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Free Download Machine Learning for Beginners: A Complete Guide to Supervised and Unsupervised Learning with Python: Master Regression, Classification, Decision Trees, ... Series - Learn. Build. Master. Book 9)
English | November 16, 2025 | ASIN: B0G2K5C9X1 | 417 pages | Epub | 11.17 MB
Master Machine Learning and Build Production-Ready AI Models with Python Machine Learning for Beginners is your comprehensive guide to building real-world AI systems using industry-standard tools. This book bridges theory and practice, teaching you to develop, evaluate, and deploy machine learning models professionally. What's Inside Learn machine learning fundamentals including supervised and unsupervised learning, proper model evaluation, and the iterative mindset essential for success. Master regression techniques from linear models through advanced regularization methods including Ridge, Lasso, and ElasticNet for feature selection and handling non-linear patterns. Progress to classification algorithms including logistic regression with probability outputs, decision trees with visual interpretability, random forests demonstrating ensemble learning power, and XGBoost with competition-winning techniques. Explore unsupervised learning through K-Means clustering for customer segmentation and Principal Component Analysis for dimensionality reduction. Develop professional practices including systematic model comparison, hyperparameter tuning with grid and random search, and complete end-to-end project workflows from business problem through deployment with documentation. Practical Projects Included Build house price predictors, customer churn classifiers, fraud detection systems, sales forecasters, customer segmentation models, and a portfolio-ready employee attrition prediction system with deployment code and professional documentation. Industry-Standard Tools Master scikit-learn, XGBoost, Pandas, NumPy, MatDescriptionlib, and Seaborn. All code runs in Jupyter Notebooks, Google Colab, or local Python environments. Complete GitHub repository included. Who This Book Is For Aspiring data scientists, analysts expanding technical skills, software developers adding ML capabilities, and professionals wanting to understand AI applications. Requires basic Python knowledge. No advanced mathematics needed. Unique Approach Each concept includes intuitive explanations before mathematics, complete working code, real-world business context, visual demonstrations, and common pitfall warnings. Learn proper evaluation metrics, systematic algorithm selection, feature engineering, deployment strategies, and professional documentation practices. Address practical challenges including missing values, imbalanced classes, data leakage prevention, feature scaling, and production deployment. Understand not just how algorithms work, but when and why to use each technique. Career Development Includes guidance on data scientist versus ML engineer roles, portfolio building with GitHub best practices, Kaggle competition strategies, interview preparation, and career pathways in this rapidly growing field. What You'll Achieve Fundamental machine learning skills applicable across industries, portfolio projects demonstrating capabilities, systematic model development approaches, understanding of algorithm selection, and confidence to explore advanced topics including deep learning and natural language processing. Machine learning expertise opens doors to high-demand careers in data science, artificial intelligence, and business analytics with median salaries exceeding six figures. This book provides the practical foundation for professional success. Start building production-ready machine learning models today.

Free Download Machine Learning and Mixed Reality for the Enhancement of Cultural Heritage: The Monastery of Saints Severino and Sossio Case Study by Maurizio Perticarini
English | October 1, 2024 | ISBN: 3031712862 | 106 pages | MOBI | 30 Mb
This book addresses the role of modern surveying and representation technologies in preserving and disseminating cultural heritage. A workflow is illustrated, describing the Former Monastery of Ss Severino and Sossio case study, currently the headquarters of the State Archives of Naples, Italy. After offering a historical overview, the work examines the spaces and structure of the building. A methodology for three-dimensional restitution is presented, using low-cost image-based and professional range-based surveying, concluding with recent AI technologies such as NeRF. The research continues with the virtual and augmented restitution of parts of the building that have been modified, lost over the centuries, or are no longer accessible. The Atrio dei Marmi, the Atrio del Platano, and the Sala del Capitolo and Sala del Refettorio are some of the places where the research has focused, creating a BIM model, using AR for precise interventions, and developing an immersive applied game to understand the third level of the monastery, rich in works of art and today also serving as a museum. In the final chapters, a particular focus is placed on the future of representation: new techniques, ongoing developments in AI supporting surveying, and the new possibilities offered by virtual spaces.

Free Download Machine Learning Mastery: From Data To Decisions
English | December 22, 2025 | ASIN: B0GBYTJ3VZ | 122 pages | Epub | 1.20 MB
AI Insights Series Volume 2: Machine Learning Mastery - From Data to Decisions By James Head Book Description Building directly on the foundational knowledge from Volume 1, Machine Learning Mastery - From Data to Decisions takes you from curious beginner to confident practitioner. This hands-on volume demystifies the complete machine learning workflow, guiding you step-by-step through the process of turning raw data into accurate, actionable predictions and insights. Whether you're a business professional seeking to leverage data-driven decisions, a student preparing for a career in tech, or a developer ready to build your first real models, this book equips you with practical skills and deep understanding-no advanced mathematics degree required. What You'll Learn The Full Machine Learning Pipeline : Explore every stage from data collection and cleaning to model training, evaluation, and deployment. Learn why each step matters and how mistakes in one stage can cascade through the entire process. Data Preparation Mastery : Discover proven techniques for handling missing values, outliers, feature engineering, scaling, and encoding categorical variables-the often-overlooked steps that separate mediocre models from outstanding ones. Core Algorithms in Depth : Gain intuitive understanding (backed by clear visuals and real-world examples) of essential algorithms including linear regression, logistic regression, decision trees, random forests, support vector machines, k-nearest neighbors, and clustering methods like k-means. Model Evaluation and Validation : Master metrics such as accuracy, precision, recall, F1-score, ROC-AUC, and confusion matrices. Learn cross-validation, train-test splits, and how to detect and prevent overfitting and underfitting. Practical Projects : Build and deploy complete models with guided, end-to-end projects including: Predicting house prices with regression Customer churn classification Credit risk assessment Market segmentation using clustering All projects include downloadable datasets and complete Python code. Tools and Best Practices : Work fluently with industry-standard libraries-Pandas for data manipulation, Scikit-learn for modeling, MatDescriptionlib and Seaborn for visualization-while learning workflow tips used by professional data scientists. Real-World Case Studies : See how companies like Netflix, Amazon, and healthcare providers apply these techniques to recommendation systems, fraud detection, personalized medicine, and inventory optimization. Written in the same accessible style as Volume 1, this book continues to use everyday analogies, avoids unnecessary jargon, and includes diagrams, code snippets, quizzes, and companion resources. By the end, you'll not only understand how machine learning works-you'll be able to apply it to solve real problems in your own domain. Perfect for readers who have completed Volume 1 or have basic familiarity with AI concepts and introductory Python. Ready to turn data into decisions? Volume 2 gives you the mastery you need to make it happen.

Free Download Machine Learning Engineering with Python: Build, Deploy, and Scale Real-World ML Systems with MLOps, Cloud Pipelines, and Production-Ready AI Solutions
English | November 17, 2025 | ASIN: B0G2CZ25BQ | 328 pages | Epub | 1.58 MB
Machine Learning Engineering with Python: Build, Deploy, and Scale Real-World ML Systems with MLOps, Cloud Pipelines, and Production-Ready AI Solutions This book gives you a practical roadmap for turning machine learning ideas into reliable, scalable, and production-ready systems. It guides you through the entire ML engineering lifecycle from data pipelines and model development to deployment, monitoring, scaling, and optimization using modern Python tools and proven MLOps practices. You explore how real-world AI systems work behind the scenes and learn how to build your own using cloud platforms, automation pipelines, and best-in-class engineering techniques. Designed for clarity and real-world relevance, this book shows you how to bridge the gap between experimentation and production. You move beyond notebooks and learn how to create ML solutions that run efficiently, scale seamlessly, and deliver consistent value. Summary You discover how to use Python, cloud services, CI/CD workflows, feature stores, orchestration frameworks, and containerized deployments to build robust machine learning systems. The book highlights the patterns used by experienced ML engineers, explains the pitfalls that often break production models, and provides the tools you need to design secure, efficient, and maintainable ML pipelines. Whether you're deploying models to the cloud, serving predictions in real time, or optimizing inference at scale, you gain the confidence to engineer solutions that meet real business needs. Key Features of This Book Covers the full ML engineering lifecycle from dataset design to scalable deployment Shows how to build end-to-end pipelines with MLOps tools and cloud platforms Explains real-world techniques for monitoring, observability, and continuous retraining Includes guidance on securing ML APIs, managing model lineage, and ensuring compliance Offers practical insights from real production environments Helps you understand both batch and streaming systems at scale Presents complex concepts in a simple, conversational, and SEO-optimized style This book is ideal for ML engineers, data scientists, software engineers, DevOps professionals, and students who want to master production-grade machine learning. If you're moving from experimentation to real deployments or aiming to design scalable, reliable AI systems this book gives you the structure and clarity you need. Beginners with Python experience and professionals seeking to upgrade their MLOps skills will also benefit greatly. If you're ready to build machine learning systems that perform reliably in the real world, scale effortlessly, and deliver measurable impact, this book is your complete guide. Take the next step in your ML engineering journey start reading today and learn how to transform models into production-ready AI solutions. Read more

Free Download Machine Learning: A Comprehensive Beginner's Guide by Akshay B R, Sini Raj Pulari, T S Murugesh
English | July 15, 2024 | ISBN: 1032676655 | 248 pages | EPUB | 51 Mb
Machine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to master machine learning concepts with the step-by-step approach outlined in this book and contribute to its exciting future.

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Dl4All is download free for all software and educational resource. With a little searching, you can download what you need for free without spending any money.

Dl4All - Download free For All: software and educational resource.

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Machine Learning for Beginners A Complete Guide to Supervised and Unsupervised Learning with Python Master Regression,

Machine Learning and Mixed Reality for the Enhancement of Cultural Heritage

Machine Learning and Deep Learning Using Python and TensorFlow

Machine Learning Mastery From Data To Decisions

Machine Learning Fundamentals Concepts, Models, and Applications

Machine Learning For Dummies, 3rd Edition

Machine Learning Engineering with Python Build, Deploy, and Scale Real-World ML Systems with MLOps, Cloud Pipelines

Machine Learning A Comprehensive Beginner's Guide