zero to Hero in Machine Learning: Part 1 |
2020 |
Linear Algebra and Optimization for Machine Learning: A Textbook |
2020 |
Linear Algebra and Optimization for Machine Learning: A Textbook |
2020 |
Machine Learning for Beginners. A Complete and Phased Beginner’s Guide to Learning and Understanding Machine Learning and Artificial Intelligence |
2020 |
AI Self-Driving Cars Divulgement: Practical Advances In Artificial Intelligence And Machine Learning |
2020 |
AI Self-Driving Cars Divulgement: Practical Advances In Artificial Intelligence And Machine Learning |
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Approaching (almost) any machine learning problem |
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PYTHON: Learn Coding Programs with Python Programming and Master Data Analysis & Analytics, Data Science and Machine Learning with the Complete Python for Beginners Crash Course - 4 Books in 1 |
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Python for Beginners: The Crash Course to Learn Python Programming in 3-Days (or less). Master Artificial Intelligence for Data Science and Machine Learning + Practical Exercises |
2020 |
Python for Beginners: Start Right Now to Learn Computer Programming with the Best Crash Course. Improve your Skills with Machine Learning, Data Analysis and Data Science |
2019 |
Machine Learning Paradigms |
2019 |
Machine Learning and Iot: A Biological Perspective |
2019 |
Empirical Approach to Machine Learning |
2019 |
Source Separation and Machine Learning |
2019 |
Machine Learning Applications Using Python - Cases Studies from Healthcare, Retail, and Finance |
2019 |
Building Chatbots with Python: Using Natural Language Processing and Machine Learning |
2019 |
Machine Learning Using R: With Time Series and Industry-Based Use Cases in R |
2019 |
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems |
2019 |
Machine Learning with PySpark with Natural Language Processing and Recommender Systems |
2019 |
Machine Learning using R with Time Series and Industry-based Use Cases in R [2nd ed.] |
2019 |
Machine Learning Applications using Python. Cases Studies from Healthcare, Retail and Finance |
2019 |
Multiview Machine Learning |
2019 |
Advances in Neural Computation, Machine Learning, and Cognitive Research II: Selected Papers from the XX International Conference on Neuroinformatics, October 8-12, 2018, Moscow, Russia |
2019 |
Machine Learning Risk Assessments in Criminal Justice Settings |
2019 |
Machine Learning Paradigms: Theory and Application |
2019 |
Dealing with Imbalanced and Weakly Labelled Data in Machine Learning using Fuzzy and Rough Set Methods |
2019 |
Applying Machine Learning for Automated Classification of Biomedical Data in Subject-Independent Settings |
2019 |
Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2018 |
2019 |
Recent Developments in Machine Learning and Data Analytics: IC3 2018 |
2019 |
Social Network Forensics, Cyber Security, and Machine Learning |
2019 |
Machine Learning in Sports: Identifying Potential Archers |
2019 |
Compact and Fast Machine Learning Accelerator for IoT Devices |
2019 |
The Hundred-Page Machine Learning Book |
2019 |
Lie Group Machine Learning |
2019 |
Natural Language Processing Recipes: Unlocking Text Data with Machine Learning and Deep Learning using Python |
2019 |
Machine Learning and AI for Healthcare. Big Data for improved Health Outcomes |
2019 |
Machine Learning Applications Using Python: Cases Studies from Healthcare, Retail, and Finance |
2019 |
Building Chatbots with Python: Using Natural Language Processing and Machine Learning |
2019 |
Machine Learning with PySpark: With Natural Language Processing and Recommender Systems |
2019 |
Beginning Machine Learning in iOS: CoreML Framework |
2019 |
Advanced R Statistical Programming and Data Models: Analysis, Machine Learning, and Visualization |
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The Universal Subject of Our Time: (or: How I Learned to Stop Worrying and Love the Machine) |
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The hundred-page machine learning book |
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Genetic Algorithms and Machine Learning for Programmers: Create AI Models and Evolve Solutions |
2019 |
Python Machine Learning |
2019 |
Python Machine Learning |
2019 |
Machine Learning, Optimization, and Data Science: 4th International Conference, LOD 2018, Volterra, Italy, September 13-16, 2018, Revised Selected Papers |
2019 |
Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes |
2019 |
Practical Machine Learning and Image Processing: For Facial Recognition, Object Detection, and Pattern Recognition Using Python |
2019 |
Beginning Machine Learning in iOS: CoreML Framework |
2019 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part I |
2019 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part II |
2019 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2018, Dublin, Ireland, September 10–14, 2018, Proceedings, Part III |
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Machine Learning in VLSI Computer-Aided Design |
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Keras to Kubernetes: The Journey of a Machine Learning Model to Production |
2019 |
Hands on Machine Learning with Scikit Learn Keras and TensorFlow |
2019 |
Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data |
2019 |
Machine Learning Paradigms: Advances in Data Analytics |
2019 |
Multiview machine learning |
2019 |
Source separation and machine learning |
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Hands-On Java Deep Learning for Computer Vision - Implement machine learning and neural network methodologies to perform computer vision-related tasks. |
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Machine Learning by Tutorials: Beginning Machine Learning for Apple and iOS |
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Machine Learning with Python for Everyone |
2019 |
Machine Learning in Java - Helpful techniques to design, build, and deploy powerful machine learning applications in Java |
2019 |
A global approach to data value maximization : integration, machine learning and multimodal analysis |
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Hands-On Machine Learning with Scikit-Learn, Keras, and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems |
2019 |
Introduction to Algorithms for Data Mining and Machine Learning |
2019 |
An Introduction to Machine Learning |
2019 |
Python for Probability, Statistics, and Machine Learning |
2019 |
Machine Learning for Finance |
2019 |
Applied Machine Learning |
2019 |
Applied Machine Learning |
2019 |
Machine Learning with Microsoft Technologies: Selecting the Right Architecture and Tools for Your Project |
2019 |
Statistical Methods for Machine Learning: Discover How to Transform Data into Knowledge with Python |
2019 |
Intelligent Projects Using Python: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras |
2019 |
Intelligent Projects Using Python: 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras |
2019 |
Applications of Machine Learning in Wireless Communications |
2019 |
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play |
2019 |
Hands-On Unsupervised Learning Using Python: How to Build Applied Machine Learning Solutions from Unlabeled Data |
2019 |
Learn PySpark. Build Python-based Machine Learning and Deep Learning Models |
2019 |
Learn PySpark. Build Python-based Machine Learning and Deep Learning Models |
2019 |
Automated Machine Learning: Methods, Systems, Challenges |
2019 |
Dynamics On and Of Complex Networks III: Machine Learning and Statistical Physics Approaches |
2019 |
Machine Learning Paradigms: Applications of Learning and Analytics in Intelligent Systems |
2019 |
An Introduction to Machine Learning |
2019 |
Machine Learning and Data Mining for Sports Analytics: 5th International Workshop, MLSA 2018, Co-located with ECML/PKDD 2018, Dublin, Ireland, September 10, 2018, Proceedings |
2019 |
From Bioinspired Systems and Biomedical Applications to Machine Learning: 8th International Work-Conference on the Interplay Between Natural and Artificial Computation, IWINAC 2019, Almería, Spain, June 3–7, 2019, Proceedings, Part II |
2019 |
Machine Learning for Networking: First International Conference, MLN 2018, Paris, France, November 27–29, 2018, Revised Selected Papers |
2019 |
Cyber Security Cryptography and Machine Learning: Third International Symposium, CSCML 2019, Beer-Sheva, Israel, June 27–28, 2019, Proceedings |
2019 |
Adversarial Machine Learning |
2019 |
Machine Learning Pocket Reference: Working with Structured Data in Python |
2019 |
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems |
2019 |
Applied machine learning for smart data analysis |
2019 |
Industrial applications of machine learning |
2019 |
Machine learning for computer and cyber security: principles, algorithms, and practices |
2019 |
Machine Learning in Translation Corpora Processing |
2019 |
Machine Learning with R: Expert techniques for predictive modeling |
2019 |
Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control |
2019 |
Machine Learning for Data Mining |
2019 |
Practical Automated Machine Learning on Azure: Using Azure Machine Learning to Quickly Build AI Solutions |
2019 |
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python |
2019 |
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python |
2019 |
Practical Time Series Analysis: Prediction with Statistics and Machine Learning |
2019 |
Practical Data Science with SAP: Machine Learning Techniques for Enterprise Data |
2019 |
Hands-On Artificial Intelligence for IoT: Expert machine learning and deep learning techniques for developing smarter IoT systems |
2019 |
Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play |
2019 |
Artificial Intelligence: What You Need to Know About Machine Learning, Robotics, Deep Learning, Recommender Systems, Internet of Things, Neural Networks, Reinforcement Learning, and Our Future |
2019 |
What’s New in TensorFlow 2.0: Use the new and improved features of TensorFlow to enhance machine learning and deep learning |
2019 |
Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions |
2019 |
Business Data Science: Combining Machine Learning and Economics to Optimize, Automate, and Accelerate Business Decisions |
2019 |
Machine Learning with Spark and Python: Essential Techniques for Predictive Analytic |
2019 |
Mastering Go: Create Golang production applications using network libraries, concurrency, machine learning, and advanced data structures |
2019 |
Practical Time Series Analysis: Prediction with Statistics and Machine Learning |
2019 |
Machine Learning for Cybersecurity Cookbook |
2019 |
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow |
2019 |
Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python |
2019 |
Hands-on Scikit-Learn for Machine Learning Applications: Data Science Fundamentals with Python |
2019 |
Hands-on unsupervised learning with Python : implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more |
2019 |
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 |
2019 |
ROS Robotics Projects: Build And Control Robots Powered By The Robot Operating System, Machine Learning, And Virtual Reality |
2019 |
Python Data Science: The Ultimate Handbook for Beginners on How to Explore NumPy for Numerical Data, Pandas for Data Analysis, IPython, Scikit-Learn and Tensorflow for Machine Learning and Business |
2019 |
Machine Learning for Cybersecurity Cookbook: Over 80 recipes on how to implement machine learning algorithms for building security systems using Python |
2019 |
Mathematics For Machine Learning |
2019 |
Practical Guide for Biomedical Signals Analysis Using Machine Learning Techniques: A MATLAB Based Approach |
2019 |
Python Machine Learning Blueprints: Put your machine learning concepts to the test by developing real-world smart projects |
2019 |
Python Machine Learning, 3rd Edition |
2019 |
Practical Time Series Analysis: Prediction with Statistics and Machine Learning |
2019 |
TinyML: Machine Learning With TensorFlow on Arduino, and Ultra-Low Power Micro-Controllers |
2019 |
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow |
2019 |
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition |
2019 |
Machine Learning Using Python |
2019 |
An Introduction to Machine Learning Interpretability, Second Edition |
2019 |
Linear Algebra for Computer Vision, Robotics, and Machine Learning |
2019 |
Fundamentals of Optimization Theory With Applications to Machine Learning |
2019 |
Building Machine Learning and Deep Learning Models on Google Cloud Platform: A Comprehensive Guide for Beginners |
2019 |
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python |
2019 |
Agile Machine Learning: Effective Machine Learning Inspired by the Agile Manifesto |
2019 |
SQL Server 2019 Revealed: Including Big Data Clusters and Machine Learning |
2019 |
Efficacy Analysis in Clinical Trials an Update: Efficacy Analysis in an Era of Machine Learning |
2019 |
Cause Effect Pairs in Machine Learning |
2019 |
Centrality and Diversity in Search: Roles in A.I., Machine Learning, Social Networks, and Pattern Recognition |
2019 |
Machine Learning and Knowledge Extraction: Third IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conference, CD-MAKE 2019, Canterbury, UK, August 26–29, 2019, Proceedings |
2019 |
Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part II |
2019 |
Artificial Neural Networks and Machine Learning – ICANN 2019: Theoretical Neural Computation: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part I |
2019 |
Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV |
2019 |
Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings |
2019 |
Artificial Neural Networks and Machine Learning – ICANN 2019: Image Processing: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part III |
2019 |
Machine Learning for Cyber Security: Second International Conference, ML4CS 2019, Xi’an, China, September 19-21, 2019, Proceedings |
2019 |
Machine Learning and Intelligent Communications: 4th International Conference, MLICOM 2019, Nanjing, China, August 24–25, 2019, Proceedings |
2019 |
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging and Clinical Image-Based Procedures: First International Workshop, UNSURE 2019, and 8th International Workshop, CLIP 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, Octob |
2019 |
Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Proceedings |
2019 |
OR 2.0 Context-Aware Operating Theaters and Machine Learning in Clinical Neuroimaging: Second International Workshop, OR 2.0 2019, and Second International Workshop, MLCN 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13 and 17, 2019 |
2019 |
Machine Learning and Medical Engineering for Cardiovascular Health and Intravascular Imaging and Computer Assisted Stenting: First International Workshop, MLMECH 2019, and 8th Joint International Workshop, CVII-STENT 2019, Held in Conjunction with MICCAI |
2019 |
Machine Learning for Medical Image Reconstruction: Second International Workshop, MLMIR 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 17, 2019, Proceedings |
2019 |
Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support: Second International Workshop, iMIMIC 2019, and 9th International Workshop, ML-CDS 2019, Held in Conjunction with MICCAI 2019, Shenz |
2019 |
Intelligence Science and Big Data Engineering. Big Data and Machine Learning: 9th International Conference, IScIDE 2019, Nanjing, China, October 17–20, 2019, Proceedings, Part II |
2019 |
Smart Agents for the Industry 4.0: Enabling Machine Learning in Industrial Production |
2019 |
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow |
2019 |
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems |
2019 |
Monetizing Machine Learning: quickly turn python ml ideas into web applications on the ... serverless cloud |
2018 |
Machine learning for the quantified self : on the art of learning from sensory data |
2018 |
Advances in Neural Computation, Machine Learning, and Cognitive Research : Selected Papers from the XIX International Conference on Neuroinformatics, October 2-6, 2017, Moscow, Russia |
2018 |
Introduction to Machine Learning with Applications in Information Security |
2018 |
Granular Computing based Machine Learning. A Big Data Processing Approach |
2018 |
Advances in Soft Computing and Machine Learning in Image Processing |
2018 |
Granular Computing Based Machine Learning: A Big Data Processing Approach |
2018 |
Kinesthetic Perception: A Machine Learning Approach |
2018 |
Machine Learning. A Constraint-based Approach |
2018 |
Practical Machine Learning with Python |
2018 |
Visual Knowledge Discovery and Machine Learning |
2018 |
Basics of Linear Algebra for Machine Learning - Discover the Mathematical Language of Data in Python |
2018 |
Oracle Business Intelligence with Machine Learning : Artificial Intelligence Techniques in OBIEE for Actionable BI |
2018 |
Automatic Tuning of Compilers Using Machine Learning |
2018 |
Machine Learning, Optimization, and Big Data: Third International Conference, MOD 2017, Volterra, Italy, September 14–17, 2017, Revised Selected Papers |
2018 |
Visual Knowledge Discovery and Machine Learning |
2018 |
Machine Learning and Intelligent Communications: Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part II |
2018 |
Machine Learning and Intelligent Communications: Second International Conference, MLICOM 2017, Weihai, China, August 5-6, 2017, Proceedings, Part I |
2018 |
The International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2018) |
2018 |
Enhanced Machine Learning and Data Mining Methods for Analysing Large Hybrid Electric Vehicle Fleets based on Load Spectrum Data |
2018 |
Machine Learning for Model Order Reduction |
2018 |
Advances in Financial Machine Learning |
2018 |
Building Intelligent Systems: A Guide to Machine Learning Engineering |
2018 |
Machine Learning with TensorFlow |
2018 |
Machine Learning for Text |
2018 |
Image Quality Assessment of Computer-generated Images: Based on Machine Learning and Soft Computing |
2018 |
Machine Learning Methods for Behaviour Analysis and Anomaly Detection in Video |
2018 |
Machine Learning for Model Order Reduction |
2018 |
Multisensor Data Fusion and Machine Learning for Environmental Remote Sensing |
2018 |
Machine Learning with TensorFlow |
2018 |
Hands-On Automated Machine Learning |
2018 |
Hands-On Automated Machine Learning |
2018 |
Machine Learning with TensorFlow |
2018 |
Application of FPGA to Real‐Time Machine Learning: Hardware Reservoir Computers and Software Image Processing |
2018 |
Artificial Intelligence: With an Introduction to Machine Learning |
2018 |
Practical Artificial Intelligence. Machine Learning, Bots and Agent Solutions using C# |
2018 |
Artificial intelligence : with an introduction to machine learning |
2018 |
Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent |
2018 |
Machine Learning: a Concise Introduction |
2018 |
Evolutionary Approach to Machine Learning and Deep Neural Networks. Neuro-Evolution and Gene Regulatory Networks |
2018 |
Machine Learning: Step-by-Step Guide To Implement Machine Learning Algorithms with Python |
2018 |
Machine Learning: An Essential Guide to Machine Learning for Beginners |
2018 |
Neural Networks: Deep Learning and Machine Learning Outlined |
2018 |
Machine Learning. A Practical Approach on the Statistical Learning Theory |
2018 |
Scala Machine Learning Projects |
2018 |
Applied Analytics through Case Studies using SAS and R. Implementing Predictive Models and Machine Learning Techniques |
2018 |
Scala Machine Learning Projects (source code) |
2018 |
Scala Machine Learning Projects |
2018 |
Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies |
2018 |
Pro Machine Learning Algorithms |
2018 |
Machine Learning Techniques for Online Social Networks |
2018 |
Cyber Security Cryptography and Machine Learning |
2018 |
Machine Learning for Dynamic Software Analysis: Potentials and Limits |
2018 |
Advances in Machine Learning and Data Science |
2018 |
Introduction to Machine Learning with R: Rigorous Mathematical Analysis |
2018 |
An Introduction to Machine Learning Interpretability |
2018 |
Signal processing and machine learning for biomedical big data |
2018 |
Mastering TensorFlow 1.x: Advanced machine learning and deep learning concepts using TensorFlow 1.x and Keras |
2018 |
Feature Engineering for Machine Learning and Data Analytics |
2018 |
Artificial Intelligence: With an Introduction to Machine Learning |
2018 |
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists |
2018 |
Introduction to Machine Learning with R. Rigorous Mathematical Analysis |
2018 |
Adversarial Machine Learning |
2018 |
Lifelong Machine Learning |
2018 |
Lifelong Machine Learning [2nd ed.] |
2018 |
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing |
2018 |
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud |
2018 |
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing |
2018 |
Applied Natural Language Processing with Python. Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing |
2018 |
Machine Learning for Medical Image Reconstruction: First International Workshop, MLMIR 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings |
2018 |
Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists |
2018 |
Machine Learning and Security: Protecting Systems with Data and Algorithms |
2018 |
Signal Processing and Machine Learning for Brain–Machine Interfaces |
2018 |
Machine Learning Algorithms - Popular algorithms for data science and machine learning |
2018 |
TensorFlow 2 Machine Learning Cookbook |
2018 |
MASTERING MACHINE LEARNING ALGORITHMS. |
2018 |
Applied Text Analysis with Python: Enabling Language-Aware Data Products with Machine Learning |
2018 |
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning |
2018 |
Beginning Apache Spark 2: With Resilient Distributed Datasets, Spark SQL, Structured Streaming and Spark Machine Learning library |
2018 |
Make: Jumpstarting Raspberry Pi Vision: Machine Learning and Facial Recognition on a Single-Board Computer |
2018 |
Machine Learning for Ecology and Sustainable Natural Resource Management |
2018 |
Learn Unity ML-Agents - Fundamentals of Unity Machine Learning |
2018 |
Artificial Intelligence for Business: What You Need to Know about Machine Learning and Neural Networks |
2018 |
Hands-On Automated Machine Learning: A Beginner’s Guide to Building Automated Machine Learning Systems Using AutoML and Python |
2018 |
Algorithmic Aspects of Machine Learning |
2018 |
Source Separation and Machine Learning |
2018 |
Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services |
2018 |
The Deep Learning Revolution: Machine Intelligence Meets Human Intelligence |
2018 |
Big Data and Machine Learning in Quantitative Investment |
2018 |
Machine Learning with AWS |
2018 |
Machine Learning Projects for Mobile Applications |
2018 |
Artificial Intelligence and Machine Learning Fundamentals |
2018 |
Advances in Financial Machine Learning |
2018 |
A Brief Introduction to Machine Learning for Engineers |
2018 |
A Brief Introduction to Machine Learning for Engineers |
2018 |
Practical Java Machine Learning: Projects with Google Cloud Platform and Amazon Web Services |
2018 |
Machine Learning and Intelligent Communications: Third International Conference, MLICOM 2018, Hangzhou, China, July 6-8, 2018, Proceedings |
2018 |
Machine Learning in Medical Imaging: 9th International Workshop, MLMI 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings |
2018 |
Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part I |
2018 |
Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part II |
2018 |
Artificial Neural Networks and Machine Learning – ICANN 2018: 27th International Conference on Artificial Neural Networks, Rhodes, Greece, October 4-7, 2018, Proceedings, Part III |
2018 |
Understanding and Interpreting Machine Learning in Medical Image Computing Applications: First International Workshops, MLCN 2018, DLF 2018, and iMIMIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16-20, 2018, Proceedings |
2018 |
Explainable and Interpretable Models in Computer Vision and Machine Learning |
2018 |
Machine Learning at the Belle II Experiment: The Full Event Interpretation and Its Validation on Belle Data |
2018 |
Braverman Readings in Machine Learning. Key Ideas from Inception to Current State: International Conference Commemorating the 40th Anniversary of Emmanuil Braverman's Decease, Boston, MA, USA, April 28-30, 2017, Invited Talks |
2018 |
Machine Learning and Knowledge Extraction: Second IFIP TC 5, TC 8/WG 8.4, 8.9, TC 12/WG 12.9 International Cross-Domain Conference, CD-MAKE 2018, Hamburg, Germany, August 27–30, 2018, Proceedings |
2018 |
Foundations of Machine Learning |
2018 |
Pragmatic AI: An Introduction to Cloud-Based Machine Learning |
2018 |
Practical Machine Learning with Python : A Problem-Solver’s Guide to Building Real-World Intelligent Systems |
2018 |
Building Intelligent Systems: A Guide to Machine Learning Engineering |
2018 |
Veracity of Big Data: Machine Learning and Other Approaches to Verifying Truthfulness |
2018 |
Pro Machine Learning Algorithms: A Hands-On Approach to Implementing Algorithms in Python and R |
2018 |
Applied Natural Language Processing with Python: Implementing Machine Learning and Deep Learning Algorithms for Natural Language Processing |
2018 |
Monetizing Machine Learning: Quickly Turn Python ML Ideas into Web Applications on the Serverless Cloud |
2018 |
Mastering Machine Learning for Penetration Testing: Develop an extensive skill set to break self-learning systems using Python |
2018 |
Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples |
2018 |
Advanced Data Analytics Using Python: With Machine Learning, Deep Learning and NLP Examples |
2018 |
Machine Methods |
2018 |
Practical Artificial Intelligence: Machine Learning, Bots, and Agent Solutions Using C# |
2018 |
Practical Artificial Intelligence: Machine Learning, Bots, and Agent Solutions Using C# |
2018 |
MATLAB Machine Learning Recipes: A Problem-Solution Approach |
2018 |
Machine Learning Yearning (Draft Version) |
2018 |
Prognostics and health management of electronics: fundamentals, machine learning, and internet of things |
2018 |
Advances in Machine Learning and Data Science: Recent Achievements and Research Directives |
2018 |
Explainable and interpretable models in computer vision and machine learning |
2018 |
Machine Learning: a Practical Approach on the Statistical Learning Theory |
2018 |
Machine learning for text |
2018 |
Machine learning methods for behaviour analysis and anomaly detection in video |
2018 |
Mathematical Analysis For Machine Learning And Data Mining |
2018 |
Hands-On Machine Learning for Algorithmic Trading |
2018 |
Industrial Applications of Machine Learning |
2018 |
Machine Learning for Mobile |
2018 |
Foundations of Machine Learning |
2018 |
Machine Learning for Finance: Data algorithms for the markets and deep learning from the ground up for financial experts and economics |
2018 |
Building Recommender Systems with Machine Learning and AI: Help People Discover New Products and Content with Deep Learning, Neural Networks, and Machine Learning Recommendations |
2018 |
Python Machine Learning: A Guide For Beginners (2nd Edition) |
2018 |
Hands-On Machine Learning for Cybersecurity: Safeguard your system by making your machines intelligent using the Python ecosystem |
2018 |
An Introduction to Machine Learning Interpretability |
2018 |
MASTERING MACHINE LEARNING ALGORITHMS |
2018 |
Machine Learning: a Concise Introduction |
2018 |
Machine learning for OpenCV : advanced methods and deep learning |
2018 |
Machine Learning in Python: Essential Techniques for Predictive Analysis |
2018 |
Machine learning with Python cookbook : practical solutions from preprocessing to deep learning |
2018 |
Hands-on Machine Learning for Cyber Security |
2018 |
Advanced Machine Learning with Python |
2018 |
Thoughtful Machine Learning with Python |
2018 |
Pragmatic AI: an Introduction to Cloud-Based Machine Learning |
2018 |
Introduction to Machine Learning with Python |
2018 |
Building Machine Learning Systems with Python |
2018 |
Real-World Machine Learning |
2018 |
Applied Machine Learning |
2017 |
Data Mining: Practical Machine Learning Tools and Techniques, Fourth Edition |
2017 |
Machine learning: algorithms and applications |
2017 |
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence |
2017 |
Machine Learning Paradigms: Artificial Immune Systems and their Applications in Software Personalization |
2017 |
Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning |
2017 |
Applied Text Analysis with Python: Enabling Language Aware Data Products with Machine Learning |
2017 |
Thoughtful Machine Learning with Python A Test-Driven Approach |
2017 |
Thoughtful Machine Learning with Python: A Test-Driven Approach |
2017 |
Thoughtful Machine Learning with Python: A Test-Driven Approach |
2017 |
Thoughtful Machine Learning with Python: A Test-Driven Approach |
2017 |
Machine Learning Using R |
2017 |
Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2016 |
2017 |
A first course in machine learning |
2017 |
Scala:Applied Machine Learning |
2017 |
Scala:Applied Machine Learning |
2017 |
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems |
2017 |
Reverse Hypothesis Machine Learning |
2017 |
State-Space Approaches for Modelling and Control in Financial Engineering: Systems Theory and Machine Learning Methods |
2017 |
Encyclopedia of Machine Learning and Data Mining |
2017 |
Practical machine learning cookbook |
2017 |
Machine Learning and Intelligent Communications: First International Conference, MLICOM 2016, Shanghai, China, August 27-28, 2016, Revised Selected Papers |
2017 |
Demystifying big data and machine learning for healthcare |
2017 |
Hands-on Machine Learning with Scikit-Learn and TensorFlow |
2017 |
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems |
2017 |
Practical Guide to Cluster Analysis in R. Unsupervised Machine Learning |
2017 |
Practical Guide to Cluster Analysis in R. Unsupervised Machine Learning |
2017 |
Machine Learning made easy with R |
2017 |
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms |
2017 |
TensorFlow Machine Learning Cookbook |
2017 |
Lifelong Machine Learning |
2017 |
Mastering Machine Learning with Python in six Steps |
2017 |
Data Mining. Practical Machine Learning Tools and Techniques |
2017 |
MatLab Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence |
2017 |
MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence |
2017 |
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python |
2017 |
State-Space Approaches for Modelling and Control in Financial Engineering: Systems theory and machine learning methods |
2017 |
Reverse Hypothesis Machine Learning : A Practitioner's Perspective |
2017 |
Cyber Security Cryptography and Machine Learning: First International Conference, CSCML 2017, Beer-Sheva, Israel, June 29-30, 2017, Proceedings |
2017 |
Facial Kinship Verification: A Machine Learning Approach |
2017 |
Machine Learning Made Easy with R. An Intuitive Step by Step Blueprint for Beginners |
2017 |
Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python |
2017 |
Machine Learning. Algorithms and Applications |
2017 |
Augmented Cognition. Neurocognition and Machine Learning: 11th International Conference, AC 2017, Held as Part of HCI International 2017, Vancouver, BC, Canada, July 9-14, 2017, Proceedings, Part I |
2017 |
Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms |
2017 |
Ghosts in the Machine: Rethinking Learning Work and Culture in Air Traffic Control |
2017 |
Thoughtful Machine Learning with Python: A Test-Driven Approach |
2017 |
An Introduction to Machine Learning |
2017 |
An Introduction to Machine Learning |
2017 |
Machine Learning and Knowledge Extraction. First IFIP TC 5, WG 8.4, 8.9, 12.9 International Cross-Domain Conference, CD-MAKE 2017, Reggio, Italy |
2017 |
Machine Learning Algorithms |
2017 |
Statistical Regression and Classification: From Linear Models to Machine Learning |
2017 |
Practical Machine Learning Cookbook |
2017 |
Mastering Machine Learning with Spark 2.x |
2017 |
Machine Learning for the Quantified Self. On the Art of Learning from Sensory Data |
2017 |
Python Machine Learning |
2017 |
Python Machine Learning |
2017 |
Apache Spark 2.x Machine Learning Cookbook |
2017 |
Machine learning and data mining in pattern recognition : 13th International Conference, MLDM 2017, New York, NY, USA, July 15-20, 2017, Proceedings |
2017 |
An Introduction to Machine Learning |
2017 |
Machine learning in medical imaging : 8th International Workshop, MLMI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 10, 2017, Proceedings |
2017 |
Introduction to Machine Learning with Applications in Information Security |
2017 |
MATLAB Deep Learning: With Machine Learning, Neural Networks and Artificial Intelligence |
2017 |
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist |
2017 |
Python Machine Learning Case Studies |
2017 |
Machine Learning for Absolute Beginners |
2017 |
Machine Learning and Cognition in Enterprises. Business Intelligence transformed |
2017 |
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist |
2017 |
Machine Learning and Cognition in Enterprises: Business Intelligence Transformed |
2017 |
Artificial Neural Networks and Machine Learning – ICANN 2017: 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings, Part I |
2017 |
Artificial Neural Networks and Machine Learning – ICANN 2017: 26th International Conference on Artificial Neural Networks, Alghero, Italy, September 11-14, 2017, Proceedings, Part II |
2017 |
Future and Emerging Trends in Language Technology. Machine Learning and Big Data: Second International Workshop, FETLT 2016, Seville, Spain, November 30 –December 2, 2016, Revised Selected Papers |
2017 |
Towards Integrative Machine Learning and Knowledge Extraction: BIRS Workshop, Banff, AB, Canada, July 24-26, 2015, Revised Selected Papers |
2017 |
Machine Learning with R |
2017 |
Machine Learning for OpenCV: Intelligent image processing with Python |
2017 |
Thoughtful Machine Learning with Python: A Test-Driven Approach |
2017 |
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems |
2017 |
Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R |
2017 |
Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark |
2017 |
Statistical Regression and Classification: From Linear Models to Machine Learning |
2017 |
Machine Learning for Decision Makers: Cognitive Computing Fundamentals for Better Decision Making |
2017 |
Machine-learning Techniques in Economics: New Tools for Predicting Economic Growth |
2017 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part II |
2017 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part I |
2017 |
Learning Microsoft Cognitive Services : leverage machine learning APIs to build smart applications |
2017 |
Learning Microsoft Cognitive Services - Second Edition: Leverage Machine Learning APIs to build smart applications |
2017 |
Machine Learning with R Cookbook |
2017 |
Machine learning with Go |
2017 |
Machine Learning with R Cookbook |
2017 |
Machine Learning With Go |
2017 |
Machine learning with Go : (src Code) |
2017 |
Machine Learning With Go |
2017 |
Python Machine Learning Cookbook (Early Release) |
2017 |
Machine Learning for Developers. |
2017 |
Machine Learning for Developers. (Code) |
2017 |
Machine Learning: End-to-End guide for Java developers: Data Analysis, Machine Learning, and Neural Networks simplified |
2017 |
Machine Learning For Beginners: Algorithms, Decision Tree & Random Forest Introduction |
2017 |
Mastering Java Machine Learning |
2017 |
Mastering Java machine learning : mastering and implementing advanced techniques in machine learning |
2017 |
Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn and TensorFlow [source code] |
2017 |
Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn and TensorFlow |
2017 |
An Introduction to Machine Learning |
2017 |
Encyclopedia of Machine Learning and Data Mining |
2017 |
Advances in Soft Computing and Machine Learning in Image Processing |
2017 |
Statistical regression and classification. From linear models to machine learning |
2017 |
Data Science Algorithms in a Week: Top 7 algorithms for computing, data analysis, and machine learning |
2017 |
Machine Learning with R Cookbook |
2017 |
Machine Learning for Developers (source code) |
2017 |
Machine Learning for Developers |
2017 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2017, Skopje, Macedonia, September 18–22, 2017, Proceedings, Part III |
2017 |
Machine Learning with R |
2017 |
Machine Learning with Go |
2017 |
R: Unleash Machine Learning Techniques |
2017 |
Python Machine Learning |
2017 |
Python Machine Learning |
2017 |
Encyclopedia of Machine Learning and Data Mining |
2017 |
Scikit-Learn Cookbook: Over 80 Recipes for Machine Learning in Python With Scikit-Learn |
2017 |
Scikit-Learn Cookbook: Over 80 Recipes for Machine Learning in Python With Scikit-Learn |
2017 |
Scikit-Learn Cookbook: Over 80 Recipes for Machine Learning in Python With Scikit-Learn |
2017 |
Statistics for Machine Learning: Techniques for exploring supervised, unsupervised, and reinforcement learning models with Python and R |
2017 |
Mastering Java Machine Learning |
2017 |
Statistical and Machine-Learning Data Mining, Third Edition: Techniques for Better Predictive Modeling and Analysis of Big Data, Third Edition |
2017 |
Machine Learning Algorithms |
2017 |
Python Natural Language Processing: Advanced machine learning and deep learning techniques for natural language processing |
2017 |
Scala and Spark for Big Data Analytics: Explore the concepts of functional programming, data streaming, and machine learning |
2017 |
Mastering Apache Spark 2.x Scale your machine learning and deep learning systems with SparkML, DeepLearning4j and H2O |
2017 |
Practical Machine Learning with Python: A Problem-Solver’s Guide to Building Real-World Intelligent Systems |
2017 |
Hands-On Data Science and Python Machine Learning |
2017 |
Mastering Machine Learning with scikit-learn: Apply effective learning algorithms to real-world problems using scikit-learn |
2017 |
scikit-learn Cookbook: Over 80 recipes for machine learning in Python with scikit-learn |
2017 |
Machine Learning for OpenCV: Intelligent image processing with Python |
2017 |
Python Machine Learning by Example |
2017 |
Effective Amazon Machine Learning |
2017 |
Mastering Machine Learning with Spark 2.x: Harness the potential of machine learning, through spark |
2017 |
Python Machine Learning By Example: The Easiest Way to Get into Machine Learning |
2017 |
Python Machine Learning Case Studies: Five Case Studies for the Data Scientist |
2017 |
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems |
2017 |
Machine Learning Systems: Designs That Scale |
2017 |
Scala and Spark for Big Data Analytics: Explore the concepts of functional programming, data streaming, and machine learning |
2017 |
Scala and Spark for Big Data Analytics: Explore the concepts of functional programming, data streaming, and machine learning |
2017 |
Statistical and machine-learning data mining: techniques for better predictive modeling |
2017 |
Cloud Computing for Machine Learning and Cognitive Applications |
2017 |
Demystifying Big Data and Machine Learning for Healthcare |
2017 |
Machine Learning for Absolute Beginners |
2017 |
MATLAB Machine Learning |
2017 |
Hands-On Machine Learning with Scikit-Learn and TensorFlow Concepts, Tools, and Techniques to Build Intelligent Systems |
2017 |
Encyclopedia of Machine Learning and Data Mining |
2017 |
Hands-On Machine Learning with Scikit-Learn and TensorFlow |
2017 |
Thoughtful Machine Learning in Python |
2017 |
Machine Learning For Absolute Beginners: A Plain English Introduction |
2017 |
Machine Learning with Spark |
2016 |
Introduction to Machine Learning with Python: A Guide for Data Scientists |
2016 |
Machine Learning Models and Algorithms for Big Data Classification: Thinking with Examples for Effective Learning |
2016 |
Machine Learning in Complex Networks |
2016 |
Machine Learning Techniques for Gait Biometric Recognition: Using the Ground Reaction Force |
2016 |
Machine Learning for Cyber Physical Systems: Selected papers from the International Conference ML4CPS 2015 |
2016 |
Data Mining and Machine Learning in Building Energy Analysis: Towards High Performance Computing |
2016 |
Designing Machine Learning Systems with Python |
2016 |
Designing Machine Learning Systems with Python |
2016 |
Designing Machine Learning Systems with Python |
2016 |
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence |
2016 |
Introduction to Statistical Machine Learning |
2016 |
Medical image recognition, segmentation and parsing : machine learning and multiple object approaches |
2016 |
Machine Learning For Dummies |
2016 |
Designing Machine Learning Systems with Python: Design efficient machine learning systems that give you more accurate results |
2016 |
F# for Machine Learning Essentials: Get up and running with machine learning with F# in a fun and functional way |
2016 |
Introduction to Machine Learning with Python: A Guide for Data Scientists |
2016 |
Learning Proxmox VE: Unleash the power of Proxmox VE by setting up a dedicated virtual environment to serve both containers and virtual machines |
2016 |
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools |
2016 |
Introducing Data Science: Big Data, Machine Learning, and more, using Python tools |
2016 |
Natural Language Processing in Python: Master Data Science and Machine Learning for spam detection, sentiment analysis, latent semantic analysis, and article spinning (Machine Learning in Python) |
2016 |
Convolutional Neural Networks in Python: Master Data Science and Machine Learning with Modern Deep Learning in Python, Theano, and TensorFlow (Machine Learning in Python) |
2016 |
Unsupervised Machine Learning in Python: Master Data Science and Machine Learning with Cluster Analysis, Gaussian Mixture Models, and Principal Components Analysis |
2016 |
Python for Probability, Statistics, and Machine Learning |
2016 |
Advances in Machine Learning and Signal Processing: Proceedings of MALSIP 2015 |
2016 |
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence |
2016 |
Machine Learning for Evolution Strategies |
2016 |
Machine Learning and Data Mining in Pattern Recognition: 12th International Conference, MLDM 2016, New York, NY, USA, July 16-21, 2016, Proceedings |
2016 |
Machine Learning for Microbial Phenotype Prediction |
2016 |
Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python, Theano, and TensorFlow |
2016 |
TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms |
2016 |
TensorFlow for Machine Intelligence: A Hands-On Introduction to Learning Algorithms |
2016 |
Introduction to Machine Learning with Python: A Guide for Data Scientists |
2016 |
Introduction to Machine Learning with Python: A Guide for Data Scientists |
2016 |
Real-World Machine Learning |
2016 |
Real-World Machine Learning |
2016 |
From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence |
2016 |
Introduction to Machine Learning with Python: A Guide for Data Scientists |
2016 |
Machine Learning Control – Taming Nonlinear Dynamics and Turbulence |
2016 |
How Machines Learn: An Illustrated Guide to Machine Learning |
2016 |
Genetic Algorithms Demystified: Unravel the Myths and Power of Genetic Algorithms in Machine Learning |
2016 |
Machine Learning and Medical Imaging |
2016 |
Python Real World Machine Learning |
2016 |
Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks |
2016 |
Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part I |
2016 |
Artificial Neural Networks and Machine Learning – ICANN 2016: 25th International Conference on Artificial Neural Networks, Barcelona, Spain, September 6-9, 2016, Proceedings, Part II |
2016 |
Machine Learning and Interpretation in Neuroimaging: 4th International Workshop, MLINI 2014, Held at NIPS 2014, Montreal, QC, Canada, December 13, 2014, Revised Selected Papers |
2016 |
Availability, Reliability, and Security in Information Systems: IFIP WG 8.4, 8.9, TC 5 International Cross-Domain Conference, CD-ARES 2016, and Workshop on Privacy Aware Machine Learning for Health Data Science, PAML 2016, Salzburg, Austria, August 31 - S |
2016 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part I |
2016 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part III |
2016 |
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2016, Riva del Garda, Italy, September 19-23, 2016, Proceedings, Part II |
2016 |
Machine Learning in Medical Imaging: 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 17, 2016, Proceedings |
2016 |
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) |
2016 |
Deep Learning: Recurrent Neural Networks in Python: LSTM, GRU, and more RNN machine learning architectures in Python and Theano (Machine Learning in Python) |
2016 |
Unsupervised Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python and Theano |
2016 |
Unsupervised Deep Learning in Python: Master Data Science and Machine Learning with Modern Neural Networks written in Python and Theano |
2016 |
Data Mining: Practical Machine Learning Tools and Techniques |
2016 |
Financial signal processing and machine learning |
2016 |
Large Scale Machine Learning with Python |
2016 |
Large Scale Machine Learning with Python |
2016 |
Machine Learning for Cyber Physical Systems |
2016 |
Machine Learning Paradigms Artificial Immune Systems and their Applications in Software Personalization |
2016 |
R: Recipes for Analysis, Visualization and Machine Learning |
2016 |
Financial Signal Processing and Machine Learning |
2016 |
Introducing Data Science: Big Data, Machine Learning and More, Using Python tools |
2016 |
Machine Learning Refined: Foundations, Algorithms, and Applications |
2016 |
Machine Learning Refined: Foundations, Algorithms, and Applications |
2016 |
Machine Learning for Health Informatics: State-of-the-Art and Future Challenges |
2016 |
Practical Machine Learning with H2O: Powerful, Scalable Techniques for Deep Learning and AI |
2016 |
Machine Learning, Optimization, and Big Data: Second International Workshop, MOD 2016, Volterra, Italy, August 26-29, 2016, Revised Selected Papers |
2016 |
Statistical modeling and machine learning for molecular biology |
2016 |
Machine Learning Refined: Foundations, Algorithms, and Applications |
2016 |
Machine Learning. The new AI |
2016 |
R Machine Learning by Example |
2016 |
Machine Learning: The New AI |
2016 |
Mastering .NET Machine Learning |
2016 |
Real-World Machine Learning |
2016 |
Building Machine Learning Projects with TensorFlow |
2016 |
Apache Spark Machine Learning Blueprints |
2016 |
Machine Learning in Java |
2016 |
R: Recipes for Analysis Visualization and Machine Learning |
2016 |
Python Machine Learning Cookbook |
2016 |
Machine Learning for Evolution Strategies |
2016 |
Animal Electricity: How We Learned That the Body and Brain Are Electric Machines |
2016 |
How Machines Learn An Illustrated Guide to Machine Learning |
2016 |
Real-World Machine Learning |
2016 |
Machine Learning in Complex Networks |
2016 |
Python for Probability, Statistics, and Machine Learning |
2016 |
Introduction to Machine Learning with Python: A Guide for Data Scientists |
2016 |
Petascale analytics : large-scale machine learning in the earth sciences |
2016 |
Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques |
2016 |
Large Scale Machine Learning with Spark |
2016 |
Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners |
2016 |
Machine Learning for the Web |
2016 |
Medical image recognition, segmentation and parsing : machine learning and multiple object approaches |
2016 |
Machine Learning: The New AI |
2016 |
Introduction to Statistical Machine Learning |
2016 |
Support Vector Machines and Perceptrons: Learning, Optimization, Classification, and Application to Social Networks |
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From Curve Fitting to Machine Learning: An Illustrative Guide to Scientific Data Analysis and Computational Intelligence |
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Building Machine Learning Projects with TensorFlow |
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R Machine Learning By Example |
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A First Course in Machine Learning |
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Python Machine Learning Blueprints: Intuitive data projects you can relate to |
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Machine Learning Refined: Foundations, Algorithms, and Applications |
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Machine Learning in Java |
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Algorithmic advances in Riemannian geometry and applications : for machine learning, computer vision, statistics, and optimization |
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Python: Python Mastery. "A Code like a Pro" Guide for Python Beginners (Python Programming, Programming For Beginners, Machine Learning, Informatics, Learn ... Fast, Crash Course,(FREE BONUS BOOK)) |
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Python: Python Mastery. "A Code like a Pro" Guide for Python Beginners (Python Programming, Programming For Beginners, Machine Learning, Informatics, Learn ... Fast, Crash Course,(FREE BONUS BOOK)) |
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Practical machine learning : tackle the real-world complexities of modern machine learning with innovative and cutting-edge techniques |
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Advanced Machine Learning with Python |
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R - Unleash Machine Learning Techniques (A course in three modules) |
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Master Machine Learning Algorithms |
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Large Scale Machine Learning with Python |
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Machine Learning for Evolution Strategies |
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Data Mining: Practical Machine Learning Tools and Techniques |
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Data Mining: Practical Machine Learning Tools and Techniques |
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Deep Learning: Adaptive Computation and Machine Learning |
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Introduction to Machine Learning with Python: A Guide for Data Scientists |
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Large Scale Machine Learning with Python |
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Building Machine Learning Projects with TensorFlow |
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Machine Learning in Complex Networks |
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Introduction to Machine Learning with Python A Guide for Data Scientists |
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Python: Real World Machine Learning |
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Deep learning: adaptive computation and machine learning |
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Machine Learning Mastery with Python: Understand Your Data, Create Accurate Models and Work Projects End-To-End |
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Mastering .NET Machine Learning |
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Introducing Data Science : Big data, machine learning, and more, using Python tools |
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Python machine learning blueprints : intuitive data projects you can relate to : an approachable guide to applying advanced machine learning methods to everyday problems |
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Large Scale Machine Learning with Python |
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Machine Learning Paradigms: Artificial Immune Systems and their Applications in Software Personalization |
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Introduction to machine learning with Python a guide for data scientists |