Mastering Machine Learning With Python And Scikit Learn
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Mastering Machine Learning With Scikit Learn Second Edition
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Author : Gavin Hackeling
language : en
Publisher:
Release Date : 2017-07-27
Mastering Machine Learning With Scikit Learn Second Edition written by Gavin Hackeling and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-07-27 with Computers categories.
Mastering Machine Learning With Scikit Learn
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Author : Gavin Hackeling
language : en
Publisher:
Release Date : 2014-10-29
Mastering Machine Learning With Scikit Learn written by Gavin Hackeling and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-10-29 with Machine learning categories.
If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential.
Mastering Machine Learning With Python And Scikit Learn
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Author : Katarina Juric
language : en
Publisher: Independently Published
Release Date : 2025-04-14
Mastering Machine Learning With Python And Scikit Learn written by Katarina Juric and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-14 with Computers categories.
Unlock the power of machine learning with Mastering Machine Learning with Python and Scikit-Learn. This in-depth guide will walk you through the process of building machine learning models, from the ground up, using Scikit-Learn, one of the most widely used Python libraries for machine learning. Whether you're a beginner looking to dive into machine learning or an experienced data scientist seeking to master advanced techniques, this book will equip you with the tools and knowledge to build efficient and scalable models for real-world applications. Scikit-Learn provides simple and efficient tools for data analysis and machine learning. With its extensive functionality, this book will teach you how to implement various machine learning algorithms, such as classification, regression, clustering, and dimensionality reduction. You'll also explore key concepts like feature engineering, model evaluation, hyperparameter tuning, and how to apply these methods to solve real-world problems. Inside, you'll learn: The fundamentals of machine learning and the Scikit-Learn library How to preprocess data, including feature scaling, encoding categorical variables, and handling missing values The principles behind supervised learning algorithms like linear regression, decision trees, and support vector machines (SVMs) Techniques for unsupervised learning, including k-means clustering and principal component analysis (PCA) How to evaluate machine learning models using cross-validation, metrics like accuracy, precision, recall, and confusion matrices Advanced topics such as ensemble learning, random forests, and boosting methods Hyperparameter tuning techniques like GridSearchCV and RandomizedSearchCV for improving model performance How to deploy machine learning models and integrate them into production systems By the end of this book, you'll have the expertise to build and deploy machine learning models, from simple to complex, using Python and Scikit-Learn. Whether you're working on business analytics, predictive modeling, or artificial intelligence projects, Mastering Machine Learning with Python and Scikit-Learn will give you the skills to tackle a wide range of machine learning problems. Key Features: Master machine learning algorithms and techniques using Python and Scikit-Learn Step-by-step guidance for building, evaluating, and tuning machine learning models Practical examples and real-world case studies to apply machine learning to solve problems Advanced topics such as ensemble methods, hyperparameter tuning, and model deployment Best practices for preprocessing data, feature selection, and evaluating model performance Start mastering machine learning today with Mastering Machine Learning with Python and Scikit-Learn and take your data science and machine learning skills to the next level.
Machine Learning Mastery With Python
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Author : Jason Brownlee
language : en
Publisher: Machine Learning Mastery
Release Date : 2016-04-08
Machine Learning Mastery With Python written by Jason Brownlee and has been published by Machine Learning Mastery this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-08 with Computers categories.
The Python ecosystem with scikit-learn and pandas is required for operational machine learning. Python is the rising platform for professional machine learning because you can use the same code to explore different models in R&D then deploy it directly to production. In this Ebook, learn exactly how to get started and apply machine learning using the Python ecosystem.
Mastering Machine Learning On Aws
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Author : Dr. Saket S.R. Mengle
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-20
Mastering Machine Learning On Aws written by Dr. Saket S.R. Mengle 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 2019-05-20 with Computers categories.
Gain expertise in ML techniques with AWS to create interactive apps using SageMaker, Apache Spark, and TensorFlow. Key FeaturesBuild machine learning apps on Amazon Web Services (AWS) using SageMaker, Apache Spark and TensorFlowLearn model optimization, and understand how to scale your models using simple and secure APIsDevelop, train, tune and deploy neural network models to accelerate model performance in the cloudBook Description AWS is constantly driving new innovations that empower data scientists to explore a variety of machine learning (ML) cloud services. This book is your comprehensive reference for learning and implementing advanced ML algorithms in AWS cloud. As you go through the chapters, you’ll gain insights into how these algorithms can be trained, tuned and deployed in AWS using Apache Spark on Elastic Map Reduce (EMR), SageMaker, and TensorFlow. While you focus on algorithms such as XGBoost, linear models, factorization machines, and deep nets, the book will also provide you with an overview of AWS as well as detailed practical applications that will help you solve real-world problems. Every practical application includes a series of companion notebooks with all the necessary code to run on AWS. In the next few chapters, you will learn to use SageMaker and EMR Notebooks to perform a range of tasks, right from smart analytics, and predictive modeling, through to sentiment analysis. By the end of this book, you will be equipped with the skills you need to effectively handle machine learning projects and implement and evaluate algorithms on AWS. What you will learnManage AI workflows by using AWS cloud to deploy services that feed smart data productsUse SageMaker services to create recommendation modelsScale model training and deployment using Apache Spark on EMRUnderstand how to cluster big data through EMR and seamlessly integrate it with SageMakerBuild deep learning models on AWS using TensorFlow and deploy them as servicesEnhance your apps by combining Apache Spark and Amazon SageMakerWho this book is for This book is for data scientists, machine learning developers, deep learning enthusiasts and AWS users who want to build advanced models and smart applications on the cloud using AWS and its integration services. Some understanding of machine learning concepts, Python programming and AWS will be beneficial.
Python Machine Learning
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Author : Zach Codings
language : en
Publisher:
Release Date : 2019-10-21
Python Machine Learning written by Zach Codings and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-21 with categories.
What is machine learning and why would a programmer want to learn how to use it? Is artificial intelligence the same as working with machine learning? Are you interested in becoming a machine learning expert but don't know where to start from? Keep reading... The future of our world is evolving towards an era where interaction with machines form the foundation of most tasks we perform. In light of this, it is important to gain actionable knowledge in machine learning technologies and skills. These skills will be useful in the near future as you maneuver through different career paths. Today data is driving many business processes, and without data, it is impossible to imagine where many of the top businesses would be. Imagine how you used to struggle with search results online back in the day, and how easy it is to look for something online today and get the right results. All this is possible through machine learning models. What you need is a foundational approach to learning the basics of machine learning. You can use this knowledge to build your expertise in machine learning over time. While this is an introductory level book, it introduces you to vast concepts in machine learning that will be important to your career. By the end of the book, you will have learned so much about machine learning and the respective python libraries that you will use when building models all the time. An important aspect of machine learning that we must stress even at this juncture is data analysis. Data is key to the success of machine learning and deep learning models. When implemented properly, the kind of data you have will make a big difference in whether your model succeeds or not. Since we are discussing machine learning and the future of computing as we know it, we will also dedicate some time to discussing the current trends in the world, and how they affect our ability to perform some tasks. In this case, we will look at the Internet of Things (IoT) and how we can use different approaches to integrate machine learning and IoT models. Throughout these pages, you will learn: The Fundamentals of Python for Machine Learning Data Analysis in Python Comparing Deep Learning and Machine Learning Machine Learning with Scikit-Learn Deep Learning with TensorFlow Deep Learning with PyTorch and Keras The Role of Machine Learning in the Internet of Things (IoT) Looking to the Future with Machine Learning And much more... Even if you don't have any background in machine learning and Python programming, this book will give you the tools to develop machine learning models. Arm yourself with all this knowledge! Scroll up and click the BUY NOW BUTTON!
Mastering Machine Learning With Python In Six Steps
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Author : Manohar Swamynathan
language : en
Publisher: Apress
Release Date : 2017-06-05
Mastering Machine Learning With Python In Six Steps written by Manohar Swamynathan and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-06-05 with Computers categories.
Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage. What You'll Learn Examine the fundamentals of Python programming language Review machine Learning history and evolution Understand machine learning system development frameworks Implement supervised/unsupervised/reinforcement learning techniques with examples Explore fundamental to advanced text mining techniques Implement various deep learning frameworks Who This Book Is For Python developers or data engineers looking to expand their knowledge or career into machine learning area. Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python. Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.
Python Machine Learning
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Author : Sebastian Raschka
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-12-12
Python Machine Learning 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 2019-12-12 with Computers categories.
Applied machine learning with a solid foundation in theory. Revised and expanded for TensorFlow 2, GANs, and reinforcement learning. Purchase of the print or Kindle book includes a free eBook in the PDF format. Key Features Third edition of the bestselling, widely acclaimed Python machine learning book Clear and intuitive explanations take you deep into the theory and practice of Python machine learning Fully updated and expanded to cover TensorFlow 2, Generative Adversarial Network models, reinforcement learning, and best practices Book Description Python Machine Learning, Third Edition is a comprehensive guide to machine learning and deep learning with Python. 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 working 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, Raschka and Mirjalili teach the principles behind machine learning, allowing you to build models and applications for yourself. Updated for TensorFlow 2.0, this new third edition introduces readers to its new Keras API features, as well as the latest additions to scikit-learn. It's also expanded to cover cutting-edge reinforcement learning techniques based on deep learning, as well as an introduction to GANs. Finally, this book also explores a subfield of natural language processing (NLP) called sentiment analysis, helping you learn how to use machine learning algorithms to classify documents. This 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 Master the frameworks, models, and techniques that enable machines to 'learn' from data Use scikit-learn for machine learning and TensorFlow for deep learning Apply machine learning to image classification, sentiment analysis, intelligent web applications, and more Build and train neural networks, GANs, and other models 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 know some Python and you want to use machine learning and deep learning, pick up this book. Whether you want to start from scratch or extend your machine learning knowledge, this is an essential resource. Written for developers and data scientists who want to create practical machine learning and deep learning code, this book is ideal for anyone who wants to teach computers how to learn from data.
Mastering Machine Learning With Tensorflow Pytorch And Scikit Learn
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Author : Dr Benjamin Neudorf
language : en
Publisher: Independently Published
Release Date : 2025-08-27
Mastering Machine Learning With Tensorflow Pytorch And Scikit Learn written by Dr Benjamin Neudorf and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-27 with Computers categories.
Unlock the Power of Machine Learning-No Experience Required Are you fascinated by artificial intelligence but feel overwhelmed by the jargon, complexity, or sheer scale of machine learning? Do you dream of building intelligent systems, but worry you lack the background, confidence, or mathematical skills to get started? You're not alone-and this book is for you. Mastering Machine Learning with TensorFlow, PyTorch, and Scikit-Learn: A Practical Python Guide is your friendly, step-by-step introduction to modern machine learning. Whether you're a complete beginner or a curious developer, you'll discover how easy-and fun-machine learning can be with the right guide at your side. What You'll Find Inside: Beginner-Friendly Approach: No prior experience in machine learning, statistics, or advanced Python required. Every concept is broken down into plain language and hands-on examples, guiding you gently from your very first line of code to complete, working projects. Confidence-Building Tutorials: Learn by doing with real-world datasets, detailed walkthroughs, and plenty of practical exercises-so you'll never feel lost or left behind. Three Powerful Frameworks, One Book: Master the essentials of TensorFlow, PyTorch, and Scikit-Learn-the leading Python libraries used by top companies and research labs worldwide. Real-World Projects: Go beyond theory. Build your own machine learning models for regression, classification, image recognition, and more, using code you can run, adapt, and expand for your own ideas. Supportive, Encouraging Voice: Mistakes are normal-and often the best teachers. Throughout this book, you'll find troubleshooting tips, gentle encouragement, and guidance that celebrates your progress and every small win. Key Benefits: Gain a clear, practical understanding of the entire machine learning workflow-from data preparation to model deployment. Develop strong Python skills while building confidence with professional tools and libraries. Understand core concepts like neural networks, deep learning, transfer learning, and explainable AI without the intimidation. Apply your new skills immediately to real problems, unlocking doors in tech, business, research, and beyond. Why This Book Stands Out: Step-by-step, project-based lessons perfect for absolute beginners. Friendly explanations that demystify machine learning and artificial intelligence. Practical, working code for every topic-no more guesswork or copying from unreliable sources. Written by an experienced educator who remembers what it feels like to start from scratch. Ready to Begin Your Machine Learning Journey? You don't need a PhD or years of experience. All you need is curiosity, determination, and the right companion to guide you. Start reading Mastering Machine Learning with TensorFlow, PyTorch, and Scikit-Learn today-and take your first confident step toward a future in AI. Don't just learn machine learning-master it, one step at a time. Scroll up and get your copy now!
Hands On Scikit Learn For Machine Learning Applications
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Author : David Paper
language : en
Publisher: Apress
Release Date : 2019-11-16
Hands On Scikit Learn For Machine Learning Applications written by David Paper and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-11-16 with Mathematics categories.
Aspiring data science professionals can learn the Scikit-Learn library along with the fundamentals of machine learning with this book. The book combines the Anaconda Python distribution with the popular Scikit-Learn library to demonstrate a wide range of supervised and unsupervised machine learning algorithms. Care is taken to walk you through the principles of machine learning through clear examples written in Python that you can try out and experiment with at home on your own machine. All applied math and programming skills required to master the content are covered in this book. In-depth knowledge of object-oriented programming is not required as working and complete examples are provided and explained. Coding examples are in-depth and complex when necessary. They are also concise, accurate, and complete, and complement the machine learning concepts introduced. Working the examples helps to build the skills necessary to understand and apply complexmachine learning algorithms. Hands-on Scikit-Learn for Machine Learning Applications is an excellent starting point for those pursuing a career in machine learning. Students of this book will learn the fundamentals that are a prerequisite to competency. Readers will be exposed to the Anaconda distribution of Python that is designed specifically for data science professionals, and will build skills in the popular Scikit-Learn library that underlies many machine learning applications in the world of Python. What You'll Learn Work with simple and complex datasets common to Scikit-Learn Manipulate data into vectors and matrices for algorithmic processing Become familiar with the Anaconda distribution used in data science Apply machine learning with Classifiers, Regressors, and Dimensionality Reduction Tune algorithms and find the best algorithms for each dataset Load data from and save to CSV, JSON, Numpy, and Pandas formats Who This Book Is For The aspiring data scientist yearning to break into machine learning through mastering the underlying fundamentals that are sometimes skipped over in the rush to be productive. Some knowledge of object-oriented programming and very basic applied linear algebra will make learning easier, although anyone can benefit from this book.