Basic Guide For Machine Learning Algorithms And Models

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Basic Guide For Machine Learning Algorithms And Models

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Author : Ms.G.Vanitha
language : en
Publisher: SK Research Group of Companies
Release Date : 2024-07-10

Basic Guide For Machine Learning Algorithms And Models written by Ms.G.Vanitha and has been published by SK Research Group of Companies this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-10 with Computers categories.

Ms.G.Vanitha, Associate Professor, Department of Information Technology, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India. Dr.M.Kasthuri, Associate Professor, Department of Computer Science, Bishop Heber College, Tiruchirappalli, Tamil Nadu, India.

Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications

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Author : J. Paaul
language : en
Publisher: Code Academy
Release Date : 2025-05-07

Machine Learning For Beginners 2025 Step By Step Guide To Master Ml Algorithms Real World Applications written by J. Paaul and has been published by Code Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-07 with Computers categories.

Machine Learning for Beginners 2025 is the perfect guide for anyone looking to dive into the world of machine learning. This book breaks down complex concepts into easy-to-understand explanations and hands-on examples. Covering the fundamentals of ML algorithms, data preprocessing, model evaluation, and real-world applications, this book is ideal for newcomers to the field. With practical projects and step-by-step tutorials, readers will gain the skills to implement machine learning models using Python and popular libraries like Scikit-learn and TensorFlow, making this a comprehensive resource for aspiring data scientists.

Machine Learning With R Quick Start Guide

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Author : Iván Pastor Sanz
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-03-29

Machine Learning With R Quick Start Guide written by Iván Pastor Sanz 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 2019-03-29 with Computers categories.

Learn how to use R to apply powerful machine learning methods and gain insight into real-world applications using clustering, logistic regressions, random forests, support vector machine, and more. Key FeaturesUse R 3.5 to implement real-world examples in machine learningImplement key machine learning algorithms to understand the working mechanism of smart modelsCreate end-to-end machine learning pipelines using modern libraries from the R ecosystemBook Description Machine Learning with R Quick Start Guide takes you on a data-driven journey that starts with the very basics of R and machine learning. It gradually builds upon core concepts so you can handle the varied complexities of data and understand each stage of the machine learning pipeline. From data collection to implementing Natural Language Processing (NLP), this book covers it all. You will implement key machine learning algorithms to understand how they are used to build smart models. You will cover tasks such as clustering, logistic regressions, random forests, support vector machines, and more. Furthermore, you will also look at more advanced aspects such as training neural networks and topic modeling. By the end of the book, you will be able to apply the concepts of machine learning, deal with data-related problems, and solve them using the powerful yet simple language that is R. What you will learnIntroduce yourself to the basics of machine learning with R 3.5Get to grips with R techniques for cleaning and preparing your data for analysis and visualize your resultsLearn to build predictive models with the help of various machine learning techniquesUse R to visualize data spread across multiple dimensions and extract useful featuresUse interactive data analysis with R to get insights into dataImplement supervised and unsupervised learning, and NLP using R librariesWho this book is for This book is for graduate students, aspiring data scientists, and data analysts who wish to enter the field of machine learning and are looking to implement machine learning techniques and methodologies from scratch using R 3.5. A working knowledge of the R programming language is expected.

The Definitive Guide To Machine Learning Operations In Aws

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Author : Neel Sendas
language : en
Publisher: Springer Nature
Release Date : 2025-01-03

The Definitive Guide To Machine Learning Operations In Aws written by Neel Sendas and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-03 with Computers categories.

Foreword by Dr. Shreyas Subramanian, Principal Data Scientist, Amazon This book focuses on deploying, testing, monitoring, and automating ML systems in production. It covers AWS MLOps tools like Amazon SageMaker, Data Wrangler, and AWS Feature Store, along with best practices for operating ML systems on AWS. This book explains how to design, develop, and deploy ML workloads at scale using AWS cloud's well-architected pillars. It starts with an introduction to AWS services and MLOps tools, setting up the MLOps environment. It covers operational excellence, including CI/CD pipelines and Infrastructure as code. Security in MLOps, data privacy, IAM, and reliability with automated testing are discussed. Performance efficiency and cost optimization, like Right-sizing ML resources, are explored. The book concludes with MLOps best practices, MLOPS for GenAI, emerging trends, and future developments in MLOps By the end, readers will learn operating ML workloads on the AWS cloud. This book suits software developers, ML engineers, DevOps engineers, architects, and team leaders aspiring to be MLOps professionals on AWS. What you will learn: ● Create repeatable training workflows to accelerate model development ● Catalog ML artifacts centrally for model reproducibility and governance ● Integrate ML workflows with CI/CD pipelines for faster time to production ● Continuously monitor data and models in production to maintain quality ● Optimize model deployment for performance and cost Who this book is for: This book suits ML engineers, DevOps engineers, software developers, architects, and team leaders aspiring to be MLOps professionals on AWS.

Machine Learning For Data Science Using Ml Algorithms For Predictive Modeling

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Author : Dilip Siddareddy
language : en
Publisher: Xoffencerpublication
Release Date : 2023-10-30

Machine Learning For Data Science Using Ml Algorithms For Predictive Modeling written by Dilip Siddareddy and has been published by Xoffencerpublication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-10-30 with Computers categories.

Because of the advancements that have been made in machine learning, the world is being changed in ways that are difficult to conceive. If you stop for a second and take a good look around, you'll see that the area of data science is everywhere you turn. Take, for example, Alexa from Amazon; she is an artificial intelligence that has been developed to be as simple and straightforward to use as is humanly conceivable. There are many other digital assistants similar to Alexa, such as Google Assistant, Cortana, and so on. Alexa is not the only one of its sort. Therefore, the question of why they were formed in the first place is the most crucial one to ask; the question of how they developed is the second most important one to ask. In any event, we are going to make an attempt to study each and every one of these issues, and we are also going to make an effort to devise answers that are both logical and technological in nature. Within the scope of this discussion, the question that has to be inquired about first and foremost is, "What exactly are Machine Learning and Data Science?" A widespread misconception is that data science and machine learning are interchangeable terms for the same thing. Those people do have a point, to some extent, considering that data science is nothing more than taking a huge amount of data and analyzing it using a variety of machine learning approaches, methodologies, and technologies. Therefore, in order to become an expert in data science, you need to have a solid understanding of mathematics and statistics, in addition to a profound comprehension of the area that you intend to specialize in. To be more specific, what does it mean to have "subject expertise"? Subject expertise is nothing more than the knowledge necessary about a given topic in order to be able to abstract and calculate the data that pertains to that field, as the name of this type of expertise indicates. In a nutshell, these three concepts are considered as the foundations of data science, and if you are successful in mastering all of them, then you should rejoice yourself because you have achieved the level of an A-level data scientist.

Deep Learning With Keras Beginner S Guide To Deep Learning With Keras

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Author : Frank Millstein
language : en
Publisher: Frank Millstein
Release Date : 2019-10-20

Deep Learning With Keras Beginner S Guide To Deep Learning With Keras written by Frank Millstein and has been published by Frank Millstein this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-20 with Computers categories.

" This book will introduce you to various supervised and unsupervised deep learning algorithms like the multilayer perceptron, linear regression and other more advanced deep convolutional and recurrent neural networks. You will also learn about image processing, handwritten recognition, object recognition and much more. Furthermore, you will get familiar with recurrent neural networks like LSTM and GAN as you explore processing sequence data like time series, text, and audio. The book will definitely be your best companion on this great deep learning journey with Keras introducing you to the basics you need to know in order to take next steps and learn more advanced deep neural networks. Here Is a Preview of What You'll Learn Here… The difference between deep learning and machine learning Deep neural networks Convolutional neural networks Building deep learning models with Keras Multi-layer perceptron network models Activation functions Handwritten recognition using MNIST Solving multi-class classification problems Recurrent neural networks and sequence classification And much more... Get this book NOW and learn more about Deep Learning with Keras! "

Machine Learning Algorithms And Concepts

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Author : Sariya Ansari
language : en
Publisher: Notion Press
Release Date : 2023-09-13

Machine Learning Algorithms And Concepts written by Sariya Ansari and has been published by Notion Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-09-13 with Computers categories.

This book is for machine learning professional & aspiring data scientist who wanted to be established themselves as a machine learning engineer or data science professional. Machine Learning Algorithms & Concepts gives complete idea to begin the phase of machine learning professional. This can be referred as a great starting point to switch the career path from existing profession to a machine learning professional. The book covers all major algorithms, its concept, usage, and other miscellaneous concepts based on situation which helps to its reader to decide in which situation what to be used. This book serves as guide to prepare for interviews, exams, campus work as well as for industry professional. It also covers basic programming which gives fair idea to its reader to learn how to code for machine learning problem statement even if he is a beginner in coding.

Machine Learning With Pytorch And Scikit Learn

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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2022-02-25

Machine Learning With Pytorch And Scikit Learn written by Sebastian Raschka 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 2022-02-25 with Computers categories.

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Learn applied machine learning with a solid foundation in theory Clear, intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover PyTorch, transformers, XGBoost, graph neural networks, and best practices Book DescriptionMachine Learning with PyTorch and Scikit-Learn is a comprehensive guide to machine learning and deep learning with PyTorch. It acts as both a step-by-step tutorial and a reference you'll keep coming back to as you build your machine learning systems. Packed with clear explanations, visualizations, and examples, the book covers all the essential machine learning techniques in depth. While some books teach you only to follow instructions, with this machine learning book, we teach the principles allowing you to build models and applications for yourself. Why PyTorch? PyTorch is the Pythonic way to learn machine learning, making it easier to learn and simpler to code with. This book explains the essential parts of PyTorch and how to create models using popular libraries, such as PyTorch Lightning and PyTorch Geometric. You will also learn about generative adversarial networks (GANs) for generating new data and training intelligent agents with reinforcement learning. Finally, this new edition is expanded to cover the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). This PyTorch book is your companion to machine learning with Python, whether you're a Python developer new to machine learning or want to deepen your knowledge of the latest developments.What you will learn Explore frameworks, models, and techniques for machines to learn from data Use scikit-learn for machine learning and PyTorch for deep learning Train machine learning classifiers on images, text, and more Build and train neural networks, transformers, and boosting algorithms Discover best practices for evaluating and tuning models Predict continuous target outcomes using regression analysis Dig deeper into textual and social media data using sentiment analysis Who this book is for If you have a good grasp of Python basics and want to start learning about machine learning and deep learning, then this is the book for you. This is an essential resource written for developers and data scientists who want to create practical machine learning and deep learning applications using scikit-learn and PyTorch. Before you get started with this book, you’ll need a good understanding of calculus, as well as linear algebra.

Ai For Everyone A Beginner S Guide To Artificial Intelligence

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Author : Logan tremblay
language : en
Publisher: Recorded Books
Release Date : 2024-12-20

Ai For Everyone A Beginner S Guide To Artificial Intelligence written by Logan tremblay and has been published by Recorded Books this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-20 with Education categories.

Artificial intelligence is transforming every aspect of our lives, yet it remains a mystery to many. This beginner-friendly guide demystifies AI, breaking down complex concepts into simple, relatable terms. From understanding how AI works to exploring its everyday applications, this book provides a comprehensive overview of the technology shaping our future. You’ll learn about machine learning, data analytics, and ethical considerations, as well as practical ways to incorporate AI into your personal and professional life. Whether you’re a student, professional, or curious learner, this guide equips you with the knowledge to navigate and embrace the AI revolution.

Introduction To Artificial Intelligence Understanding The Basics A Comprehensive Guide To Artificial Intelligence

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Author : Konstantin Titov
language : en
Publisher: Konstantin Titov
Release Date : 2024-01-06

Introduction To Artificial Intelligence Understanding The Basics A Comprehensive Guide To Artificial Intelligence written by Konstantin Titov and has been published by Konstantin Titov this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-01-06 with Computers categories.

Definition and History of AI: Explore the origins and evolution of AI, from its humble beginnings to its current transformative impact. Types of AI: Delve into the different types of AI, from Narrow AI and General AI to the intriguing realm of Superintelligent AI. Data's Crucial Role: Understand the importance of data in AI, its various types (Structured, Unstructured, Semi-Structured), and how it drives AI innovation. Fundamentals of Machine Learning: Uncover the core concepts of machine learning, from Supervised vs. Unsupervised Learning to Reinforcement Learning and Common Algorithms. Neural Networks and Deep Learning: Learn the basics of neural networks, explore the power of deep learning, and grasp the significance of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). Natural Language Processing (NLP): Gain insights into how AI understands language, including Sentiment Analysis, Chatbots, and Translation. Computer Vision: Discover the wonders of image recognition and object detection, along with the intricacies of Facial Recognition Technology. Robotics and Autonomous Systems: Explore AI's role in robotics, from AI-driven robots to self-driving cars and drones. Ethical Considerations: Delve into the ethical aspects of AI, addressing bias, fairness, privacy, and security concerns. Real-World Applications: Witness AI's impact across industries such as healthcare, finance, and retail, and glimpse into the future of AI in various sectors. Emerging Trends: Stay ahead of the curve by exploring quantum computing's synergy with AI and the convergence of AI with the Internet of Things (IoT). Career Paths: Learn about the diverse roles in AI and the essential skills required, as well as the exciting future of work in the AI field. Whether you're a fan of AI, a student eager to learn, or a seasoned professional, "Introduction to Artificial Intelligence: Understanding the Basics" provides you with the essential knowledge to grasp, appreciate, and effectively navigate the AI revolution. Get ready for an exciting adventure into the fascinating world of artificial intelligence.