Comprehensive Machine Learning Techniques: A Guide for the Experienced Analyst PDF Download
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Author: Adam Jones
Publisher: Walzone Press
ISBN:
Category : Computers
Languages : en
Pages : 211
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Book Description
"Comprehensive Machine Learning Techniques: A Guide for the Experienced Analyst" is an in-depth resource crafted to elevate seasoned machine learning analysts to the cutting-edge of their profession. This definitive guide comprehensively explores advanced machine learning methodologies, offering a wide-ranging collection of chapters that cover essential foundations, innovative neural network designs, optimization tactics, and pivotal applications in areas like natural language processing, computer vision, and time series analysis. Each chapter thoughtfully dissects complex topics—from the core principles of deep learning and generative models to the intricacies of reinforcement learning and the crucial role of ethics and interpretability in AI—providing the insights necessary to address contemporary machine learning challenges. Ideal for practitioners, researchers, and graduate students with a solid foundation in machine learning, this book is an indispensable resource for those aiming to deepen their expertise in advanced techniques and methodologies. Through comprehensive explorations of each topic, it equips readers with the skills to create sophisticated models, apply state-of-the-art algorithms, and drive innovation in their work and research. "Comprehensive Machine Learning Techniques" is more than a mere textbook; it is a transformative tool for advancing mastery in machine learning. Whether you seek to refine your skills, delve into new areas, or contribute to the advancement of AI technologies, this guide provides the depth of knowledge and practical insights necessary to excel in the dynamic field of machine learning.
Author: Adam Jones
Publisher: Walzone Press
ISBN:
Category : Computers
Languages : en
Pages : 211
Get Book Here
Book Description
"Comprehensive Machine Learning Techniques: A Guide for the Experienced Analyst" is an in-depth resource crafted to elevate seasoned machine learning analysts to the cutting-edge of their profession. This definitive guide comprehensively explores advanced machine learning methodologies, offering a wide-ranging collection of chapters that cover essential foundations, innovative neural network designs, optimization tactics, and pivotal applications in areas like natural language processing, computer vision, and time series analysis. Each chapter thoughtfully dissects complex topics—from the core principles of deep learning and generative models to the intricacies of reinforcement learning and the crucial role of ethics and interpretability in AI—providing the insights necessary to address contemporary machine learning challenges. Ideal for practitioners, researchers, and graduate students with a solid foundation in machine learning, this book is an indispensable resource for those aiming to deepen their expertise in advanced techniques and methodologies. Through comprehensive explorations of each topic, it equips readers with the skills to create sophisticated models, apply state-of-the-art algorithms, and drive innovation in their work and research. "Comprehensive Machine Learning Techniques" is more than a mere textbook; it is a transformative tool for advancing mastery in machine learning. Whether you seek to refine your skills, delve into new areas, or contribute to the advancement of AI technologies, this guide provides the depth of knowledge and practical insights necessary to excel in the dynamic field of machine learning.
Author: Andrew W. Trask
Publisher: Simon and Schuster
ISBN: 163835720X
Category : Computers
Languages : en
Pages : 475
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Book Description
Summary Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Deep learning, a branch of artificial intelligence, teaches computers to learn by using neural networks, technology inspired by the human brain. Online text translation, self-driving cars, personalized product recommendations, and virtual voice assistants are just a few of the exciting modern advancements possible thanks to deep learning. About the Book Grokking Deep Learning teaches you to build deep learning neural networks from scratch! In his engaging style, seasoned deep learning expert Andrew Trask shows you the science under the hood, so you grok for yourself every detail of training neural networks. Using only Python and its math-supporting library, NumPy, you'll train your own neural networks to see and understand images, translate text into different languages, and even write like Shakespeare! When you're done, you'll be fully prepared to move on to mastering deep learning frameworks. What's inside The science behind deep learning Building and training your own neural networks Privacy concepts, including federated learning Tips for continuing your pursuit of deep learning About the Reader For readers with high school-level math and intermediate programming skills. About the Author Andrew Trask is a PhD student at Oxford University and a research scientist at DeepMind. Previously, Andrew was a researcher and analytics product manager at Digital Reasoning, where he trained the world's largest artificial neural network and helped guide the analytics roadmap for the Synthesys cognitive computing platform. Table of Contents Introducing deep learning: why you should learn it Fundamental concepts: how do machines learn? Introduction to neural prediction: forward propagation Introduction to neural learning: gradient descent Learning multiple weights at a time: generalizing gradient descent Building your first deep neural network: introduction to backpropagation How to picture neural networks: in your head and on paper Learning signal and ignoring noise:introduction to regularization and batching Modeling probabilities and nonlinearities: activation functions Neural learning about edges and corners: intro to convolutional neural networks Neural networks that understand language: king - man + woman == ? Neural networks that write like Shakespeare: recurrent layers for variable-length data Introducing automatic optimization: let's build a deep learning framework Learning to write like Shakespeare: long short-term memory Deep learning on unseen data: introducing federated learning Where to go from here: a brief guide
Author: Abdulhamit Subasi
Publisher: Academic Press
ISBN: 0128213809
Category : Computers
Languages : en
Pages : 536
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Book Description
Practical Machine Learning for Data Analysis Using Python is a problem solver's guide for creating real-world intelligent systems. It provides a comprehensive approach with concepts, practices, hands-on examples, and sample code. The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-world case studies across various fields, including biomedical signal analysis, healthcare, security, economics, and finance. Moreover, it covers a wide range of machine learning models, including regression, classification, and forecasting. The goal of the book is to help a broad range of readers, including IT professionals, analysts, developers, data scientists, engineers, and graduate students, to solve their own real-world problems. - Offers a comprehensive overview of the application of machine learning tools in data analysis across a wide range of subject areas - Teaches readers how to apply machine learning techniques to biomedical signals, financial data, and healthcare data - Explores important classification and regression algorithms as well as other machine learning techniques - Explains how to use Python to handle data extraction, manipulation, and exploration techniques, as well as how to visualize data spread across multiple dimensions and extract useful features
Author: Peter Harrington
Publisher: Simon and Schuster
ISBN: 1638352453
Category : Computers
Languages : en
Pages : 558
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Book Description
Summary Machine Learning in Action is unique book that blends the foundational theories of machine learning with the practical realities of building tools for everyday data analysis. You'll use the flexible Python programming language to build programs that implement algorithms for data classification, forecasting, recommendations, and higher-level features like summarization and simplification. About the Book A machine is said to learn when its performance improves with experience. Learning requires algorithms and programs that capture data and ferret out the interestingor useful patterns. Once the specialized domain of analysts and mathematicians, machine learning is becoming a skill needed by many. Machine Learning in Action is a clearly written tutorial for developers. It avoids academic language and takes you straight to the techniques you'll use in your day-to-day work. Many (Python) examples present the core algorithms of statistical data processing, data analysis, and data visualization in code you can reuse. You'll understand the concepts and how they fit in with tactical tasks like classification, forecasting, recommendations, and higher-level features like summarization and simplification. Readers need no prior experience with machine learning or statistical processing. Familiarity with Python is helpful. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's Inside A no-nonsense introduction Examples showing common ML tasks Everyday data analysis Implementing classic algorithms like Apriori and Adaboos Table of Contents PART 1 CLASSIFICATION Machine learning basics Classifying with k-Nearest Neighbors Splitting datasets one feature at a time: decision trees Classifying with probability theory: naïve Bayes Logistic regression Support vector machines Improving classification with the AdaBoost meta algorithm PART 2 FORECASTING NUMERIC VALUES WITH REGRESSION Predicting numeric values: regression Tree-based regression PART 3 UNSUPERVISED LEARNING Grouping unlabeled items using k-means clustering Association analysis with the Apriori algorithm Efficiently finding frequent itemsets with FP-growth PART 4 ADDITIONAL TOOLS Using principal component analysis to simplify data Simplifying data with the singular value decomposition Big data and MapReduce
Author: Dirk P. Kroese
Publisher: CRC Press
ISBN: 1000730778
Category : Business & Economics
Languages : en
Pages : 538
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Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author: Simon Meadows
Publisher: Richards Education
ISBN:
Category : Business & Economics
Languages : en
Pages : 239
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Book Description
"How to Be a Financial Analyst: A Comprehensive Guide" is your essential roadmap to mastering the intricate and dynamic field of financial analysis. This in-depth book and course guide provides a complete curriculum for aspiring financial analysts, covering everything from foundational concepts and advanced techniques to real-world applications and industry insights. With twenty detailed chapters, each featuring practical exercises, case studies, and expert advice, this guide equips you with the knowledge and skills needed to excel in financial analysis. Whether you're a student preparing for a career in finance or a professional seeking to enhance your expertise, this comprehensive resource will help you navigate your journey with confidence and clarity.
Author: Rob J Hyndman
Publisher: OTexts
ISBN: 0987507117
Category : Business & Economics
Languages : en
Pages : 380
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Book Description
Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning. This textbook provides a comprehensive introduction to forecasting methods and presents enough information about each method for readers to use them sensibly.
Author: Manish Soni
Publisher:
ISBN:
Category : Study Aids
Languages : en
Pages : 411
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Book Description
The title of this book, IT Analyst: The full guide itself speaks about its content. This book is for students with the critical people skills and technical knowledge to provide outstanding computer user support as this book emphasizes troubleshooting, problem solving, successful communication, determining a client's needs, training, and more. To be competent in Information Technology, as this book emphasizes, students must learn to identify each situation as unique, assess what skills are needed, and effectively apply the appropriate skills and procedures. In essence, the goal of this text is to provide a toolbox from which students can draw in any group situation—whether planning a function with a social club on campus or participating in a task-oriented group project in an academic or business context. To start this process, students must first become aware of their own communication in groups and the ways in which it can be improved to enhance group dynamics. The emphasis here is on critical thinking, skills assessment, and practice.
Author: Chetan Singh
Publisher: Chetan Singh
ISBN:
Category : Business & Economics
Languages : en
Pages : 116
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Book Description
The Complete Guide to Telephone Interview Questions and Answers is an essential resource for job seekers looking to master the art of the telephone interview. With the current job market shifting towards remote work, telephone interviews have become an increasingly important part of the hiring process. This telephone interview questions and answers guidebook covers everything from preparing for a telephone interview to following up afterward, and offers strategies for answering difficult questions, showcasing your qualifications and experience, and using effective communication skills. Inside this Telephone Interview book, you will find: An explanation of the importance of telephone interviews in the hiring process Tips for preparing for a telephone interview, include researching the company and the role, reviewing your resume and cover letter, and practicing common interview questions and answers Technical preparation strategies for ensuring a smooth interview experience Strategies for discussing your experience and qualifications, and answering situational and technical questions Tips for effective communication over the phone Strategies for answering difficult questions and handling challenging situations Best practices for following up after a telephone interview Success Stories and strategies used by successful candidates Lessons learned from successful candidates that can help you improve your own telephone interview skills Whether you're a recent graduate looking for your first job or an experienced professional looking to make a career change, this guide is the ultimate resource for mastering telephone interviews and increasing your chances of landing your dream job.
Author: Rich Collier
Publisher: Packt Publishing Ltd
ISBN: 1788471776
Category : Computers
Languages : en
Pages : 299
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Book Description
Leverage Elastic Stack’s machine learning features to gain valuable insight from your data Key FeaturesCombine machine learning with the analytic capabilities of Elastic StackAnalyze large volumes of search data and gain actionable insight from themUse external analytical tools with your Elastic Stack to improve its performanceBook Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learnInstall the Elastic Stack to use machine learning featuresUnderstand how Elastic machine learning is used to detect a variety of anomaly typesApply effective anomaly detection to IT operations and security analyticsLeverage the output of Elastic machine learning in custom views, dashboards, and proactive alertingCombine your created jobs to correlate anomalies of different layers of infrastructureLearn various tips and tricks to get the most out of Elastic machine learningWho this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.
