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Machine Learning
Welcome to the World of Machine Learning
Machine Learning, a subset of artificial intelligence, focuses on the development of algorithms that enable computers to learn from and act on data. It's a rapidly evolving field that is transforming industries worldwide. At Refhub.ir, we offer a comprehensive collection of machine learning books to cater to everyone from beginners to seasoned professionals.
Why Explore Machine Learning Books?
Described as one of the most exciting technologies of our time, Machine Learning is at the forefront of technological evolution. Reading books in this field not only equips you with foundational knowledge but also keeps you updated with the latest advancements and methodologies. Our carefully curated selection of books covers a wide range of topics including predictive analytics, neural networks, deep learning, and natural language processing.
Books for Every Level
Whether you're just starting out or looking to deepen your understanding of complex algorithms, our Machine Learning category has something for you.
Beginners can dive into introductory books that break down the basics of ML models, data processing, and essential algorithms in an easy-to-understand manner. Advanced learners and specialists will appreciate technical books that delve into sophisticated machine learning models and techniques, practical case studies, and real-world applications.
Key Topics Covered
Our collection focuses on both foundational concepts and cutting-edge developments in Machine Learning.
- Supervised and Unsupervised Learning
- Neural Networks and Deep Learning
- Data Preprocessing Techniques
- Reinforcement Learning
- Machine Learning for Big Data
- Natural Language Processing and Computer Vision
- Ethical Considerations and Bias in Machine Learning
Benefits of Learning Machine Learning
Mastering machine learning can unlock new career opportunities and enhance your data-driven decision-making skills. Learning from our selection of books will help you to:
- Understand and apply machine learning algorithms to solve real-world problems.
- Develop and deploy ML models effectively.
- Harness the power of data for strategic insights.
- Stay updated with the latest technological trends and innovations.
Enhance Your Learning Experience
Our online platform is designed to enhance your book-browsing experience with user-friendly features and intuitive categorization. With dedicated support and periodic updates to our catalog, Refhub.ir is your ideal partner in mastering machine learning.
Stay Ahead in Your Field
The field of Machine Learning is rapidly evolving, and staying ahead of the curve is essential. Whether your interest lies in theoretical concepts or practical applications, our diverse selection of books offers the resources you need to excel.
Embrace the power of learning and equip yourself with the knowledge to thrive in today's digital world. Explore our Machine Learning category and make your learning journey both insightful and enriching.
Books
608 resultsMatLab Deep Learning with Machine Learning, Neural Networks and Artificial Intelligence
Phil Kim
Visual Data Mining: Theory, Techniques and Tools for Visual Analytics
Simeon J. Simoff,Michael H. Böhlen,Arturas Mazeika (auth.),Simeon J. Simoff,Michael H. Böhlen,Arturas Mazeika (eds.)
Advanced Data Analytics Using Python with Machine Learning, Deep Learning and NLP Examples
Sayan Mukhopadhyay
Graph-Powered Analytics and Machine Learning with TigerGraph: Driving Business Outcomes with Connected Data
Victor Lee,Phuc Kien Nguyen,Alexander Thomas
Mathematics of Machine Learning: Lecture Notes
Prof. Philippe Rigollet
An Introduction to Statistical Learning: with Applications in Python (Springer Texts in Statistics)
Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Jonathan Taylor
Practical Machine Learning in JavaScript: TensorFlow.js for Web Developers
Charlie Gerard
Deep Learning with Python: A Hands-on Introduction
Nikhil Ketkar (auth.)
Large Scale Machine Learning with Python
Bastiaan Sjardin,Luca Massaron,Alberto Boschetti
Pattern Classification: Neuro-fuzzy Methods and Their Comparison
Shigeo Abe DrEng (auth.)
Building Machine Learning Systems with Python, 2nd Edition: Get more from your data through creating practical machine learning systems with Python
Luis Pedro Coelho,Willi Richert
Keras to Kubernetes: The Journey of a Machine Learning Model to Production
Dattaraj Rao
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Bharath Ramsundar,Peter Eastman,Patrick Walters,Vijay Pande
Data Science in the Cloud: with Microsoft Azure Machine Learning and R
Stephen F. Elston
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning Series)
Lise Getoor;Ben Taskar
The Psychology of Artificial Superintelligence (Cognitive Systems Monographs, 42)
Joachim Diederich
GANs in Action: Deep learning with Generative Adversarial Networks
Jakub Langr,Vladimir Bok
Scaling Machine Learning with Spark: Distributed ML with MLlib, TensorFlow, and PyTorch
Adi Polak
Data Mining: Practical Machine Learning Tools and Techniques, Third Edition
Ian H. Witten,Eibe Frank,Mark A. Hall
Artificial Intelligence, Machine Learning and Blockchain in Quantum Satellite, Drone and Network
Thiruselvan Subramanian,Archana Dhyani,Adarsh Kumar,Sukhpal Singh Gill
Hands-on data science and Python machine learning : perform data mining and machine learning efficiently using Python and Spark
Frank Kane
Beginning Apache Spark 3: With DataFrame, Spark SQL, Structured Streaming, and Spark Machine Learning Library
Hien Luu
Next-Generation Machine Learning with Spark: Covers XGBoost, LightGBM, Spark NLP, Distributed Deep Learning with Keras, and More
Butch Quinto
Practical Machine Learning with Spark: Uncover Apache Spark’s Scalable Performance with High-Quality Algorithms Across NLP, Computer Vision and ML(English Edition)
Gourav Gupta,Dr. Manish Gupta,Dr. Inder Singh Gupta
Python Natural Language Processing: Advanced machine learning and deep learning techniques for natural language processing
Jalaj Thanaki
Metaheuristics in Machine Learning: Theory and Applications (Studies in Computational Intelligence, 967)
Diego Oliva (editor),Essam H. Houssein (editor),Salvador Hinojosa (editor)
Quantum Machine Learning and Optimisation in Finance: On the Road to Quantum Advantage
Antoine Jacquier,Oleksiy Kondratyev
International Journal of Machine Learning and Cybernetics
Wang, Xizhao; Zhao, Yanxia; Pourpanah, Farhad
Knowledge Graph and Semantic Computing: Knowledge Graph Empowers New Infrastructure Construction: 6th China Conference, CCKS 2021, Guangzhou, China, ... in Computer and Information Science)
Bing Qin (editor),Zhi Jin (editor),Haofen Wang (editor),Jeff Pan (editor),Yongbin Liu (editor),Bo An (editor)
Predictive Analytics with Microsoft Azure Machine Learning
Barga, Roger.;Fontama, Valentine.;Tok, Wee Hyong
Hands-on TinyML: Harness the power of Machine Learning on the edge devices
Rohan Banerjee
Applied Neural Networks with TensorFlow 2: API Oriented Deep Learning with Python
Orhan Gazi Yalçın