Machine Learning With Lightgbm And Python

Author : Andrich van Wyk
Publisher : Packt Publishing Ltd
Page : 252 pages
File Size : 52,9 Mb
Release : 2023-09-29
Category : Computers
ISBN : 9781800563056

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Machine Learning with LightGBM and Python by Andrich van Wyk Pdf

Take your software to the next level and solve real-world data science problems by building production-ready machine learning solutions using LightGBM and Python Key Features Get started with LightGBM, a powerful gradient-boosting library for building ML solutions Apply data science processes to real-world problems through case studies Elevate your software by building machine learning solutions on scalable platforms Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionMachine Learning with LightGBM and Python is a comprehensive guide to learning the basics of machine learning and progressing to building scalable machine learning systems that are ready for release. This book will get you acquainted with the high-performance gradient-boosting LightGBM framework and show you how it can be used to solve various machine-learning problems to produce highly accurate, robust, and predictive solutions. Starting with simple machine learning models in scikit-learn, you’ll explore the intricacies of gradient boosting machines and LightGBM. You’ll be guided through various case studies to better understand the data science processes and learn how to practically apply your skills to real-world problems. As you progress, you’ll elevate your software engineering skills by learning how to build and integrate scalable machine-learning pipelines to process data, train models, and deploy them to serve secure APIs using Python tools such as FastAPI. By the end of this book, you’ll be well equipped to use various -of-the-art tools that will help you build production-ready systems, including FLAML for AutoML, PostgresML for operating ML pipelines using Postgres, high-performance distributed training and serving via Dask, and creating and running models in the Cloud with AWS Sagemaker.What you will learn Get an overview of ML and working with data and models in Python using scikit-learn Explore decision trees, ensemble learning, gradient boosting, DART, and GOSS Master LightGBM and apply it to classification and regression problems Tune and train your models using AutoML with FLAML and Optuna Build ML pipelines in Python to train and deploy models with secure and performant APIs Scale your solutions to production readiness with AWS Sagemaker, PostgresML, and Dask Who this book is forThis book is for software engineers aspiring to be better machine learning engineers and data scientists unfamiliar with LightGBM, looking to gain in-depth knowledge of its libraries. Basic to intermediate Python programming knowledge is required to get started with the book. The book is also an excellent source for ML veterans, with a strong focus on ML engineering with up-to-date and thorough coverage of platforms such as AWS Sagemaker, PostgresML, and Dask.

Author : ROSHAN. BHAVE
Publisher : Unknown
Page : 0 pages
File Size : 42,5 Mb
Release : 2021-08
Category : Machine learning
ISBN : 1800564740

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Practical Machine Learning with LightGBM and Python by ROSHAN. BHAVE Pdf

Author : Abdulhamit Subasi
Publisher : Academic Press
Page : 534 pages
File Size : 55,8 Mb
Release : 2020-06-05
Category : Computers
ISBN : 9780128213803

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Practical Machine Learning for Data Analysis Using Python by Abdulhamit Subasi Pdf

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 : Andreas C. Müller,Sarah Guido
Publisher : "O'Reilly Media, Inc."
Page : 400 pages
File Size : 48,6 Mb
Release : 2016-09-26
Category : Computers
ISBN : 9781449369903

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Introduction to Machine Learning with Python by Andreas C. Müller,Sarah Guido Pdf

Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data. Machine Learning with Python teaches you the basics of machine learning and provides a thorough hands-on understanding of the subject.You'll learn important machine learning concepts and algorithms, when to use them, and how to use them. The book will cover a machine learning workflow: data preprocessing and working with data, training algorithms, evaluating results, and implementing those algorithms into a production-level system.

Author : Tarek Amr
Publisher : Packt Publishing Ltd
Page : 368 pages
File Size : 53,7 Mb
Release : 2020-07-24
Category : Mathematics
ISBN : 9781838823580

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Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits by Tarek Amr Pdf

Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production. What you will learnUnderstand when to use supervised, unsupervised, or reinforcement learning algorithmsFind out how to collect and prepare your data for machine learning tasksTackle imbalanced data and optimize your algorithm for a bias or variance tradeoffApply supervised and unsupervised algorithms to overcome various machine learning challengesEmploy best practices for tuning your algorithm’s hyper parametersDiscover how to use neural networks for classification and regressionBuild, evaluate, and deploy your machine learning solutions to productionWho this book is for This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.

Author : Dr Deepti Chopra
Publisher : BPB Publications
Page : 134 pages
File Size : 51,5 Mb
Release : 2021-05-07
Category : Computers
ISBN : 9789389423617

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Building Machine Learning Systems Using Python by Dr Deepti Chopra Pdf

Explore Machine Learning Techniques, Different Predictive Models, and its Applications Ê KEY FEATURESÊÊ _ Extensive coverage of real examples on implementation and working of ML models. _ Includes different strategies used in Machine Learning by leading data scientists. _ Focuses on Machine Learning concepts and their evolution to algorithms. DESCRIPTIONÊ This book covers basic concepts of Machine Learning, various learning paradigms, different architectures and algorithms used in these paradigms. You will learn the power of ML models by exploring different predictive modeling techniques such as Regression, Clustering, and Classification. You will also get hands-on experience on methods and techniques such as Overfitting, Underfitting, Random Forest, Decision Trees, PCA, and Support Vector Machines. In this book real life examples with fully working of Python implementations are discussed in detail. At the end of the book you will learn about the unsupervised learning covering Hierarchical Clustering, K-means Clustering, Dimensionality Reduction, Anomaly detection, Principal Component Analysis.Ê WHAT YOU WILL LEARN _ Learn to perform data engineering and analysis. _ Build prototype ML models and production ML models from scratch. _ Develop strong proficiency in using scikit-learn and Python. _ Get hands-on experience with Random Forest, Logistic Regression, SVM, PCA, and Neural Networks. WHO THIS BOOK IS FORÊÊ This book is meant for beginners who want to gain knowledge about Machine Learning in detail. This book can also be used by Machine Learning users for a quick reference for fundamentals in Machine Learning. Readers should have basic knowledge of Python and Scikit-Learn before reading the book. TABLE OF CONTENTS 1. Introduction to Machine Learning 2. Linear Regression 3. Classification Using Logistic Regression 4. Overfitting and Regularization 5. Feasibility of Learning 6. Support Vector Machine 7. Neural Network 8. Decision Trees 9. Unsupervised Learning 10. Theory of Generalization 11. Bias and Fairness in ML

Author : Butch Quinto
Publisher : Apress
Page : 367 pages
File Size : 52,5 Mb
Release : 2020-02-22
Category : Computers
ISBN : 9781484256695

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Next-Generation Machine Learning with Spark by Butch Quinto Pdf

Access real-world documentation and examples for the Spark platform for building large-scale, enterprise-grade machine learning applications. The past decade has seen an astonishing series of advances in machine learning. These breakthroughs are disrupting our everyday life and making an impact across every industry. Next-Generation Machine Learning with Spark provides a gentle introduction to Spark and Spark MLlib and advances to more powerful, third-party machine learning algorithms and libraries beyond what is available in the standard Spark MLlib library. By the end of this book, you will be able to apply your knowledge to real-world use cases through dozens of practical examples and insightful explanations. What You Will Learn Be introduced to machine learning, Spark, and Spark MLlib 2.4.xAchieve lightning-fast gradient boosting on Spark with the XGBoost4J-Spark and LightGBM librariesDetect anomalies with the Isolation Forest algorithm for SparkUse the Spark NLP and Stanford CoreNLP libraries that support multiple languagesOptimize your ML workload with the Alluxio in-memory data accelerator for SparkUse GraphX and GraphFrames for Graph AnalysisPerform image recognition using convolutional neural networksUtilize the Keras framework and distributed deep learning libraries with Spark Who This Book Is For Data scientists and machine learning engineers who want to take their knowledge to the next level and use Spark and more powerful, next-generation algorithms and libraries beyond what is available in the standard Spark MLlib library; also serves as a primer for aspiring data scientists and engineers who need an introduction to machine learning, Spark, and Spark MLlib.

Author : Georgia Kalyva
Publisher : Packt Publishing Ltd
Page : 310 pages
File Size : 52,8 Mb
Release : 2023-12-28
Category : Computers
ISBN : 9781805123958

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Machine Learning Security with Azure by Georgia Kalyva Pdf

Implement industry best practices to identify vulnerabilities and protect your data, models, environment, and applications while learning how to recover from a security breach Key Features Learn about machine learning attacks and assess your workloads for vulnerabilities Gain insights into securing data, infrastructure, and workloads effectively Discover how to set and maintain a better security posture with the Azure Machine Learning platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWith AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure. This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture. By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.What you will learn Explore the Azure Machine Learning project life cycle and services Assess the vulnerability of your ML assets using the Zero Trust model Explore essential controls to ensure data governance and compliance in Azure Understand different methods to secure your data, models, and infrastructure against attacks Find out how to detect and remediate past or ongoing attacks Explore methods to recover from a security breach Monitor and maintain your security posture with the right tools and best practices Who this book is for This book is for anyone looking to learn how to assess, secure, and monitor every aspect of AI or machine learning projects running on the Microsoft Azure platform using the latest security and compliance, industry best practices, and standards. This is a must-have resource for machine learning developers and data scientists working on ML projects. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from this book, as the chapters cover everything from implementation to deployment, AI attack prevention, and recovery.

Author : Bob Mather
Publisher : Abiprod Pty Ltd
Page : 83 pages
File Size : 53,9 Mb
Release : 2019-11-16
Category : Computers
ISBN : 9781922300034

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Machine Learning in Python by Bob Mather Pdf

Are you excited about Artificial Intelligence and want to get started?Are you excited about Machine Learning and want to learn how to implement in Python? The book below is the answer. Given the large amounts of data we use everyday; whether it is in the web, supermarkets, social media etc. analysis of data has become integral to our daily life. The ability to do so effectively can propel your career or business to great heights. Machine Learning is the most effective data analysis tool. While it is a complex topic, it can be broken down into simpler steps, as show in this book. We are using Python, which is a great programming language for beginners. Python is a great language that is commonly used with Machine Learning. Python is used extensively in Mathematics, Gaming and Graphic Design. It is fast to develop and prototype. It is web capable, meaning that we can use Python to gather web data. It is adaptable, and has great community of users. Here's What's Included In This Book: What is Machine Learning?Why use Python?Regression Analysis using Python with an exampleClustering Analysis using Python with an exampleImplementing an Artificial Neural NetworkBackpropagation90 Day Plan to Learn and Implement Machine LearningConclusion

Author : Ryan Turner
Publisher : Publishing Factory
Page : 114 pages
File Size : 51,5 Mb
Release : 2020-04-12
Category : Computers
ISBN : 8210379456XXX

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Python Machine Learning by Ryan Turner Pdf

Are you a novice programmer who wants to learn Python Machine Learning? Are you worried about how to translate what you already know into Python? This book will help you overcome those problems. As machines get ever more complex and perform more and more tasks to free up our time, so it is that new ideas are developed to help us continually improve their speed and abilities. One of these is Python and in Python Machine Learning: The Ultimate Beginner's Guide to Learn Python Machine Learning Step by Step using Scikit-Learn and Tensorflow, you will discover information and advice on: • What machine learning is • The history of machine learning • Approaches to machine learning • Support vector machines • Machine learning and neural networks • The Internet of Things (IoT) • The future of machine learning • And more… This book has been written specifically for beginners and the simple, step by step instructions and plain language make it an ideal place to start for anyone who has a passing interest in this fascinating subject. Python really is an amazing system and can provide you with endless possibilities when you start learning about it. Get a copy of Python Machine Learning today and see where the future lies!

Author : Finn Sanders
Publisher : Roland Bind
Page : 105 pages
File Size : 42,6 Mb
Release : 2019-05-22
Category : Computers
ISBN : PKEY:6610000178063

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Python Machine Learning For Beginners by Finn Sanders Pdf

Imagine a world where you can make a computer program learn for itself? What if it could recognize who is in a picture or the exact websites that you want to look for when you type it into the program? What if you were able to create any kind of program that you wanted, even as a beginner programmer, without all of the convoluted codes and other information that makes your head spin? This is actually all possible. The programs that were mentioned before are all a part of machine learning. This is a breakthrough in the world of information technology, which allows the computer to learn how to behave, rather than asking the programmer to think of every single instance that may show up with their user ahead of time. it is taking over the world, and you may be using it now, without even realizing it. If you have used a search engine, worked with photo recognition, or done speech recognition devices on your phone, then you have worked with machine learning. And if you combine it with the Python programming language, it is faster, more powerful, and easier (even for beginners) to create your own programs today. Python is considered the ultimate coding language for beginners, but once you start to use it, you will never be able to tell. Many of the best programs out there use this language behind them, and if you are a beginner who is ready to learn, this is a great place to start. If you have a program in mind, or you just want to be able to get some programming knowledge and learn more about the power that comes behind it, then this is the guidebook for you. ★★Some of the topics that we will discuss include★★ ♦ The Fundamentals of Machine Learning, Deep learning, And Neural Networks ♦ How To Set Up Your Environment And Make Sure That Python, TensorFlow And Scikit-Learn Work Well For You ♦ How To Master Neural Network Implementation Using Different Libraries ♦ How Random Forest Algorithms Are Able To Help Out With Machine Learning ♦ How To Uncover Hidden Patterns And Structures With Clustering ♦ How Recurrent Neural Networks Work And When To Use ♦ The Importance Of Linear Classifiers And Why They Need To Be Used In Machine Learning ♦ And Much More! This guidebook is going to provide you with the information you need to get started with Python Machine Learning. If you have an idea for a great program, but you don't have the technical knowledge to make it happen, then this guidebook will help you get started. Machine learning has the capabilities, and Python has the ease, to help you, even as a beginner, create any product that you would like. If you want to learn more about how to make the best programs with Python Machine learning, buy the book today!

Author : Guillaume Saupin
Publisher : guillaume saupin
Page : 208 pages
File Size : 45,9 Mb
Release : 2022-11-10
Category : Computers
ISBN : 8210379456XXX

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Practical Gradient Boosting by Guillaume Saupin Pdf

This book on Gradient Boosting methods is intended for students, academics, engineers, and data scientists who wish to discover in depth the functioning of this Machine Learning technique used to build decision tree ensembles. All the concepts are illustrated by examples of application code. They allow the reader to rebuild from scratch his own training library of Gradient Boosting methods. In parallel, the book presents the best practices of Data Science and provides the reader with a solid technical background to build Machine Learning models. After a presentation of the principles of Gradient Boosting citing the application cases, advantages and limitations, the reader is introduced to the details of the mathematical theory. A simple implementation is given to illustrate how it works. The reader is then armed to tackle the application and configuration of these methods. Data preparation, training, explanation of a model, management of Hyper Parameter Tuning and use of objective functions are covered in detail! The last chapters of the book extend the subject to the application of Gradient Boosting for time series, the presentation of the emblematic libraries XGBoost, CatBoost and LightGBM as well as the concept of multi-resolution models.

Author : Brady Ellison
Publisher :
Page : 128 pages
File Size : 41,7 Mb
Release : 2024-07-01
Category : Computers
ISBN : 8210379456XXX

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Python Machine Learning by Brady Ellison Pdf

Ready to discover the Machine Learning world? Machine learning paves the path into the future and it’s powered by Python. All industries can benefit from machine learning and artificial intelligence whether we’re talking about private businesses, healthcare, infrastructure, banking, or social media. What exactly does it do for us and what does a machine learning specialist do? Machine learning professionals create and implement special algorithms that can learn from existing data to make an accurate prediction on new never before seen data. Python Machine Learning presents you a step-by-step guide on how to create machine learning models that lead to valuable results. The book focuses on machine learning theory as much as practical examples. You will learn how to analyse data, use visualization methods, implement regression and classification models, and how to harness the power of neural networks. By purchasing this book, your machine learning journey becomes a lot easier. While a minimal level of Python programming is recommended, the algorithms and techniques are explained in such a way that you don’t need to be intimidated by mathematics. The Topics Covered Include: Machine learning fundamentals How to set up the development environment How to use Python libraries and modules like Scikit-learn, TensorFlow, Matplotlib, and NumPy How to explore data How to solve regression and classification problems Decision trees k-means clustering Feed-forward and recurrent neural networks Get your copy now

Author : Alberto Boschetti,Luca Massaron
Publisher : Packt Publishing Ltd
Page : 373 pages
File Size : 50,5 Mb
Release : 2016-10-28
Category : Computers
ISBN : 9781786462831

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Python Data Science Essentials by Alberto Boschetti,Luca Massaron Pdf

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Author : Rajender Kumar
Publisher : Jamba Academy
Page : 504 pages
File Size : 54,9 Mb
Release : 2023-03-02
Category : Computers
ISBN : 9781960833006

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Python Machine Learning by Rajender Kumar Pdf

Are you ready to dive into the world of Python machine learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of machine learning and the powerful Scikit-learn library. Key Features: Detailed introduction to the fundamentals of machine learning and the Scikit-Learn library. Comprehensive coverage of essential concepts such as data preprocessing, model selection, evaluation, and optimization. Hands-on experience with real-world datasets and practical projects that will help you develop the skills you need to succeed in machine learning. Easy-to-follow explanations and step-by-step examples that make it easy for beginners to get started and advanced users to take their skills to the next level. See how machine learning is being used to solve problems in industries such as healthcare, finance and more. This book is perfect for beginners who are new to machine learning and want to learn Scikit-Learn from scratch. It is also ideal for intermediate and advanced users who want to expand their knowledge and build more complex models. Outcome: Unlock the earning potential of up to $300k in job after reading the book. Boosting your resume. Opening doors to new opportunities. What other people says: Don't just take our word for it - see what other readers have said: "I was able to understand machine learning concepts and implement them easily with the help of this book." "Rajender Kumar's writing style made the complex concepts easy to understand." "I highly recommend this book to anyone looking to learn machine learning with Python." Don't miss out on this opportunity to master the art of Python machine learning with "Python Machine Learning: A Beginner's Guide to Scikit-Learn". Get your copy today and start building your own intelligent systems! WHO THIS BOOK IS FOR? "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is intended for a wide range of readers, including: Individuals who are new to the field of machine learning and want to gain a solid understanding of the basics and how to apply them using the popular scikit-learn library in Python. Data scientists, statisticians, and analysts who are familiar with machine learning concepts but want to learn how to implement them using Python and scikit-learn. Developers and engineers who want to add machine learning to their skill set and build intelligent applications using Python. Students and researchers who are studying machine learning and want to learn how to apply it using a widely used and accessible library like scikit-learn. Table of Contents Introduction to Machine Learning Python: A Beginner's Overview Data Preparation Supervised Learning Unsupervised Learning Deep Learning Model Selection and Evaluation The Power of Combining: Ensemble Learning Methods Real-World Applications of Machine Learning Future Directions in Python Machine Learning Additional Resources Tools and Frameworks Datasets Career Resources Glossary