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Format: 1 online resource (304 pages) : , illustrations (some color).

Edition: 1st edition

ISBN: 1-78646-768-2

Series Statement: Quick answers to common problems

Content: 100 recipes that teach you how to perform various machine learning tasks in the real world About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide Learn about perceptrons and see how they are used to build neural networks Stuck while making sense of images, text, speech, and real estate? This guide will come to your rescue, showing you how to perform machine learning for each one of these using various techniques Who This Book Is For This book is for Python programmers who are looking to use machine-learning algorithms to create real-world applications. This book is friendly to Python beginners, but familiarity with Python programming would certainly be useful to play around with the code. What You Will Learn Explore classification algorithms and apply them to the income bracket estimation problem Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Explore data visualization techniques to interact with your data in diverse ways Find out how to build a recommendation engine Understand how to interact with text data and build models to analyze it Work with speech data and recognize spoken words using Hidden Markov Models Analyze stock market data using Conditional Random Fields Work with image data and build systems for image recognition and biometric face recognition Grasp how to use deep neural networks to build an optical character recognition system In Detail Machine learning is becoming increasingly pervasive in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. With this book, you will learn how to perform various machine learning tasks in different environments. We'll start by exploring a range of real-life scenarios where machine learning can be used, and look at various building blocks. Throughout the book, you'll use a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. You'll discover how to deal with various types of data and explore the differences between machine learning paradigms such as supervised and unsupervised learning. We also cover a range of regression techniques, classification algorithms, predictive modeling, data visualization techniques, recommendation engines, and more with the help of real-world examples. Style and approach ...

Note: Includes index.

Additional Edition: ISBN 1-78646-447-0

Language: English

Subjects: Computer Science

URL: Volltext  (URL des Erstveröffentlichers)

UID:

Format: 1 online resource (437 pages) : , illustrations

Edition: 1st edition

ISBN: 1-78646-967-7

Content: Build real-world Artificial Intelligence applications with Python to intelligently interact with the world around you About This Book Step into the amazing world of intelligent apps using this comprehensive guide Enter the world of Artificial Intelligence, explore it, and create your own applications Work through simple yet insightful examples that will get you up and running with Artificial Intelligence in no time Who This Book Is For This book is for Python developers who want to build real-world Artificial Intelligence applications. This book is friendly to Python beginners, but being familiar with Python would be useful to play around with the code. It will also be useful for experienced Python programmers who are looking to use Artificial Intelligence techniques in their existing technology stacks. What You Will Learn Realize different classification and regression techniques Understand the concept of clustering and how to use it to automatically segment data See how to build an intelligent recommender system Understand logic programming and how to use it Build automatic speech recognition systems Understand the basics of heuristic search and genetic programming Develop games using Artificial Intelligence Learn how reinforcement learning works Discover how to build intelligent applications centered on images, text, and time series data See how to use deep learning algorithms and build applications based on it In Detail Artificial Intelligence is becoming increasingly relevant in the modern world where everything is driven by technology and data. It is used extensively across many fields such as search engines, image recognition, robotics, finance, and so on. We will explore various real-world scenarios in this book and you'll learn about various algorithms that can be used to build Artificial Intelligence applications. During the course of this book, you will find out how to make informed decisions about what algorithms to use in a given context. Starting from the basics of Artificial Intelligence, you will learn how to develop various building blocks using different data mining techniques. You will see how to implement different algorithms to get the best possible results, and will understand how to apply them to real-world scenarios. If you want to add an intelligence layer to any application that's based on images, text, stock market, or some other form of data, this exciting book on Artificial Intelligence will definitely be your guid...

Note: Includes index. , Cover -- Copyright -- Credits -- About the Author -- About the Reviewer -- www.PacktPub.com -- Customer Feedback -- Table of Contents -- Preface -- Chapter 1: Introduction to Artificial Intelligence -- What is Artificial Intelligence? -- Why do we need to study AI? -- Applications of AI -- Branches of AI -- Defining intelligence using Turing Test -- Making machines think like humans -- Building rational agents -- General Problem Solver -- Solving a problem with GPS -- Building an intelligent agent -- Types of models -- Installing Python 3 -- Installing on Ubuntu -- Installing on Mac OS X -- Installing on Windows -- Installing packages -- Loading data -- Summary -- Chapter 2 : Classification and Regression Using Supervised Learning -- Supervised versus unsupervised learning -- What is classification? -- Preprocessing data -- Binarization -- Mean removal -- Scaling -- Normalization -- Label encoding -- Logistic Regression classifier -- Naïve Bayes classifier -- Confusion matrix -- Support Vector Machines -- Classifying income data using Support Vector Machines -- What is Regression? -- Building a single variable regressor -- Building a multivariable regressor -- Estimating housing prices using a Support Vector Regressor -- Summary -- Chapter 3:Predictive Analytics with Ensemble Learning -- What is Ensemble Learning? -- Building learning models with Ensemble Learning -- What are Decision Trees? -- Building a Decision Tree classifier -- What are Random Forests and Extremely Random Forests? -- Building Random Forest and Extremely Random Forest classifiers -- Estimating the confidence measure of the predictions -- Dealing with class imbalance -- Finding optimal training parameters using grid search -- Computing relative feature importance -- Predicting traffic using Extremely Random Forest regressor -- Summary. , Chapter 4:Detecting Patterns with Unsupervised Learning -- What is unsupervised learning? -- Clustering data with K-Means algorithm -- Estimating the number of clusters with Mean Shift algorithm -- Estimating the quality of clustering with silhouette scores -- What are Gaussian Mixture Models? -- Building a classifier based on Gaussian Mixture Models -- Finding subgroups in stock market using Affinity Propagation model -- Segmenting the market based on shopping patterns -- Summary -- Chapter 5: Building Recommender Systems -- Creating a training pipeline -- Extracting the nearest neighbors -- Building a K-Nearest Neighbors classifier -- Computing similarity scores -- Finding similar users using collaborative filtering -- Building a movie recommendation system -- Summary -- Chapter 6: Logic Programming -- What is logic programming? -- Understanding the building blocks of logic programming -- Solving problems using logic programming -- Installing Python packages -- Matching mathematical expressions -- Validating primes -- Parsing a family tree -- Analyzing geography -- Building a puzzle solver -- Summary -- Chapter 7: Heuristic Search Techniques -- What is heuristic search? -- Uninformed versus Informed search -- Constraint Satisfaction Problems -- Local search techniques -- Simulated Annealing -- Constructing a string using greedy search -- Solving a problem with constraints -- Solving the region-coloring problem -- Building an 8-puzzle solver -- Building a maze solver -- Summary -- Chapter 8: Genetic Algorithms -- Understanding evolutionary and genetic algorithms -- Fundamental concepts in genetic algorithms -- Generating a bit pattern with predefined parameters -- Visualizing the evolution -- Solving the symbol regression problem -- Building an intelligent robot controller -- Summary -- Chapter 9: Building Games With Artificial Intelligence. , Using search algorithms in games -- Combinatorial search -- Minimax algorithm -- Alpha-Beta pruning -- Negamax algorithm -- Installing easyAI library -- Building a bot to play Last Coin Standing -- Building a bot to play Tic-Tac-Toe -- Building two bots to play Connect Four™ against each other -- Building two bots to play Hexapawn against each other -- Summary -- Chapter 10: Natural Language Processing -- Introduction and installation of packages -- Tokenizing text data -- Converting words to their base forms using stemming -- Converting words to their base forms using lemmatization -- Dividing text data into chunks -- Extracting the frequency of terms using a Bag of Words model -- Building a category predictor -- Constructing a gender identifier -- Building a sentiment analyzer -- Topic modeling using Latent Dirichlet Allocation -- Summary -- Chapter 11: Probabilistic Reasoning for Sequential Data -- Understanding sequential data -- Handling time-series data with Pandas -- Slicing time-series data -- Operating on time-series data -- Extracting statistics from time-series data -- Generating data using Hidden Markov Models -- Identifying alphabet sequences with Conditional Random Fields -- Stock market analysis -- Summary -- Chapter 12: Building A Speech Recognizer -- Working with speech signals -- Visualizing audio signals -- Transforming audio signals to the frequency domain -- Generating audio signals -- Synthesizing tones to generate music -- Extracting speech features -- Recognizing spoken words -- Summary -- Chapter 13: Object Detection and Tracking -- Installing OpenCV -- Frame differencing -- Tracking objects using colorspaces -- Object tracking using background subtraction -- Building an interactive object tracker using the CAMShift algorithm -- Optical flow based tracking -- Face detection and tracking. , Using Haar cascades for object detection -- Using integral images for feature extraction -- Eye detection and tracking -- Summary -- Chapter 14: Artificial Neural Networks -- Introduction to artificial neural networks -- Building a neural network -- Training a neural network -- Building a Perceptron based classifier -- Constructing a single layer neural network -- Constructing a multilayer neural network -- Building a vector quantizer -- Analyzing sequential data using recurrent neural networks -- Visualizing characters in an Optical Character Recognition database -- Building an Optical Character Recognition engine -- Summary -- Chapter 15: Reinforcement Learning -- Understanding the premise -- Reinforcement learning versus supervised learning -- Real world examples of reinforcement learning -- Building blocks of reinforcement learning -- Creating an environment -- Building a learning agent -- Summary -- Chapter 16: Deep Learning with Convolutional Neural Networks -- What are Convolutional Neural Networks? -- Architecture of CNNs -- Types of layers in a CNN -- Building a perceptron-based linear regressor -- Building an image classifier using a single layer neural network -- Building an image classifier using a Convolutional Neural Network -- Summary -- Index.

Additional Edition: ISBN 1-78646-439-X

Language: English

Keywords: Electronic books.

URL: http://portal.igpublish.com/iglibrary/search/PACKT0000393.html

URL: lizenzpflichtig

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UID:

Format: xi, 269 Seiten : , Illustrationen.

ISBN: 978-1-78528-094-8

Series Statement: Open source community experience distilled

Language: English

Subjects: Computer Science

UID:

Format: vi, vi, 551 Seiten : , Illustrationen.

ISBN: 978-1-78712-549-0

Series Statement: Learning path

Language: English

Subjects: Computer Science

Keywords: OpenCV ; Python

UID:

Format: 1 Online-Ressource (xxii, 603 Seiten) : , Illustrationen, Diagramme (teilweise farbig).

Edition: Second edition

ISBN: 978-1-78980-075-3

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-78980-845-2

Language: English

Subjects: Computer Science

Keywords: Maschinelles Lernen ; Python

URL: Volltext  (URL des Erstveröffentlichers)

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URL: https://learning.oreilly.com/library/view/-/9781789808452/?ar

UID:

Format: 1 online resource (619 pages)

Edition: Second edition.

ISBN: 1-83921-607-7

Content: New edition of the bestselling guide to artificial intelligence with Python, updated to Python 3.x, with seven new chapters that cover RNNs, AI and Big Data, fundamental use cases, chatbots, and more. Key Features Completely updated and revised to Python 3.x New chapters for AI on the cloud, recurrent neural networks, deep learning models, and feature selection and engineering Learn more about deep learning algorithms, machine learning data pipelines, and chatbots Book Description Artificial Intelligence with Python, Second Edition is an updated and expanded version of the bestselling guide to artificial intelligence using the latest version of Python 3.x. Not only does it provide you an introduction to artificial intelligence, this new edition goes further by giving you the tools you need to explore the amazing world of intelligent apps and create your own applications. This edition also includes seven new chapters on more advanced concepts of Artificial Intelligence, including fundamental use cases of AI; machine learning data pipelines; feature selection and feature engineering; AI on the cloud; the basics of chatbots; RNNs and DL models; and AI and Big Data. Finally, this new edition explores various real-world scenarios and teaches you how to apply relevant AI algorithms to a wide swath of problems, starting with the most basic AI concepts and progressively building from there to solve more difficult challenges so that by the end, you will have gained a solid understanding of, and when best to use, these many artificial intelligence techniques. What you will learn Understand what artificial intelligence, machine learning, and data science are Explore the most common artificial intelligence use cases Learn how to build a machine learning pipeline Assimilate the basics of feature selection and feature engineering Identify the differences between supervised and unsupervised learning Discover the most recent advances and tools offered for AI development in the cloud Develop automatic speech recognition systems and chatbots Apply AI algorithms to time series data Who this book is for The intended audience for this book is Python developers who want to build real-world Artificial Intelligence applications. Basic Python programming experience and awareness of machine learning concepts and techniques is mandatory.

Note: Previous edition published: 2017.

Additional Edition: ISBN 1-83921-953-X

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: 1 Online-Ressource v, 243 Seiten) : , Illustrationen, Diagramme (überwieged farbig).

Edition: Second edition

ISBN: 978-1-78839-676-9 , 1-78839-676-6

Note: Description based on online resource; title from title page (Safari, viewed February 21, 2018). - Previous edition published: 2015

Additional Edition: Erscheint auch als Druck-Ausgabe ISBN 978-1-78839-690-5

Language: English

Subjects: Computer Science

Keywords: Python ; OpenCV ; Electronic books.

URL: Volltext  (URL des Erstveröffentlichers)

URL: Click to View

UID:

Format: 1 online resource (296 p.)

Edition: 1st edition

ISBN: 1-78528-987-X

Series Statement: Community experience distilled

Content: Build real-world computer vision applications and develop cool demos using OpenCV for Python About This Book Learn how to apply complex visual effects to images using geometric transformations and image filters Extract features from an image and use them to develop advanced applications Build algorithms to help you understand the image content and perform visual searches Who This Book Is For This book is intended for Python developers who are new to OpenCV and want to develop computer vision applications with OpenCV-Python. This book is also useful for generic software developers who want to deploy computer vision applications on the cloud. It would be helpful to have some familiarity with basic mathematical concepts such as vectors, matrices, and so on. What You Will Learn Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image Detect and track various body parts such as the face, nose, eyes, ears, and mouth Stitch multiple images of a scene together to create a panoramic image Make an object disappear from an image Identify different shapes, segment an image, and track an object in a live video Recognize an object in an image and build a visual search engine Reconstruct a 3D map from images Build an augmented reality application In Detail Computer vision is found everywhere in modern technology. OpenCV for Python enables us to run computer vision algorithms in real time. With the advent of powerful machines, we are getting more processing power to work with. Using this technology, we can seamlessly integrate our computer vision applications into the cloud. Web developers can develop complex applications without having to reinvent the wheel. This book will walk you through all the building blocks needed to build amazing computer vision applications with ease. We start off with applying geometric transformations to images. We then discuss affine and projective transformations and see how we can use them to apply cool geometric effects to photos. We will then cover techniques used for object recognition, 3D reconstruction, stereo imaging, and other computer vision applications. This book will also provide clear examples written in Python to build OpenCV applications. The book starts off with simple beginner's level tasks such as basic processing and handling images, image mapping, and detecting images. It also covers popular OpenCV libraries with the help of examples. The book is a prac...

Note: Description based upon print version of record. , Cover; Copyright; Credits; About the Author; About the Reviewers; www.PacktPub.com; Table of Contents; Preface; Chapter 1: Applying Geometric Transformations to Images; Installing OpenCV-Python; Windows; Mac OS X; Linux (for Ubuntu); Reading, displaying, and saving images; What just happened?; Loading and saving an image; Image color spaces; Converting between color spaces; What just happened?; Image translation; What just happened?; Image rotation; What just happened?; Image scaling; What just happened?; Affine transformations; What just happened?; Projective transformations , What just happened?Image warping; Summary; Chapter 2: Detecting Edges And Applying Image Filters; 2D convolution; Blurring; Size of the kernel versus blurriness; Edge detection; Motion blur; Under the hood; Sharpening; Understanding the pattern; Embossing; Erosion and dilation; Afterthought; Creating a vignette filter; What's happening underneath?; How do we move the focus around?; Enhancing the contrast in an image; How do we handle color images?; Summary; Chapter 3: Cartoonizing an Image; Accessing the webcam; Under the hood; Keyboard inputs; Interacting with the application; Mouse inputs , What's happening underneath?Interacting with live video stream; How did we do it?; Cartoonizing an image; Deconstructing the code; Summary; Chapter 4: Detecting And Tracking Different Body Parts; Using Haar cascades to detect things; What are integral images?; Detecting and tracking faces; Understanding it better; Fun with faces; Under the hood; Detecting eyes; Afterthought; Fun with eyes; Positioning the sunglasses; Detecting ears; Detecting a mouth; It's time for a moustache; Detecting a nose; Detecting pupils; Deconstructing the code; Summary; Chapter 5: Extracting Features From an Image , Why do we care about keypoints?What are keypoints?; Detecting the corners; Good Features To Track; Scale Invariant Feature Transform (SIFT); Speeded Up Robust Features (SURF); Features from Accelerated Segment Test (FAST); Binary Robust Independent Elementary Features (BRIEF); Oriented FAST and Rotated BRIEF (ORB); Summary; Chapter 6: Creating a Panoramic Image; Matching keypoint descriptors; How did we match the keypoints?; Understanding the matcher object; Drawing the matching keypoints; Creating the panoramic image; Finding the overlapping regions; Stitching the images , What if the images are at an angle to each other?Why does it look stretched?; Summary; Chapter 7: Seam Carving; Why do we care about seam carving?; How does it work?; How do we define ""interesting""?; How do we compute the seams?; Can we expand an image?; Can we remove an object completely?; How did we do it?; Summary; Chapter 8: Detecting Shapes and Segmenting an Image; Contour analysis and shape matching; Approximating a contour; Identifying the pizza with the slice taken out; How to censor a shape?; What is image segmentation?; How does it work?; Watershed algorithm; Summary , Chapter 9: Object Tracking , English

Additional Edition: ISBN 1-78528-393-6

Language: English

Keywords: Electronic books.

URL: Click to View

UID:

Format: 1 online resource (941 pages) : , illustrations, graphs

Edition: 1st edition

ISBN: 1-78712-067-8

Content: Learn to solve challenging data science problems by building powerful machine learning models using Python About This Book Understand which algorithms to use in a given context with the help of this exciting recipe-based guide This practical tutorial tackles real-world computing problems through a rigorous and effective approach Build state-of-the-art models and develop personalized recommendations to perform machine learning at scale Who This Book Is For This Learning Path is for Python programmers who are looking to use machine learning algorithms to create real-world applications. It is ideal for Python professionals who want to work with large and complex datasets and Python developers and analysts or data scientists who are looking to add to their existing skills by accessing some of the most powerful recent trends in data science. Experience with Python, Jupyter Notebooks, and command-line execution together with a good level of mathematical knowledge to understand the concepts is expected. Machine learning basic knowledge is also expected. What You Will Learn Use predictive modeling and apply it to real-world problems Understand how to perform market segmentation using unsupervised learning Apply your new-found skills to solve real problems, through clearly-explained code for every technique and test Compete with top data scientists by gaining a practical and theoretical understanding of cutting-edge deep learning algorithms Increase predictive accuracy with deep learning and scalable data-handling techniques Work with modern state-of-the-art large-scale machine learning techniques Learn to use Python code to implement a range of machine learning algorithms and techniques In Detail Machine learning is increasingly spreading in the modern data-driven world. It is used extensively across many fields such as search engines, robotics, self-driving cars, and more. Machine learning is transforming the way we understand and interact with the world around us. In the first module, Python Machine Learning Cookbook, you will learn how to perform various machine learning tasks using a wide variety of machine learning algorithms to solve real-world problems and use Python to implement these algorithms. The second module, Advanced Machine Learning with Python, is designed to take you on a guided tour of the most relevant and powerful machine learning techniques and you'll acquire a broad set of powerful skills in the area of feature selection and fea...

Note: "A course in three modules"--Title page.

Additional Edition: ISBN 1-78712-321-9

Language: English

UID:

Format: xi, 271 Seiten , Illustrationen

ISBN: 9781785283932

Series Statement: Community experience distilled

Language: English

Subjects: Computer Science