Machine Learning 101 With Scikit Learn And Statsmodels
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Machine Learning 101 With Scikit Learn And Statsmodels
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Author : 365 Careers
language : en
Publisher:
Release Date : 2019
Machine Learning 101 With Scikit Learn And Statsmodels written by 365 Careers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
New to machine learning? This is the place to start: Linear regression, Logistic regression, and Cluster Analysis About This Video Learn machine learning with StatsModels and sklearn Apply machine learning skills to solve real-world business cases Get started with linear regression, logistic regression, and cluster analysis In Detail Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels . If you require support please email: [email protected].
Practical Machine Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Apress
Release Date : 2017-12-20
Practical Machine Learning With Python written by Dipanjan Sarkar and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-20 with Computers categories.
Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosystem, this book is your perfect companion for learning the art and science of machine learning to become a successful practitioner. The concepts, techniques, tools, frameworks, and methodologies used in this book will teach you how to think, design, build, and execute machine learning systems and projects successfully. Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code. Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts and applications, followed by a tour of the entire Python machine learning ecosystem. Brief guides for useful machine learning tools, libraries andframeworks are also covered. Part 2 details standard machine learning pipelines, with an emphasis on data processing analysis, feature engineering, and modeling. You will learn how to process, wrangle, summarize and visualize data in its various forms. Feature engineering and selection methodologies will be covered in detail with real-world datasets followed by model building, tuning, interpretation and deployment. Part 3 explores multiple real-world case studies spanning diverse domains and industries like retail, transportation, movies, music, marketing, computer vision and finance. For each case study, you will learn the application of various machine learning techniques and methods. The hands-on examples will help you become familiar with state-of-the-art machine learning tools and techniques and understand what algorithms are best suited for any problem. Practical Machine Learning with Python will empower you to start solving your own problems with machine learning today! What You'll Learn Execute end-to-end machine learning projects and systems Implement hands-on examples with industry standard, open source, robust machine learning tools and frameworks Review case studies depicting applications of machine learning and deep learning on diverse domains and industries Apply a wide range of machine learning models including regression, classification, and clustering. Understand and apply the latest models and methodologies from deep learning including CNNs, RNNs, LSTMs and transfer learning. Who This Book Is For IT professionals, analysts, developers, data scientists, engineers, graduate students
Machine Learning For Business Analytics
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Author : Galit Shmueli
language : en
Publisher: John Wiley & Sons
Release Date : 2025-06-02
Machine Learning For Business Analytics written by Galit Shmueli and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-02 with Computers categories.
Machine Learning for Business Analytics: Concepts, Techniques, and Applications in Python is a comprehensive introduction to and an overview of the methods that underlie modern AI. This best-selling textbook covers both statistical and machine learning (AI) algorithms for prediction, classification, visualization, dimension reduction, rule mining, recommendations, clustering, text mining, experimentation, network analytics and generative AI. Along with hands-on exercises and real-life case studies, it also discusses managerial and ethical issues for responsible use of machine learning techniques. This is the second Python edition of Machine Learning for Business Analytics. This edition also includes: A new chapter on generative AI (large language models or LLMs, and image generation) An expanded chapter on deep learning A new chapter on experimental feedback techniques including A/B testing, uplift modeling, and reinforcement learning A new chapter on responsible data science Updates and new material based on feedback from instructors teaching MBA, Masters in Business Analytics and related programs, undergraduate, diploma and executive courses, and from their students A full chapter of cases demonstrating applications for the machine learning techniques End-of-chapter exercises with data A companion website with more than two dozen data sets, and instructor materials including exercise solutions, slides, and case solutions This textbook is an ideal resource for upper-level undergraduate and graduate level courses in AI, data science, predictive analytics, and business analytics. It is also an excellent reference for analysts, researchers, and data science practitioners working with quantitative data in management, finance, marketing, operations management, information systems, computer science, and information technology.
Numpy Essentials
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Author : Leo (Liang-Huan) Chin
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-04-28
Numpy Essentials written by Leo (Liang-Huan) Chin 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 2016-04-28 with Computers categories.
Boost your scientific and analytic capabilities in no time at all by discovering how to build real-world applications with NumPy About This Book Optimize your Python scripts with powerful NumPy modules Explore the vast opportunities to build outstanding scientific/ analytical modules by yourself Packed with rich examples to help you master NumPy arrays and universal functions Who This Book Is For If you are an experienced Python developer who intends to drive your numerical and scientific applications with NumPy, this book is for you. Prior experience or knowledge of working with the Python language is required. What You Will Learn Manipulate the key attributes and universal functions of NumPy Utilize matrix and mathematical computation using linear algebra modules Implement regression and curve fitting for models Perform time frequency / spectral density analysis using the Fourier Transform modules Collate with the distutils and setuptools modules used by other Python libraries Establish Cython with NumPy arrays Write extension modules for NumPy code using the C API Build sophisticated data structures using NumPy array with libraries such as Panda and Scikits In Detail In today's world of science and technology, it's all about speed and flexibility. When it comes to scientific computing, NumPy tops the list. NumPy gives you both the speed and high productivity you need. This book will walk you through NumPy using clear, step-by-step examples and just the right amount of theory. We will guide you through wider applications of NumPy in scientific computing and will then focus on the fundamentals of NumPy, including array objects, functions, and matrices, each of them explained with practical examples. You will then learn about different NumPy modules while performing mathematical operations such as calculating the Fourier Transform; solving linear systems of equations, interpolation, extrapolation, regression, and curve fitting; and evaluating integrals and derivatives. We will also introduce you to using Cython with NumPy arrays and writing extension modules for NumPy code using the C API. This book will give you exposure to the vast NumPy library and help you build efficient, high-speed programs using a wide range of mathematical features. Style and approach This quick guide will help you get to grips with the nitty-gritties of NumPy using with practical programming examples. Each topic is explained in both theoretical and practical ways with hands-on examples providing you efficient way of learning and adequate knowledge to support your professional work.
Data Science And Machine Learning
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Author : Ms. T. Mangayarkarasi
language : en
Publisher: RK Publication
Release Date : 2024-05-31
Data Science And Machine Learning written by Ms. T. Mangayarkarasi and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-05-31 with Computers categories.
Data Science and Machine Learning introduction to the fundamental concepts and techniques used in the fields of data science and machine learning. This essential topics such as data preprocessing, exploratory data analysis, statistical methods, and various machine learning algorithms, along with practical applications. Designed for beginners and intermediate learners, it offers a step-by-step guide to understanding data-driven decision-making and how to apply machine learning models to real-world problems, equipping readers with the skills to excel in the rapidly growing field of data science.
Intelligent Sensors For Positioning Tracking Monitoring Navigation And Smart Sensing In Smart Cities
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Author : Tiancheng Li
language : en
Publisher: MDPI
Release Date : 2021-03-04
Intelligent Sensors For Positioning Tracking Monitoring Navigation And Smart Sensing In Smart Cities written by Tiancheng Li and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-04 with Technology & Engineering categories.
The rapid development of advanced, arguably, intelligent sensors and their massive deployment provide a foundation for new paradigms to combat the challenges that arise in significant tasks such as positioning, tracking, navigation, and smart sensing in various environments. Relevant advances in artificial intelligence (AI) and machine learning (ML) are also finding rapid adoption by industry and fan the fire. Consequently, research on intelligent sensing systems and technologies has attracted considerable attention during the past decade, leading to a variety of effective applications related to intelligent transportation, autonomous vehicles, wearable computing, wireless sensor networks (WSN), and the internet of things (IoT). In particular, the sensors community has a great interest in novel, intelligent information fusion, and data mining methods coupling AI and ML for substantial performance enhancement, especially for the challenging scenarios that make traditional approaches inappropriate. This reprint book has collected 14 excellent papers that represent state-of-the-art achievements in the relevant topics and provides cutting-edge coverage of recent advances in sensor signal and data mining techniques, algorithms, and approaches, particularly applied for positioning, tracking, navigation, and smart sensing.
Python For Marketing Research And Analytics
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Author : Jason S. Schwarz
language : en
Publisher: Springer Nature
Release Date : 2020-11-03
Python For Marketing Research And Analytics written by Jason S. Schwarz and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-03 with Computers categories.
This book provides an introduction to quantitative marketing with Python. The book presents a hands-on approach to using Python for real marketing questions, organized by key topic areas. Following the Python scientific computing movement toward reproducible research, the book presents all analyses in Colab notebooks, which integrate code, figures, tables, and annotation in a single file. The code notebooks for each chapter may be copied, adapted, and reused in one's own analyses. The book also introduces the usage of machine learning predictive models using the Python sklearn package in the context of marketing research. This book is designed for three groups of readers: experienced marketing researchers who wish to learn to program in Python, coming from tools and languages such as R, SAS, or SPSS; analysts or students who already program in Python and wish to learn about marketing applications; and undergraduate or graduate marketing students with little or no programming background. It presumes only an introductory level of familiarity with formal statistics and contains a minimum of mathematics.
Mastering Pandas
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Author : Ashish Kumar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-10-25
Mastering Pandas written by Ashish Kumar 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 2019-10-25 with Computers categories.
Perform advanced data manipulation tasks using pandas and become an expert data analyst. Key FeaturesManipulate and analyze your data expertly using the power of pandasWork with missing data and time series data and become a true pandas expertIncludes expert tips and techniques on making your data analysis tasks easierBook Description pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process. What you will learnSpeed up your data analysis by importing data into pandasKeep relevant data points by selecting subsets of your dataCreate a high-quality dataset by cleaning data and fixing missing valuesCompute actionable analytics with grouping and aggregation in pandasMaster time series data analysis in pandasMake powerful reports in pandas using Jupyter notebooksWho this book is for This book is for data scientists, analysts and Python developers who wish to explore advanced data analysis and scientific computing techniques using pandas. Some fundamental understanding of Python programming and familiarity with the basic data analysis concepts is all you need to get started with this book.
Machine Learning With Scikit Learn Quick Start Guide
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Author : Kevin Jolly
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-30
Machine Learning With Scikit Learn Quick Start Guide written by Kevin Jolly 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 2018-10-30 with Mathematics categories.
Deploy supervised and unsupervised machine learning algorithms using scikit-learn to perform classification, regression, and clustering. Key FeaturesBuild your first machine learning model using scikit-learnTrain supervised and unsupervised models using popular techniques such as classification, regression and clusteringUnderstand how scikit-learn can be applied to different types of machine learning problemsBook Description Scikit-learn is a robust machine learning library for the Python programming language. It provides a set of supervised and unsupervised learning algorithms. This book is the easiest way to learn how to deploy, optimize, and evaluate all of the important machine learning algorithms that scikit-learn provides. This book teaches you how to use scikit-learn for machine learning. You will start by setting up and configuring your machine learning environment with scikit-learn. To put scikit-learn to use, you will learn how to implement various supervised and unsupervised machine learning models. You will learn classification, regression, and clustering techniques to work with different types of datasets and train your models. Finally, you will learn about an effective pipeline to help you build a machine learning project from scratch. By the end of this book, you will be confident in building your own machine learning models for accurate predictions. What you will learnLearn how to work with all scikit-learn's machine learning algorithmsInstall and set up scikit-learn to build your first machine learning modelEmploy Unsupervised Machine Learning Algorithms to cluster unlabelled data into groupsPerform classification and regression machine learningUse an effective pipeline to build a machine learning project from scratchWho this book is for This book is for aspiring machine learning developers who want to get started with scikit-learn. Intermediate knowledge of Python programming and some fundamental knowledge of linear algebra and probability will help.
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.