Machine Learning Hero
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Machine Learning Hero
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-16
Machine Learning Hero written by Cuantum Technologies LLC and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-16 with Computers categories.
Learn machine learning through hands-on Python projects, covering core concepts, essential libraries, and real-world applications for aspiring data scientists. Key Features Comprehensive coverage of machine learning fundamentals and advanced topics Real-world projects to apply skills in practical scenarios Integration of Python libraries for data science and AI development Book DescriptionThis book takes you on a journey through the world of machine learning, beginning with foundational concepts such as supervised and unsupervised learning, and progressing to advanced topics like feature engineering, hyperparameter tuning, and dimensionality reduction. Each chapter blends theory with practical exercises to ensure a deep understanding of the material. The book emphasizes Python, introducing essential libraries like NumPy, Pandas, Matplotlib, and Scikit-learn, along with deep learning frameworks like TensorFlow and PyTorch. You’ll learn to preprocess data, visualize insights, and build models capable of tackling complex datasets. Hands-on coding examples and exercises reinforce concepts and help bridge the gap between knowledge and application. In the final chapters, you'll work on real-world projects like predictive analytics, clustering, and regression. These projects are designed to provide a practical context for the techniques learned and equip you with actionable skills for data science and AI roles. By the end, you'll be prepared to apply machine learning principles to solve real-world challenges with confidence.What you will learn Build machine learning models using Python libraries Apply feature engineering and preprocessing techniques Visualize datasets with Matplotlib and Seaborn Optimize machine learning models with hyperparameter tuning Implement clustering and dimensionality reduction methods Work on real-world projects for practical experience Who this book is for Aspiring data scientists, software developers, and tech enthusiasts seeking to master machine learning concepts and Python libraries. Basic Python knowledge is recommended but not required, as foundational topics are covered.
Machine Learning Hero
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Author : Cuantum Technologies
language : en
Publisher: Staten House
Release Date : 2024-10-07
Machine Learning Hero written by Cuantum Technologies and has been published by Staten House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-07 with Computers categories.
This Book grants Free Access to our e-learning Platform, which includes: ✅ Free Repository Code with all code blocks used in this book ✅ Access to Free Chapters of all our library of programming published books ✅ Free premium customer support ✅ Much more... Become a Machine Learning Hero and Master Data Science with Python In a world driven by data, mastering machine learning is your key to unlocking new opportunities and solving complex problems. Whether you're a beginner or a professional looking to sharpen your skills, Machine Learning Hero: Master Data Science with Python Essentials is your guide to becoming proficient in machine learning and data science. This book provides a step-by-step journey into data science using Python, giving you the tools and knowledge to solve real-world problems confidently. What You Will Learn Master Python for Data Science Learn how to use Python and essential data science libraries such as NumPy, Pandas, Matplotlib, Seaborn, and Scikit-learn to manipulate, visualize, and analyze data efficiently. Prepare Data for Machine Learning Explore techniques for cleaning, transforming, and preparing raw data, including handling missing values, feature scaling, and encoding categorical variables to ensure your models are accurate and reliable. Build Classical Machine Learning Models Understand and implement popular machine learning algorithms such as: Linear and Polynomial Regression for predicting continuous outcomes. Classification algorithms like Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), and Decision Trees to categorize data. K-Means Clustering for discovering patterns in data without labels. Master Feature Engineering Learn the art of feature engineering, transforming raw data into features that improve model performance. Feature engineering is key to building effective machine learning models that produce meaningful results. Hands-On Projects to Solidify Learning Put theory into practice with hands-on projects designed to help you apply machine learning techniques in real-world scenarios: Predict Car Prices: Use linear regression to estimate car prices based on factors like mileage, year, and make. Customer Segmentation: Use K-Means clustering to group customers by behavior, helping businesses understand their clients better. Classification Models: Apply classification algorithms to predict survival on the Titanic, using precision, recall, and AUC-ROC metrics to evaluate performance. These projects reinforce the key concepts and ensure you gain the practical experience needed to tackle real-world data challenges. Who Is This Book For? Aspiring data scientists wanting to build a solid foundation in machine learning. Python developers looking to expand their skillset into data science. Professionals seeking to use machine learning to solve problems and make data-driven decisions. Whether you're just getting started or aiming to improve your machine learning skills, this book provides everything you need to become a machine learning hero. Start Your Journey Now With Machine Learning Hero: Master Data Science with Python Essentials, you'll be equipped to handle real-world data challenges and unlock the potential of machine learning across any industry. Are you ready to transform your data into actionable insights and become a machine learning hero?
Zero To Machine Learning Hero
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Author : Husn Ara
language : en
Publisher: Independently Published
Release Date : 2024-03-26
Zero To Machine Learning Hero written by Husn Ara and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-26 with Computers categories.
Welcome to "Zero to Machine Learning Hero: Python Edition"! Whether you're a complete novice or an experienced programmer looking to delve into the exciting world of machine learning, this book is designed to be your comprehensive guide on your journey to becoming a machine learning hero using Python. Machine learning has emerged as one of the most transformative technologies of our time, revolutionizing industries ranging from healthcare and finance to transportation and entertainment. With its ability to extract insights from data and make predictions based on patterns, machine learning has the power to drive innovation and solve complex problems in diverse domains. In this book, we'll start from the very basics and gradually build up your skills, equipping you with the knowledge and tools necessary to tackle real-world machine learning challenges. Whether you're interested in building predictive models, analysing data, or implementing cutting-edge algorithms, we'll cover everything you need to know to get started and thrive in the field of machine learning. Throughout the chapters, we'll leverage the power of Python, one of the most popular programming languages for machine learning and data science. Python's simplicity, versatility, and extensive ecosystem of libraries make it an ideal choice for both beginners and seasoned professionals alike. Here's what you can expect to learn from this book: Fundamentals of machine learning, including key concepts, terminology, and types of learning algorithms. Practical techniques for data preprocessing, feature engineering, and model evaluation. Hands-on experience with popular Python libraries such as NumPy, Pandas, Matplotlib, and Scikit-learn. Building and training machine learning models for classification, regression, clustering, and more. Advanced topics such as deep learning, natural language processing, and reinforcement learning. Whether you're a student, a data enthusiast, or a professional looking to advance your career, "From Zero to Machine Learning Hero: Python Edition" will provide you with the knowledge, confidence, and practical skills to succeed in the exciting field of machine learning. So, are you ready to embark on this journey? Let's dive in and unlock the secrets of machine learning with Python! Happy learning! [Husn Ara] Data Science Professional 11+ years of Experience IIITB Bangalore Data Science Alumni
Zero To Hero In Machine Learning
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Author : Ravindu Perera
language : en
Publisher:
Release Date : 2020-05-20
Zero To Hero In Machine Learning written by Ravindu Perera and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-05-20 with categories.
Machine Learning is just not a topic for computer science geeks. There's no area that ML can't be applied. These days ML is used in banking, astronomy, medicare, transportation, agriculture, fashion designing, IoT and robotics sector etc. So there's no limit for ML applications and this is still one of the main research areas. So there will be so many improvements in the near future.You don't need any previous ML knowledge to refer this book because this book targets every beginner in ML and Data Science domain such as enthusiastic kids, school ICT students, university students, business analysts, business developers and marketing officers.In "Zero to Hero in Machine Learning" book, we learn*What ML is*ML types*ML algorithms*How to use Jupyter Notebook for coding and Azure Notebooks*How to work with images and a little bit in computer vision*The step by step procedure to train a machine*Data science techniques*How some regression and classification algorithms really works*To train a machine by coding in Azure Notebooks.*How to improve your machine learning and data science skills
Managing Data Integrity For Finance
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Author : Jane Sarah Lat
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-01-31
Managing Data Integrity For Finance written by Jane Sarah Lat and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-31 with Computers categories.
Level up your career by learning best practices for managing the data quality and integrity of your financial data Key Features Accelerate data integrity management using artificial intelligence-powered solutions Learn how business intelligence tools, ledger databases, and database locks solve data integrity issues Find out how to detect fraudulent transactions affecting financial report integrity Book DescriptionData integrity management plays a critical role in the success and effectiveness of organizations trying to use financial and operational data to make business decisions. Unfortunately, there is a big gap between the analysis and management of finance data along with the proper implementation of complex data systems across various organizations. The first part of this book covers the important concepts for data quality and data integrity relevant to finance, data, and tech professionals. The second part then focuses on having you use several data tools and platforms to manage and resolve data integrity issues on financial data. The last part of this the book covers intermediate and advanced solutions, including managed cloud-based ledger databases, database locks, and artificial intelligence, to manage the integrity of financial data in systems and databases. After finishing this hands-on book, you will be able to solve various data integrity issues experienced by organizations globally.What you will learn Develop a customized financial data quality scorecard Utilize business intelligence tools to detect, manage, and resolve data integrity issues Find out how to use managed cloud-based ledger databases for financial data integrity Apply database locking techniques to prevent transaction integrity issues involving finance data Discover the methods to detect fraudulent transactions affecting financial report integrity Use artificial intelligence-powered solutions to resolve various data integrity issues and challenges Who this book is for This book is for financial analysts, technical leaders, and data professionals interested in learning practical strategies for managing data integrity and data quality using relevant frameworks and tools. A basic understanding of finance concepts, accounting, and data analysis is expected. Knowledge of finance management is not a prerequisite, but it’ll help you grasp the more advanced topics covered in this book.
Intelligent Systems And Applications
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Author : Kohei Arai
language : en
Publisher: Springer Nature
Release Date : 2022-08-31
Intelligent Systems And Applications written by Kohei Arai and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-31 with Technology & Engineering categories.
This book is a remarkable collection of chapters covering a wide domain of topics related to artificial intelligence and its applications to the real world. The conference attracted a total of 494 submissions from many academic pioneering researchers, scientists, industrial engineers, and students from all around the world. These submissions underwent a double-blind peer-reviewed process. Of the total submissions, 176 submissions have been selected to be included in these proceedings. It is difficult to imagine how artificial intelligence has become an inseparable part of our life. From mobile phones, smart watches, washing machines to smart homes, smart cars, and smart industries, artificial intelligence has helped to revolutionize the whole globe. As we witness exponential growth of computational intelligence in several directions and use of intelligent systems in everyday applications, this book is an ideal resource for reporting latest innovations and future of AI. Distinguished researchers have made valuable studies to understand the various bottlenecks existing in different arenas and how they can be overcome with the use of intelligent systems. This book also provides new directions and dimensions of future research work. We hope that readers find the volume interesting and valuable.
Generative Ai On Aws
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Author : Chris Fregly
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2023-11-13
Generative Ai On Aws written by Chris Fregly and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-13 with Computers categories.
Companies today are moving rapidly to integrate generative AI into their products and services. But there's a great deal of hype (and misunderstanding) about the impact and promise of this technology. With this book, Chris Fregly, Antje Barth, and Shelbee Eigenbrode from AWS help CTOs, ML practitioners, application developers, business analysts, data engineers, and data scientists find practical ways to use this exciting new technology. You'll learn the generative AI project life cycle including use case definition, model selection, model fine-tuning, retrieval-augmented generation, reinforcement learning from human feedback, and model quantization, optimization, and deployment. And you'll explore different types of models including large language models (LLMs) and multimodal models such as Stable Diffusion for generating images and Flamingo/IDEFICS for answering questions about images. Apply generative AI to your business use cases Determine which generative AI models are best suited to your task Perform prompt engineering and in-context learning Fine-tune generative AI models on your datasets with low-rank adaptation (LoRA) Align generative AI models to human values with reinforcement learning from human feedback (RLHF) Augment your model with retrieval-augmented generation (RAG) Explore libraries such as LangChain and ReAct to develop agents and actions Build generative AI applications with Amazon Bedrock
97 Things Every Cloud Engineer Should Know
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Author : Emily Freeman
language : en
Publisher: O'Reilly Media
Release Date : 2020-12-04
97 Things Every Cloud Engineer Should Know written by Emily Freeman and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-04 with Computers categories.
If you create, manage, operate, or configure systems running in the cloud, you're a cloud engineer--even if you work as a system administrator, software developer, data scientist, or site reliability engineer. With this book, professionals from around the world provide valuable insight into today's cloud engineering role. These concise articles explore the entire cloud computing experience, including fundamentals, architecture, and migration. You'll delve into security and compliance, operations and reliability, and software development. And examine networking, organizational culture, and more. You're sure to find 1, 2, or 97 things that inspire you to dig deeper and expand your own career. "Three Keys to Making the Right Multicloud Decisions," Brendan O'Leary "Serverless Bad Practices," Manases Jesus Galindo Bello "Failing a Cloud Migration," Lee Atchison "Treat Your Cloud Environment as If It Were On Premises," Iyana Garry "What Is Toil, and Why Are SREs Obsessed with It?", Zachary Nickens "Lean QA: The QA Evolving in the DevOps World," Theresa Neate "How Economies of Scale Work in the Cloud," Jon Moore "The Cloud Is Not About the Cloud," Ken Corless "Data Gravity: The Importance of Data Management in the Cloud," Geoff Hughes "Even in the Cloud, the Network Is the Foundation," David Murray "Cloud Engineering Is About Culture, Not Containers," Holly Cummins
Advances In Hardware Design For Security And Trust
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Author : Ranga Vemuri
language : en
Publisher: CRC Press
Release Date : 2025-09-30
Advances In Hardware Design For Security And Trust written by Ranga Vemuri and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-30 with Computers categories.
This book addresses various electronics supply-chain vulnerabilities, attack methods that exploit these vulnerabilities, and design techniques to mitigate the vulnerabilities while defending against the attacks. This book covers the entire spectrum of electronic hardware design including integrated circuits, embedded systems, and design automation tools. Advances in Hardware Design for Security and Trust offers self-contained tutorials within each chapter, as well as a presentation of recent advances. The relevance of each method in the context of the overall design and fabrication process is clearly articulated. Both qualitative analysis and quantitative experimental results to evaluate the significance of methods are presented. Both side-channel methods as well as front-channel techniques are covered. The authors emphasize methods that are ready for technology transition and commercialization. This book is intended for both researchers and industry practitioners. They will benefit from the tutorial style exposition of the topics along with advanced research results and emerging directions.
97 Things Every Data Engineer Should Know
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Author : Tobias Macey
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2021-06-11
97 Things Every Data Engineer Should Know written by Tobias Macey and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-11 with Computers categories.
Take advantage of today's sky-high demand for data engineers. With this in-depth book, current and aspiring engineers will learn powerful real-world best practices for managing data big and small. Contributors from notable companies including Twitter, Google, Stitch Fix, Microsoft, Capital One, and LinkedIn share their experiences and lessons learned for overcoming a variety of specific and often nagging challenges. Edited by Tobias Macey, host of the popular Data Engineering Podcast, this book presents 97 concise and useful tips for cleaning, prepping, wrangling, storing, processing, and ingesting data. Data engineers, data architects, data team managers, data scientists, machine learning engineers, and software engineers will greatly benefit from the wisdom and experience of their peers. Topics include: The Importance of Data Lineage - Julien Le Dem Data Security for Data Engineers - Katharine Jarmul The Two Types of Data Engineering and Data Engineers - Jesse Anderson Six Dimensions for Picking an Analytical Data Warehouse - Gleb Mezhanskiy The End of ETL as We Know It - Paul Singman Building a Career as a Data Engineer - Vijay Kiran Modern Metadata for the Modern Data Stack - Prukalpa Sankar Your Data Tests Failed! Now What? - Sam Bail