Machine Learning With Scikit Learn Quick Start Guide

DOWNLOAD eBooks

Download Machine Learning With Scikit Learn Quick Start Guide PDF/ePub or read online books in Mobi eBooks. Click Download or Read Online button to get Machine Learning With Scikit Learn Quick Start Guide book now. This website allows unlimited access to, at the time of writing, more than 1.5 million titles, including hundreds of thousands of titles in various foreign languages. If the content not found or just blank you must refresh this page

Machine Learning With Scikit Learn Quick Start Guide

DOWNLOAD eBooks

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.

Python Scikit Learn For Beginners

DOWNLOAD eBooks

Author : Ai Publishing
language : en
Publisher:
Release Date : 2021-03-28

Python Scikit Learn For Beginners written by Ai Publishing and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-03-28 with categories.

Python for Data Scientists -- Scikit-Learn SpecializationScikit-Learn, also known as Sklearn, is a free, open-source machine learning (ML) library used for the Python language. In February 2010, this library was first made public. And in less than three years, it became one of the most popular machine learning libraries on Github.Scikit-learn is the best place to start for access to easy-to-use, top-notch implementations of popular algorithms. This library speeds up the development of ML models.The main features of the Scikit-learn library are regression, classification, and clustering algorithms (random forests, K-means, gradient boosting, DBSCAN, AND support vector machines). The Scikit-learn library also integrates well with other Python libraries, such as NumPy, Pandas, IPython, SciPy, Sympy, and Matplotlib, to fulfill different tasks.Python for Data Scientists: Scikit-Learn Specialization presents you with a hands-on, simple approach to learn Scikit-learn fast.How Is This Book Different?Most Python books assume you know how to code using Pandas, NumPy, and Matplotlib. But this book does not. The author spends a lot of time teaching you how actually write the simplest codes in Python to achieve machine learning models.In-depth coverage of the Scikit-learn library starts from the third chapter itself. Jumping straight to Scikit-learn makes it easy for you to follow along. The other advantage is Jupyter Notebook is used to write and explain the code right through this book.You can access the datasets used in this book easily by downloading them at runtime. You can also access them through the Datasets folder in the SharePoint and GitHub repositories.You also get to work on three hands-on mini-projects: Spam Email Detection with Scikit-Learn IMDB Movies Sentimental Analysis Image Classification with Scikit-Learn The scripts, graphs, and images in the book are clear and provide easy-to-understand visuals to the text description. If you're new to data science, you will find this book a great option for self-study. Overall, you can count on this learning by doing book to help you accomplish your data science career goals faster.The topics covered include: Introduction to Scikit-Learn and Other Machine Learning Libraries Environment Setup and Python Crash Course Data Preprocessing with Scikit-Learn Feature Selection with Python Scikit-Learn Library Solving Regression Problems in Machine Learning Using Sklearn Library Solving Classification Problems in Machine Learning Using Sklearn Library Clustering Data with Scikit-Learn Library Dimensionality Reduction with PCA and LDA Using Sklearn Selecting Best Models with Scikit-Learn Natural Language Processing with Scikit-Learn Image Classification with Scikit-Learn Hit the BUY NOW button and start your Data Science Learning journey.

Learn Machine Learning For Finance

DOWNLOAD eBooks

Author : Jason Test
language : en
Publisher:
Release Date : 2020-12-07

Learn Machine Learning For Finance written by Jason Test and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-12-07 with Computers categories.

Escape the rat race now! Would you like to learn the Python Programming Language and machine learning in 7 days? Do you want to increase your trading thanks to Python and applied AI? If so, keep reading: this bundle book is for you! Today, thanks to computer programming and Python we can work with sophisticated machines that can study human behavior and identify underlying human behavioral patterns. Scientists can predict effectively what products and services consumers are interested in. You can also create various quantitative and algorithmic trading strategies using Python. Technology has become an asset in finance: financial institutions are now evolving to technology companies rather than only staying occupied with just the financial aspects. is getting increasingly challenging for traditional businesses to retain their customers without adopting one or more of the astonishing and cutting-edge technology explained in this book. LEARN MACHINE LEARNING FOR FINANCE will introduce you many selected tips and breaking down the basics of coding applied to finance. You will discover as a beginner the world of data science, machine learning and artificial intelligence with step-by-step guides that will guide you during the code-writing learning process. The following list is just a tiny fraction of what you will learn in this bundle STOCK MARKET INVESTING FOR BEGINNERS ✅ Options Trading Strategies that guarantee real results in all market conditions ✅ Top 7 endorsed indicators of a successful investment ✅ The Bull & Bear Game ✅ Learn about the 3 best charts patterns to fluctuations of stock prices OPTIONS TRADING FOR BEGINNERS ✅How Swing trading differs from Day trading in terms of risk-aversion ✅How your money should be invested and which trade is more profitable ✅Swing and Day trading proven indicators to learn investment timing ✅The secret DAY trading strategies leading to a gain of $ 9,000 per month and more than $100,000 per year. PYTHON CRASH COURSE ✅A Proven Method to Write your First Program in 7 Days ✅3 Common Mistakes to Avoid when You Start Coding ✅Importing Financial Data Into Python ✅7 Most effective Machine Learning Algorithms ✅ Build machine learning models for trading Even if you have never written a programming code before, you will quickly grasp the basics thanks to visual charts and guidelines for coding. Approached properly artificial intelligence, can provide significant benefits for the firm, its customers and wider society. Today is the best day to start programming like a pro and help your trading online! For those trading with leverage, looking for step-by-step process to take a controlled approach and manage risk, this bundle book is the answer If you really wish to LEARN MACHINE LEARNING FOR FINANCE and master its language, please click the BUY NOW button.

Python Machine Learning

DOWNLOAD eBooks

Author : Rajender Kumar
language : en
Publisher: Jamba Academy
Release Date : 2023-03-02

Python Machine Learning written by Rajender Kumar and has been published by Jamba Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-02 with Computers categories.

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

Python Machine Learning

DOWNLOAD eBooks

Author : Railey Brandon
language : en
Publisher: Roland Bind
Release Date : 2019-04-25

Python Machine Learning written by Railey Brandon and has been published by Roland Bind this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-25 with Computers categories.

★☆Have you come across the terms machine learning and neural networks in most articles you have recently read? Do you also want to learn how to build a machine learning model that will answer your questions within a blink of your eyes?☆★ If you responded yes to any of the above questions, you have come to the right place. Machine learning is an incredibly dense topic. It's hard to imagine condensing it into an easily readable and digestible format. However, this book aims to do exactly that. Machine learning and artificial intelligence have been used in different machines and applications to improve the user's experience. One can also use machine learning to make data analysis and predicting the output for some data sets easy. All you need to do is choose the right algorithm, train the model and test the model before you apply it on any real-world tool. It is that simple isn't it? ★★Apart from this, you will also learn more about★★ ♦ The Different Types Of Learning Algorithm That You Can Expect To Encounter ♦ The Numerous Applications Of Machine Learning And Deep Learning ♦ The Best Practices For Picking Up Neural Networks ♦ What Are The Best Languages And Libraries To Work With ♦ The Various Problems That You Can Solve With Machine Learning Algorithms ♦ And much more... Well, you can do it faster if you use Python. This language has made it easy for any user, even an amateur, to build a strong machine learning model since it has numerous directories and libraries that make it easy for one to build a model. Do you want to know how to build a machine learning model and a neural network? So, what are you waiting for? Grab a copy of this book now!

In Depth Tutorials Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui

DOWNLOAD eBooks

Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2021-06-05

In Depth Tutorials Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-06-05 with Computers categories.

BOOK 1: LEARN FROM SCRATCH MACHINE LEARNING WITH PYTHON GUI In this book, you will learn how to use NumPy, Pandas, OpenCV, Scikit-Learn and other libraries to how to plot graph and to process digital image. Then, you will learn how to classify features using Perceptron, Adaline, Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and K-Nearest Neighbor (KNN) models. You will also learn how to extract features using Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), Kernel Principal Component Analysis (KPCA) algorithms and use them in machine learning. In Chapter 1, you will learn: Tutorial Steps To Create A Simple GUI Application, Tutorial Steps to Use Radio Button, Tutorial Steps to Group Radio Buttons, Tutorial Steps to Use CheckBox Widget, Tutorial Steps to Use Two CheckBox Groups, Tutorial Steps to Understand Signals and Slots, Tutorial Steps to Convert Data Types, Tutorial Steps to Use Spin Box Widget, Tutorial Steps to Use ScrollBar and Slider, Tutorial Steps to Use List Widget, Tutorial Steps to Select Multiple List Items in One List Widget and Display It in Another List Widget, Tutorial Steps to Insert Item into List Widget, Tutorial Steps to Use Operations on Widget List, Tutorial Steps to Use Combo Box, Tutorial Steps to Use Calendar Widget and Date Edit, and Tutorial Steps to Use Table Widget. In Chapter 2, you will learn: Tutorial Steps To Create A Simple Line Graph, Tutorial Steps To Create A Simple Line Graph in Python GUI, Tutorial Steps To Create A Simple Line Graph in Python GUI: Part 2, Tutorial Steps To Create Two or More Graphs in the Same Axis, Tutorial Steps To Create Two Axes in One Canvas, Tutorial Steps To Use Two Widgets, Tutorial Steps To Use Two Widgets, Each of Which Has Two Axes, Tutorial Steps To Use Axes With Certain Opacity Levels, Tutorial Steps To Choose Line Color From Combo Box, Tutorial Steps To Calculate Fast Fourier Transform, Tutorial Steps To Create GUI For FFT, Tutorial Steps To Create GUI For FFT With Some Other Input Signals, Tutorial Steps To Create GUI For Noisy Signal, Tutorial Steps To Create GUI For Noisy Signal Filtering, and Tutorial Steps To Create GUI For Wav Signal Filtering. In Chapter 3, you will learn: Tutorial Steps To Convert RGB Image Into Grayscale, Tutorial Steps To Convert RGB Image Into YUV Image, Tutorial Steps To Convert RGB Image Into HSV Image, Tutorial Steps To Filter Image, Tutorial Steps To Display Image Histogram, Tutorial Steps To Display Filtered Image Histogram, Tutorial Steps To Filter Image With CheckBoxes, Tutorial Steps To Implement Image Thresholding, and Tutorial Steps To Implement Adaptive Image Thresholding. You will also learn: Tutorial Steps To Generate And Display Noisy Image, Tutorial Steps To Implement Edge Detection On Image, Tutorial Steps To Implement Image Segmentation Using Multiple Thresholding and K-Means Algorithm, Tutorial Steps To Implement Image Denoising, Tutorial Steps To Detect Face, Eye, and Mouth Using Haar Cascades, Tutorial Steps To Detect Face Using Haar Cascades with PyQt, Tutorial Steps To Detect Eye, and Mouth Using Haar Cascades with PyQt, Tutorial Steps To Extract Detected Objects, Tutorial Steps To Detect Image Features Using Harris Corner Detection, Tutorial Steps To Detect Image Features Using Shi-Tomasi Corner Detection, Tutorial Steps To Detect Features Using Scale-Invariant Feature Transform (SIFT), and Tutorial Steps To Detect Features Using Features from Accelerated Segment Test (FAST). In Chapter 4, In this tutorial, you will learn how to use Pandas, NumPy and other libraries to perform simple classification using perceptron and Adaline (adaptive linear neuron). The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron, Tutorial Steps To Implement Perceptron with PyQt, Tutorial Steps To Implement Adaline (ADAptive LInear NEuron), and Tutorial Steps To Implement Adaline with PyQt. In Chapter 5, you will learn how to use the scikit-learn machine learning library, which provides a wide variety of machine learning algorithms via a user-friendly Python API and to perform classification using perceptron, Adaline (adaptive linear neuron), and other models. The dataset used is Iris dataset directly from the UCI Machine Learning Repository. You will learn: Tutorial Steps To Implement Perceptron Using Scikit-Learn, Tutorial Steps To Implement Perceptron Using Scikit-Learn with PyQt, Tutorial Steps To Implement Logistic Regression Model, Tutorial Steps To Implement Logistic Regression Model with PyQt, Tutorial Steps To Implement Logistic Regression Model Using Scikit-Learn with PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Using Scikit-Learn, Tutorial Steps To Implement Decision Tree (DT) Using Scikit-Learn, Tutorial Steps To Implement Random Forest (RF) Using Scikit-Learn, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Using Scikit-Learn. In Chapter 6, you will learn how to use Pandas, NumPy, Scikit-Learn, and other libraries to implement different approaches for reducing the dimensionality of a dataset using different feature selection techniques. You will learn about three fundamental techniques that will help us to summarize the information content of a dataset by transforming it onto a new feature subspace of lower dimensionality than the original one. Data compression is an important topic in machine learning, and it helps us to store and analyze the increasing amounts of data that are produced and collected in the modern age of technology. You will learn the following topics: Principal Component Analysis (PCA) for unsupervised data compression, Linear Discriminant Analysis (LDA) as a supervised dimensionality reduction technique for maximizing class separability, Nonlinear dimensionality reduction via Kernel Principal Component Analysis (KPCA). You will learn: Tutorial Steps To Implement Principal Component Analysis (PCA), Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn, Tutorial Steps To Implement Principal Component Analysis (PCA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Linear Discriminant Analysis (LDA), Tutorial Steps To Implement Linear Discriminant Analysis (LDA) with Scikit-Learn, Tutorial Steps To Implement Linear Discriminant Analysis (LDA) Using Scikit-Learn with PyQt, Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn, and Tutorial Steps To Implement Kernel Principal Component Analysis (KPCA) Using Scikit-Learn with PyQt. In Chapter 7, you will learn how to use Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset. You will learn: Tutorial Steps To Load MNIST Dataset, Tutorial Steps To Load MNIST Dataset with PyQt, Tutorial Steps To Implement Perceptron With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Perceptron With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Logistic Regression (LR) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement , Tutorial Steps To Implement Support Vector Machine (SVM) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Support Vector Machine (SVM) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Decision Tree (DT) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement Random Forest (RF) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With PCA Feature Extractor on MNIST Dataset Using PyQt, Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With LDA Feature Extractor on MNIST Dataset Using PyQt, and Tutorial Steps To Implement K-Nearest Neighbor (KNN) Model With KPCA Feature Extractor on MNIST Dataset Using PyQt. BOOK 2: THE PRACTICAL GUIDES ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on recognizing traffic signs using GTSRB dataset, detecting brain tumor using Brain Image MRI dataset, classifying gender, and recognizing facial expression using FER2013 dataset In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, Pandas, NumPy and other libraries to perform prediction on handwritten digits using MNIST dataset with PyQt. You will build a GUI application for this purpose. In Chapter 3, you will learn how to perform recognizing traffic signs using GTSRB dataset from Kaggle. There are several different types of traffic signs like speed limits, no entry, traffic signals, turn left or right, children crossing, no passing of heavy vehicles, etc. Traffic signs classification is the process of identifying which class a traffic sign belongs to. In this Python project, you will build a deep neural network model that can classify traffic signs in image into different categories. With this model, you will be able to read and understand traffic signs which are a very important task for all autonomous vehicles. You will build a GUI application for this purpose. In Chapter 4, you will learn how to perform detecting brain tumor using Brain Image MRI dataset provided by Kaggle (https://www.kaggle.com/navoneel/brain-mri-images-for-brain-tumor-detection) using CNN model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to perform classifying gender using dataset provided by Kaggle (https://www.kaggle.com/cashutosh/gender-classification-dataset) using MobileNetV2 and CNN models. You will build a GUI application for this purpose. In Chapter 6, you will learn how to perform recognizing facial expression using FER2013 dataset provided by Kaggle (https://www.kaggle.com/nicolejyt/facialexpressionrecognition) using CNN model. You will also build a GUI application for this purpose. BOOK 3: STEP BY STEP TUTORIALS ON DEEP LEARNING USING SCIKIT-LEARN, KERAS, AND TENSORFLOW WITH PYTHON GUI In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. Then, you will learn how to use OpenCV, NumPy, and other libraries to perform feature extraction with Python GUI (PyQt). The feature detection techniques used in this chapter are Harris Corner Detection, Shi-Tomasi Corner Detector, and Scale-Invariant Feature Transform (SIFT). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. BOOK 4: Project-Based Approach On DEEP LEARNING Using Scikit-Learn, Keras, And TensorFlow with Python GUI In this book, implement deep learning on detecting vehicle license plates, recognizing sign language, and detecting surface crack using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting vehicle license plates using Car License Plate Detection dataset provided by Kaggle (https://www.kaggle.com/andrewmvd/car-plate-detection/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform sign language recognition using Sign Language Digits Dataset provided by Kaggle (https://www.kaggle.com/ardamavi/sign-language-digits-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting surface crack using Surface Crack Detection provided by Kaggle (https://www.kaggle.com/arunrk7/surface-crack-detection/download). BOOK 5: Hands-On Guide To IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on detecting face mask, classifying weather, and recognizing flower using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting face mask using Face Mask Detection Dataset provided by Kaggle (https://www.kaggle.com/omkargurav/face-mask-dataset/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify weather using Multi-class Weather Dataset provided by Kaggle (https://www.kaggle.com/pratik2901/multiclass-weather-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize flower using Flowers Recognition dataset provided by Kaggle (https://www.kaggle.com/alxmamaev/flowers-recognition/download). BOOK 6: Step by Step Tutorial IMAGE CLASSIFICATION Using Scikit-Learn, Keras, And TensorFlow with PYTHON GUI In this book, implement deep learning-based image classification on classifying monkey species, recognizing rock, paper, and scissor, and classify airplane, car, and ship using TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries. In Chapter 1, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify monkey species using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/slothkong/10-monkey-species/download). In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to recognize rock, paper, and scissor using 10 Monkey Species dataset provided by Kaggle (https://www.kaggle.com/sanikamal/rock-paper-scissors-dataset/download). In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform how to classify airplane, car, and ship using Multiclass-image-dataset-airplane-car-ship dataset provided by Kaggle (https://www.kaggle.com/abtabm/multiclassimagedatasetairplanecar).

Fasttext Quick Start Guide

DOWNLOAD eBooks

Author : Joydeep Bhattacharjee
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-07-26

Fasttext Quick Start Guide written by Joydeep Bhattacharjee 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 2018-07-26 with Computers categories.

Perform efficient fast text representation and classification with Facebook's fastText library Key Features Introduction to Facebook's fastText library for NLP Perform efficient word representations, sentence classification, vector representation Build better, more scalable solutions for text representation and classification Book Description Facebook's fastText library handles text representation and classification, used for Natural Language Processing (NLP). Most organizations have to deal with enormous amounts of text data on a daily basis, and gaining efficient data insights requires powerful NLP tools such as fastText. This book is your ideal introduction to fastText. You will learn how to create fastText models from the command line, without the need for complicated code. You will explore the algorithms that fastText is built on and how to use them for word representation and text classification. Next, you will use fastText in conjunction with other popular libraries and frameworks such as Keras, TensorFlow, and PyTorch. Finally, you will deploy fastText models to mobile devices. By the end of this book, you will have all the required knowledge to use fastText in your own applications at work or in projects. What you will learn Create models using the default command line options in fastText Understand the algorithms used in fastText to create word vectors Combine command line text transformation capabilities and the fastText library to implement a training, validation, and prediction pipeline Explore word representation and sentence classification using fastText Use Gensim and spaCy to load the vectors, transform, lemmatize, and perform other NLP tasks efficiently Develop a fastText NLP classifier using popular frameworks, such as Keras, Tensorflow, and PyTorch Who this book is for This book is for data analysts, data scientists, and machine learning developers who want to perform efficient word representation and sentence classification using Facebook's fastText library. Basic knowledge of Python programming is required.

Caffe2 Quick Start Guide

DOWNLOAD eBooks

Author : Ashwin Nanjappa
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-05-31

Caffe2 Quick Start Guide written by Ashwin Nanjappa and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-05-31 with Computers categories.

Build and train scalable neural network models on various platforms by leveraging the power of Caffe2 Key FeaturesMigrate models trained with other deep learning frameworks on Caffe2Integrate Caffe2 with Android or iOS and implement deep learning models for mobile devicesLeverage the distributed capabilities of Caffe2 to build models that scale easilyBook Description Caffe2 is a popular deep learning library used for fast and scalable training and inference of deep learning models on various platforms. This book introduces you to the Caffe2 framework and shows how you can leverage its power to build, train, and deploy efficient neural network models at scale. It will cover the topics of installing Caffe2, composing networks using its operators, training models, and deploying models to different architectures. It will also show how to import models from Caffe and from other frameworks using the ONNX interchange format. It covers the topic of deep learning accelerators such as CPU and GPU and shows how to deploy Caffe2 models for inference on accelerators using inference engines. Caffe2 is built for deployment to a diverse set of hardware, using containers on the cloud and resource constrained hardware such as Raspberry Pi, which will be demonstrated. By the end of this book, you will be able to not only compose and train popular neural network models with Caffe2, but also be able to deploy them on accelerators, to the cloud and on resource constrained platforms such as mobile and embedded hardware. What you will learnBuild and install Caffe2Compose neural networksTrain neural network on CPU or GPUImport a neural network from CaffeImport deep learning models from other frameworksDeploy models on CPU or GPU accelerators using inference enginesDeploy models at the edge and in the cloudWho this book is for Data scientists and machine learning engineers who wish to create fast and scalable deep learning models in Caffe2 will find this book to be very useful. Some understanding of the basic machine learning concepts and prior exposure to programming languages like C++ and Python will be useful.

Machine Learning With Apache Spark Quick Start Guide

DOWNLOAD eBooks

Author : Jillur Quddus
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-12-26

Machine Learning With Apache Spark Quick Start Guide written by Jillur Quddus 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 2018-12-26 with Computers categories.

Combine advanced analytics including Machine Learning, Deep Learning Neural Networks and Natural Language Processing with modern scalable technologies including Apache Spark to derive actionable insights from Big Data in real-time Key FeaturesMake a hands-on start in the fields of Big Data, Distributed Technologies and Machine LearningLearn how to design, develop and interpret the results of common Machine Learning algorithmsUncover hidden patterns in your data in order to derive real actionable insights and business valueBook Description Every person and every organization in the world manages data, whether they realize it or not. Data is used to describe the world around us and can be used for almost any purpose, from analyzing consumer habits to fighting disease and serious organized crime. Ultimately, we manage data in order to derive value from it, and many organizations around the world have traditionally invested in technology to help process their data faster and more efficiently. But we now live in an interconnected world driven by mass data creation and consumption where data is no longer rows and columns restricted to a spreadsheet, but an organic and evolving asset in its own right. With this realization comes major challenges for organizations: how do we manage the sheer size of data being created every second (think not only spreadsheets and databases, but also social media posts, images, videos, music, blogs and so on)? And once we can manage all of this data, how do we derive real value from it? The focus of Machine Learning with Apache Spark is to help us answer these questions in a hands-on manner. We introduce the latest scalable technologies to help us manage and process big data. We then introduce advanced analytical algorithms applied to real-world use cases in order to uncover patterns, derive actionable insights, and learn from this big data. What you will learnUnderstand how Spark fits in the context of the big data ecosystemUnderstand how to deploy and configure a local development environment using Apache SparkUnderstand how to design supervised and unsupervised learning modelsBuild models to perform NLP, deep learning, and cognitive services using Spark ML librariesDesign real-time machine learning pipelines in Apache SparkBecome familiar with advanced techniques for processing a large volume of data by applying machine learning algorithmsWho this book is for This book is aimed at Business Analysts, Data Analysts and Data Scientists who wish to make a hands-on start in order to take advantage of modern Big Data technologies combined with Advanced Analytics.

Python Machine Learning

DOWNLOAD eBooks

Author : Ryan Turner
language : en
Publisher: Publishing Factory
Release Date : 2020-04-12

Python Machine Learning written by Ryan Turner and has been published by Publishing Factory this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-04-12 with Computers categories.

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!