Advanced Predictive Techniques With Scikit Learn And Tensorflow

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Mastering Predictive Analytics with scikit-learn and TensorFlow

Author : Alvaro Fuentes
Publisher : Packt Publishing Ltd
ISBN 13 : 1789612241
Total Pages : 149 pages
Book Rating : 4.7/5 (896 download)

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Book Synopsis Mastering Predictive Analytics with scikit-learn and TensorFlow by : Alvaro Fuentes

Download or read book Mastering Predictive Analytics with scikit-learn and TensorFlow written by Alvaro Fuentes and published by Packt Publishing Ltd. This book was released on 2018-09-29 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn advanced techniques to improve the performance and quality of your predictive models Key FeaturesUse ensemble methods to improve the performance of predictive analytics modelsImplement feature selection, dimensionality reduction, and cross-validation techniquesDevelop neural network models and master the basics of deep learningBook Description Python is a programming language that provides a wide range of features that can be used in the field of data science. Mastering Predictive Analytics with scikit-learn and TensorFlow covers various implementations of ensemble methods, how they are used with real-world datasets, and how they improve prediction accuracy in classification and regression problems. This book starts with ensemble methods and their features. You will see that scikit-learn provides tools for choosing hyperparameters for models. As you make your way through the book, you will cover the nitty-gritty of predictive analytics and explore its features and characteristics. You will also be introduced to artificial neural networks and TensorFlow, and how it is used to create neural networks. In the final chapter, you will explore factors such as computational power, along with improvement methods and software enhancements for efficient predictive analytics. By the end of this book, you will be well-versed in using deep neural networks to solve common problems in big data analysis. What you will learnUse ensemble algorithms to obtain accurate predictionsApply dimensionality reduction techniques to combine features and build better modelsChoose the optimal hyperparameters using cross-validationImplement different techniques to solve current challenges in the predictive analytics domainUnderstand various elements of deep neural network (DNN) modelsImplement neural networks to solve both classification and regression problemsWho this book is for Mastering Predictive Analytics with scikit-learn and TensorFlow is for data analysts, software engineers, and machine learning developers who are interested in implementing advanced predictive analytics using Python. Business intelligence experts will also find this book indispensable as it will teach them how to progress from basic predictive models to building advanced models and producing more accurate predictions. Prior knowledge of Python and familiarity with predictive analytics concepts are assumed.

Advanced Predictive Techniques with Scikit-Learn and TensorFlow

Author : Alvaro Fuentes
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (113 download)

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Book Synopsis Advanced Predictive Techniques with Scikit-Learn and TensorFlow by : Alvaro Fuentes

Download or read book Advanced Predictive Techniques with Scikit-Learn and TensorFlow written by Alvaro Fuentes and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: "Ensemble methods offer a powerful way to improve prediction accuracy by combining in a clever way predictions from many individual predictors. In this course, you will learn how to use ensemble methods to improve accuracy in classification and regression problems. When using Predictive Analytics to solve actual problems, besides models and algorithms there are many other practical considerations that must be considered like which features should I use, how many features are enough, should I create new features, how to combine features to give the same underlying information, which hyper-parameters should I use? We explore topics that will help you answer such questions. Artificial Neural Networks are models loosely based on how neural networks work in a living being. These models have a long history in the Artificial Intelligence community with ups and downs in popularity. Nowadays, because of the increase in computational power, improved methods, and software enhancements, they are popular again and are the basis for advanced approaches such as Deep Learning. This course introduces the use of Deep Learning models for Predictive Analytics using the powerful TensorFlow library."--Resource description page.

Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow

Author : Aurélien Géron
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 149203259X
Total Pages : 851 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow by : Aurélien Géron

Download or read book Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow written by Aurélien Géron and published by "O'Reilly Media, Inc.". This book was released on 2019-09-05 with total page 851 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This practical book shows you how. By using concrete examples, minimal theory, and two production-ready Python frameworks—Scikit-Learn and TensorFlow—author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what you’ve learned, all you need is programming experience to get started. Explore the machine learning landscape, particularly neural nets Use Scikit-Learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets

Handbook of HydroInformatics

Author : Saeid Eslamian
Publisher : Elsevier
ISBN 13 : 012821970X
Total Pages : 484 pages
Book Rating : 4.1/5 (282 download)

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Book Synopsis Handbook of HydroInformatics by : Saeid Eslamian

Download or read book Handbook of HydroInformatics written by Saeid Eslamian and published by Elsevier. This book was released on 2022-11-30 with total page 484 pages. Available in PDF, EPUB and Kindle. Book excerpt: Classic Soft-Computing Techniques is the first volume of the three, in the Handbook of HydroInformatics series.? Through this comprehensive, 34-chapters work, the contributors explore the difference between traditional computing, also known as hard computing, and soft computing, which is based on the importance given to issues like precision, certainty and rigor. The chapters go on to define fundamentally classic soft-computing techniques such as Artificial Neural Network, Fuzzy Logic, Genetic Algorithm, Supporting Vector Machine, Ant-Colony Based Simulation, Bat Algorithm, Decision Tree Algorithm, Firefly Algorithm, Fish Habitat Analysis, Game Theory, Hybrid Cuckoo–Harmony Search Algorithm, Honey-Bee Mating Optimization, Imperialist Competitive Algorithm, Relevance Vector Machine, etc.?It is a fully comprehensive handbook providing all the information needed around classic soft-computing techniques. This volume is a true interdisciplinary work, and the audience includes postgraduates and early career researchers interested in Computer Science, Mathematical Science, Applied Science, Earth and Geoscience, Geography, Civil Engineering, Engineering, Water Science, Atmospheric Science, Social Science, Environment Science, Natural Resources, and Chemical Engineering. Key insights from global contributors in the fields of data management research, climate change and resilience, insufficient data problem, etc. Offers applied examples and case studies in each chapter, providing the reader with real world scenarios for comparison. Introduces classic soft-computing techniques, necessary for a range of disciplines.

TensorFlow Machine Learning

Author : Benjamin Smith
Publisher :
ISBN 13 :
Total Pages : 248 pages
Book Rating : 4.5/5 (92 download)

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Book Synopsis TensorFlow Machine Learning by : Benjamin Smith

Download or read book TensorFlow Machine Learning written by Benjamin Smith and published by . This book was released on 2021-01-04 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: Have you ever wondered how machine learning works? These days, machine learning, deep learning and neural nets are common terms and they are here to stay as a part of our everyday language. Machine learning is not the easiest of topics to teach, purely because there is so much to it. Machine learning, deep learning and artificial intelligence are used in more applications than most humans even think about - email, Amazon, Netflix, Spotify, and other popular online marketplaces use machine learning to weed out spam emails and bring you recommendations based on your shopping or streaming preferences. Machine learning is used in healthcare, in finance, in just about every industry you can think of - it's here to stay, whether we like it or not.One of the most important parts of learning machine learning is knowing which algorithm to choose and which library. Python is the most popular machine learning programming language and it has a huge advantage over other languages - the large amount of built-in libraries; three of the most important are TensorFlow, Keras and Scikit-Learn. And that's what this book is about - machine learning with TensorFlow, Keras and Scikit-learn. Here's what you will learn: -What machine learning is-How it applies in the real world-Different models and learning types-Different machine learning algorithms-Deep learning vs. machine learning-What TensorFlow is and how to use it-What TensorFlow comprises-Operators, variables, placeholders, and more-What Keras is and how to use it-Keras vs. TensorFlow-How to use Keras for linear regression-How to use Keras to build a neural net-What Scikit-Learn is and how to use it-Using Scikit-Learn to build regression and classification trees-How to build a random forest model-How to install Keras, TensorFlow and Scikit-LearnAnd much more!All the practical examples in the book use Python, so you are expected to need some knowledge of the language before you start. If you're looking to advanced your skills in machine learning, then this is the book for you! Grab your copy of this book today!

Applied Deep Learning with Python

Author : Alex Galea
Publisher : Packt Publishing Ltd
ISBN 13 : 1789806992
Total Pages : 317 pages
Book Rating : 4.7/5 (898 download)

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Book Synopsis Applied Deep Learning with Python by : Alex Galea

Download or read book Applied Deep Learning with Python written by Alex Galea and published by Packt Publishing Ltd. This book was released on 2018-08-31 with total page 317 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on guide to deep learning that’s filled with intuitive explanations and engaging practical examples Key Features Designed to iteratively develop the skills of Python users who don’t have a data science background Covers the key foundational concepts you’ll need to know when building deep learning systems Full of step-by-step exercises and activities to help build the skills that you need for the real-world Book Description Taking an approach that uses the latest developments in the Python ecosystem, you’ll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We’ll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It’s okay if these terms seem overwhelming; we’ll show you how to put them to work. We’ll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It’s after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. By guiding you through a trained neural network, we’ll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We’ll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively. What you will learn Discover how you can assemble and clean your very own datasets Develop a tailored machine learning classification strategy Build, train and enhance your own models to solve unique problems Work with production-ready frameworks like Tensorflow and Keras Explain how neural networks operate in clear and simple terms Understand how to deploy your predictions to the web Who this book is for If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.

Programming with TensorFlow

Author : Kolla Bhanu Prakash
Publisher : Springer Nature
ISBN 13 : 3030570770
Total Pages : 190 pages
Book Rating : 4.0/5 (35 download)

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Book Synopsis Programming with TensorFlow by : Kolla Bhanu Prakash

Download or read book Programming with TensorFlow written by Kolla Bhanu Prakash and published by Springer Nature. This book was released on 2021-01-22 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book provides an end-to-end guide to TensorFlow, the leading open source software library that helps you build and train neural networks for deep learning, Natural Language Processing (NLP), speech recognition, and general predictive analytics. The book provides a hands-on approach to TensorFlow fundamentals for a broad technical audience—from data scientists and engineers to students and researchers. The authors begin by working through some basic examples in TensorFlow before diving deeper into topics such as CNN, RNN, LSTM, and GNN. The book is written for those who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. The authors demonstrate TensorFlow projects on Single Board Computers (SBCs).

Python: Advanced Predictive Analytics

Author : Joseph Babcock
Publisher : Packt Publishing Ltd
ISBN 13 : 1788993039
Total Pages : 661 pages
Book Rating : 4.7/5 (889 download)

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Book Synopsis Python: Advanced Predictive Analytics by : Joseph Babcock

Download or read book Python: Advanced Predictive Analytics written by Joseph Babcock and published by Packt Publishing Ltd. This book was released on 2017-12-27 with total page 661 pages. Available in PDF, EPUB and Kindle. Book excerpt: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications About This Book A step-by-step guide to predictive modeling including lots of tips, tricks, and best practices Learn how to use popular predictive modeling algorithms such as Linear Regression, Decision Trees, Logistic Regression, and Clustering Master open source Python tools to build sophisticated predictive models Who This Book Is For This book is designed for business analysts, BI analysts, data scientists, or junior level data analysts who are ready to move on from a conceptual understanding of advanced analytics and become an expert in designing and building advanced analytics solutions using Python. If you are familiar with coding in Python (or some other programming/statistical/scripting language) but have never used or read about predictive analytics algorithms, this book will also help you. What You Will Learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis In Detail Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You'll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: 1. Learning Predictive Analytics with Python 2. Mastering Predictive Analytics with Python Style and approach This course aims to create a smooth learning path that will teach you how to effectively perform predictive analytics using Python. Through this comprehensive course, you'll learn the basics of predictive analytics and progress to predictive modeling in the modern world.

TensorFlow: Powerful Predictive Analytics with TensorFlow

Author : Md. Rezaul Karim
Publisher : Packt Publishing Ltd
ISBN 13 : 1789130425
Total Pages : 165 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis TensorFlow: Powerful Predictive Analytics with TensorFlow by : Md. Rezaul Karim

Download or read book TensorFlow: Powerful Predictive Analytics with TensorFlow written by Md. Rezaul Karim and published by Packt Publishing Ltd. This book was released on 2018-03-14 with total page 165 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to solve real life problems using different methods like logic regression, random forests and SVM’s with TensorFlow. Key Features Understand predictive analytics along with its challenges and best practices Embedded with assessments that will help you revise the concepts you have learned in this book Book Description Predictive analytics discovers hidden patterns from structured and unstructured data for automated decision making in business intelligence. Predictive decisions are becoming a huge trend worldwide, catering to wide industry sectors by predicting which decisions are more likely to give maximum results. TensorFlow, Google’s brainchild, is immensely popular and extensively used for predictive analysis. This book is a quick learning guide on all the three types of machine learning, that is, supervised, unsupervised, and reinforcement learning with TensorFlow. This book will teach you predictive analytics for high-dimensional and sequence data. In particular, you will learn the linear regression model for regression analysis. You will also learn how to use regression for predicting continuous values. You will learn supervised learning algorithms for predictive analytics. You will explore unsupervised learning and clustering using K-meansYou will then learn how to predict neighborhoods using K-means, and then, see another example of clustering audio clips based on their audio features. This book is ideal for developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow. This book is embedded with useful assessments that will help you revise the concepts you have learned in this book. What you will learn Learn TensorFlow features in a real-life problem, followed by detailed TensorFlow installation and configuration Explore computation graphs, data, and programming models also get an insight into an example of implementing linear regression model for predictive analytics Solve the Titanic survival problem using logistic regression, random forests, and SVMs for predictive analytics Dig deeper into predictive analytics and find out how to take advantage of it to cluster records belonging to the certain group or class for a dataset of unsupervised observations Learn several examples of how to apply reinforcement learning algorithms for developing predictive models on real-life datasets Who this book is for This book is aimed at developers, data analysts, machine learning practitioners, and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow.

Hands-On Predictive Analytics with Python

Author : Alvaro Fuentes
Publisher : Packt Publishing Ltd
ISBN 13 : 1789134544
Total Pages : 320 pages
Book Rating : 4.7/5 (891 download)

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Book Synopsis Hands-On Predictive Analytics with Python by : Alvaro Fuentes

Download or read book Hands-On Predictive Analytics with Python written by Alvaro Fuentes and published by Packt Publishing Ltd. This book was released on 2018-12-28 with total page 320 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step guide to build high performing predictive applications Key FeaturesUse the Python data analytics ecosystem to implement end-to-end predictive analytics projectsExplore advanced predictive modeling algorithms with an emphasis on theory with intuitive explanationsLearn to deploy a predictive model's results as an interactive applicationBook Description Predictive analytics is an applied field that employs a variety of quantitative methods using data to make predictions. It involves much more than just throwing data onto a computer to build a model. This book provides practical coverage to help you understand the most important concepts of predictive analytics. Using practical, step-by-step examples, we build predictive analytics solutions while using cutting-edge Python tools and packages. The book's step-by-step approach starts by defining the problem and moves on to identifying relevant data. We will also be performing data preparation, exploring and visualizing relationships, building models, tuning, evaluating, and deploying model. Each stage has relevant practical examples and efficient Python code. You will work with models such as KNN, Random Forests, and neural networks using the most important libraries in Python's data science stack: NumPy, Pandas, Matplotlib, Seaborn, Keras, Dash, and so on. In addition to hands-on code examples, you will find intuitive explanations of the inner workings of the main techniques and algorithms used in predictive analytics. By the end of this book, you will be all set to build high-performance predictive analytics solutions using Python programming. What you will learnGet to grips with the main concepts and principles of predictive analyticsLearn about the stages involved in producing complete predictive analytics solutionsUnderstand how to define a problem, propose a solution, and prepare a datasetUse visualizations to explore relationships and gain insights into the datasetLearn to build regression and classification models using scikit-learnUse Keras to build powerful neural network models that produce accurate predictionsLearn to serve a model's predictions as a web applicationWho this book is for This book is for data analysts, data scientists, data engineers, and Python developers who want to learn about predictive modeling and would like to implement predictive analytics solutions using Python's data stack. People from other backgrounds who would like to enter this exciting field will greatly benefit from reading this book. All you need is to be proficient in Python programming and have a basic understanding of statistics and college-level algebra.

Fundamentals of Machine Learning for Predictive Data Analytics, second edition

Author : John D. Kelleher
Publisher : MIT Press
ISBN 13 : 0262361108
Total Pages : 853 pages
Book Rating : 4.2/5 (623 download)

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Book Synopsis Fundamentals of Machine Learning for Predictive Data Analytics, second edition by : John D. Kelleher

Download or read book Fundamentals of Machine Learning for Predictive Data Analytics, second edition written by John D. Kelleher and published by MIT Press. This book was released on 2020-10-20 with total page 853 pages. Available in PDF, EPUB and Kindle. Book excerpt: The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.

HEPATITIS C: Classification and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI

Author : Vivian Siahaan
Publisher : BALIGE PUBLISHING
ISBN 13 :
Total Pages : 319 pages
Book Rating : 4./5 ( download)

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Book Synopsis HEPATITIS C: Classification and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI by : Vivian Siahaan

Download or read book HEPATITIS C: Classification and Prediction Using Scikit-Learn, Keras, and TensorFlow with Python GUI written by Vivian Siahaan and published by BALIGE PUBLISHING. This book was released on 2023-08-19 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this comprehensive project focusing on Hepatitis C classification and prediction, the journey begins with a meticulous exploration of the dataset. Through Python, Scikit-Learn, Keras, and TensorFlow, the project aims to develop an effective model to predict Hepatitis C based on given features. The dataset's attributes are systematically examined, and their distributions are analyzed to uncover insights into potential correlations and patterns. The subsequent step involves categorizing the feature distributions. This phase sheds light on the underlying characteristics of each attribute, facilitating the understanding of their roles in influencing the target variable. This categorization lays the foundation for feature scaling and preprocessing, ensuring that the data is optimized for machine learning. The core of the project revolves around the development of machine learning models. Employing Scikit-Learn, various classification algorithms are applied, including K-Nearest Neighbors (KNN), Decision Trees, Random Forests, Naive Bayes, Gradient Boosting, AdaBoost, Light Gradient Boosting, Multi-Layer Perceptron, and XGBoost. The models are fine-tuned using Grid Search to optimize hyperparameters, enhancing their performance and generalization capability. Taking the project a step further, deep learning techniques are harnessed to tackle the Hepatitis C classification challenge. A key component is the construction of an Artificial Neural Network (ANN) using Keras and TensorFlow. This ANN leverages layers of interconnected nodes to learn complex patterns within the data. LSTM, FNN, RNN, DBN, and Autoencoders are also explored, offering a comprehensive understanding of deep learning's versatility. To evaluate the models' performances, an array of metrics are meticulously employed. Metrics such as accuracy, precision, recall, F1-score, and AUC-ROC are meticulously calculated. The significance of each metric is meticulously explained, underpinning the assessment of a model's true predictive power and its potential weaknesses. The evaluation phase emerges as a pivotal aspect, accentuated by an array of comprehensive metrics. Performance assessment encompasses metrics such as accuracy, precision, recall, F1-score, and ROC-AUC. Cross-validation and learning curves are strategically employed to mitigate overfitting and ensure model generalization. Furthermore, visual aids such as ROC curves and confusion matrices provide a lucid depiction of the models' interplay between sensitivity and specificity. The culmination of the project involves the creation of a user-friendly Graphical User Interface (GUI) using PyQt. The GUI enables users to interact seamlessly with the models, facilitating data input, model selection, and prediction execution. A detailed description of the GUI's components, including buttons, checkboxes, and interactive plots, highlights its role in simplifying the entire classification process. In a comprehensive journey of exploration, experimentation, and analysis, this project effectively marries data science and machine learning. By thoroughly examining the dataset, engineering features, utilizing a diverse range of machine learning models, harnessing the capabilities of deep learning, evaluating performance metrics, and creating an intuitive GUI, the project encapsulates the multi-faceted nature of modern data-driven endeavors.

Machine Learning with R

Author : Brett Lantz
Publisher : Packt Publishing Ltd
ISBN 13 : 1788291557
Total Pages : 459 pages
Book Rating : 4.7/5 (882 download)

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Book Synopsis Machine Learning with R by : Brett Lantz

Download or read book Machine Learning with R written by Brett Lantz and published by Packt Publishing Ltd. This book was released on 2019-04-15 with total page 459 pages. Available in PDF, EPUB and Kindle. Book excerpt: Solve real-world data problems with R and machine learning Key Features Third edition of the bestselling, widely acclaimed R machine learning book, updated and improved for R 3.6 and beyond Harness the power of R to build flexible, effective, and transparent machine learning models Learn quickly with a clear, hands-on guide by experienced machine learning teacher and practitioner, Brett Lantz Book Description Machine learning, at its core, is concerned with transforming data into actionable knowledge. R offers a powerful set of machine learning methods to quickly and easily gain insight from your data. Machine Learning with R, Third Edition provides a hands-on, readable guide to applying machine learning to real-world problems. Whether you are an experienced R user or new to the language, Brett Lantz teaches you everything you need to uncover key insights, make new predictions, and visualize your findings. This new 3rd edition updates the classic R data science book to R 3.6 with newer and better libraries, advice on ethical and bias issues in machine learning, and an introduction to deep learning. Find powerful new insights in your data; discover machine learning with R. What you will learn Discover the origins of machine learning and how exactly a computer learns by example Prepare your data for machine learning work with the R programming language Classify important outcomes using nearest neighbor and Bayesian methods Predict future events using decision trees, rules, and support vector machines Forecast numeric data and estimate financial values using regression methods Model complex processes with artificial neural networks — the basis of deep learning Avoid bias in machine learning models Evaluate your models and improve their performance Connect R to SQL databases and emerging big data technologies such as Spark, H2O, and TensorFlow Who this book is for Data scientists, students, and other practitioners who want a clear, accessible guide to machine learning with R.

Intelligent Techniques for Predictive Data Analytics

Author : Neha Singh
Publisher : John Wiley & Sons
ISBN 13 : 1394227973
Total Pages : 276 pages
Book Rating : 4.3/5 (942 download)

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Book Synopsis Intelligent Techniques for Predictive Data Analytics by : Neha Singh

Download or read book Intelligent Techniques for Predictive Data Analytics written by Neha Singh and published by John Wiley & Sons. This book was released on 2024-06-21 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive resource covering tools and techniques used for predictive analytics with practical applications across various industries Intelligent Techniques for Predictive Data Analytics provides an in-depth introduction of the tools and techniques used for predictive analytics, covering applications in cyber security, network security, data mining, and machine learning across various industries. Each chapter offers a brief introduction on the subject to make the text accessible regardless of background knowledge. Readers will gain a clear understanding of how to use data processing, classification, and analysis to support strategic decisions, such as optimizing marketing strategies and customer relationship management and recommendation systems, improving general business operations, and predicting occurrence of chronic diseases for better patient management. Traditional data analytics uses dashboards to illustrate trends and outliers, but with large data sets, this process is labor-intensive and time-consuming. This book provides everything readers need to save time by performing deep, efficient analysis without human bias and time constraints. A section on current challenges in the field is also included. Intelligent Techniques for Predictive Data Analytics covers sample topics such as: Models to choose from in predictive modeling, including classification, clustering, forecast, outlier, and time series models Price forecasting, quality optimization, and insect and disease plant and monitoring in agriculture Fraud detection and prevention, credit scoring, financial planning, and customer analytics Big data in smart grids, smart grid analytics, and predictive smart grid quality monitoring, maintenance, and load forecasting Management of uncertainty in predictive data analytics and probable future developments in the field Intelligent Techniques for Predictive Data Analytics is an essential resource on the subject for professionals and researchers working in data science or data management seeking to understand the different models of predictive analytics, along with graduate students studying data science courses and professionals and academics new to the field.

Predictive Analytics with TensorFlow

Author : Md. Rezaul Karim
Publisher :
ISBN 13 : 9781789615494
Total Pages : pages
Book Rating : 4.6/5 (154 download)

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Book Synopsis Predictive Analytics with TensorFlow by : Md. Rezaul Karim

Download or read book Predictive Analytics with TensorFlow written by Md. Rezaul Karim and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Accomplish the power of data in your business by building advanced predictive modelling applications with Tensorflow About This Video A quick guide to gaining hands-on experience with deep learning in different domains such as digit/image classification and text Build your own smart, predictive models with TensorFlow using an easy-to-follow approach Understand deep learning and predictive analytics along with its challenges and best practices In Detail Predictive analytics discovers hidden patterns in structured and unstructured data for automated decision-making in business intelligence. This course will help you build, tune, and deploy predictive models with TensorFlow in three main divisions. The first division covers linear algebra, statistics, and probability theory for predictive modeling. The second division covers developing predictive models via supervised (classification and regression) and unsupervised (clustering) algorithms. It then explains how to develop predictive models for NLP and covers reinforcement learning algorithms. Lastly, this division covers developing a factorization machine-based recommendation system. The third division covers deep learning architectures for advanced predictive analytics, including deep neural networks and recurrent neural networks for high-dimensional and sequence data. Finally, you'll use convolutional neural networks for predictive modeling for emotion recognition, image classification, and sentiment analysis.

Applied Text Analysis with Python

Author : Benjamin Bengfort
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1491962992
Total Pages : 328 pages
Book Rating : 4.4/5 (919 download)

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Book Synopsis Applied Text Analysis with Python by : Benjamin Bengfort

Download or read book Applied Text Analysis with Python written by Benjamin Bengfort and published by "O'Reilly Media, Inc.". This book was released on 2018-06-11 with total page 328 pages. Available in PDF, EPUB and Kindle. Book excerpt: From news and speeches to informal chatter on social media, natural language is one of the richest and most underutilized sources of data. Not only does it come in a constant stream, always changing and adapting in context; it also contains information that is not conveyed by traditional data sources. The key to unlocking natural language is through the creative application of text analytics. This practical book presents a data scientist’s approach to building language-aware products with applied machine learning. You’ll learn robust, repeatable, and scalable techniques for text analysis with Python, including contextual and linguistic feature engineering, vectorization, classification, topic modeling, entity resolution, graph analysis, and visual steering. By the end of the book, you’ll be equipped with practical methods to solve any number of complex real-world problems. Preprocess and vectorize text into high-dimensional feature representations Perform document classification and topic modeling Steer the model selection process with visual diagnostics Extract key phrases, named entities, and graph structures to reason about data in text Build a dialog framework to enable chatbots and language-driven interaction Use Spark to scale processing power and neural networks to scale model complexity

Deep Learning with TensorFlow

Author : Giancarlo Zaccone
Publisher : Packt Publishing Ltd
ISBN 13 : 1788831837
Total Pages : 483 pages
Book Rating : 4.7/5 (888 download)

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Book Synopsis Deep Learning with TensorFlow by : Giancarlo Zaccone

Download or read book Deep Learning with TensorFlow written by Giancarlo Zaccone and published by Packt Publishing Ltd. This book was released on 2018-03-30 with total page 483 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Gain real-world contextualization through some deep learning problems concerning research and application Book Description Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. What you will learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications Who this book is for The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.