Search results for "introduction to python programming"
Items we need to order for you
R 428.00
R 305.00
OpenCV: Computer Vision Projects with Python
Get savvy with OpenCV and actualize cool computer vision applicationsAbout This Book* Use OpenCV's Python bindings to capture video, manipulate images, and track objects* Learn about the different functions of OpenCV and their actual implementations.* Develop a series of intermediate to advanced projects using OpenCV and PythonWho This Book Is ForThis learning path is for someone who has a working knowledge of Python and wants to try out OpenCV. This Learning Path will take you from a beginner to an expert in computer vision applications using OpenCV. OpenCV's application are humongous and this Learning Path is the best resource to get yourself acquainted thoroughly with OpenCV.What You Will Learn* Install OpenCV and related software such as Python, NumPy, SciPy, OpenNI, and SensorKinect - all on Windows, Mac or Ubuntu* Apply "curves" and other color transformations to simulate the look of old photos, movies, or video games* Apply geometric transformations to images, perform image filtering, and convert an image into a cartoon-like image* Recognize hand gestures in real time and perform hand-shape analysis based on the output of a Microsoft Kinect sensor* Reconstruct a 3D real-world scene from 2D camera motion and common camera reprojection techniques* Detect and recognize street signs using a cascade classifier and support vector machines (SVMs)* Identify emotional expressions in human faces using convolutional neural networks (CNNs) and SVMs* Strengthen your OpenCV2 skills and learn how to use new OpenCV3 featuresIn DetailOpenCV is a state-of-art computer vision library that allows a great variety of image and video processing operations. OpenCV for Python enables us to run computer vision algorithms in real time. This learning path proposes to teach the following topics. First, we will learn how to get started with OpenCV and OpenCV3's Python API, and develop a computer vision application that tracks body parts. Then, we will build amazing intermediate-level computer vision applications such as making an object disappear from an image, identifying different shapes, reconstructing a 3D map from images , and building an augmented reality application, Finally, we'll move to more advanced projects such as hand gesture recognition, tracking visually salient objects, as well as recognizing traffic signs and emotions on faces using support vector machines and multi-layer perceptrons respectively. This Learning Path combines some of the best that Packt has to offer in one complete, curated package. It includes content from the following Packt products:* OpenCV Computer Vision with Python by Joseph Howse * OpenCV with Python By Example by Prateek Joshi* OpenCV with Python Blueprints by Michael BeyelerStyle and approachThis course aims to create a smooth learning path that will teach you how to get started with will learn how to get started with OpenCV and OpenCV 3's Python API, and develop superb computer vision applications. Through this
R 2,605.00
Hands-On Introduction to Machine Learning
Packed with real-world examples, industry insights and practical activities, this textbook is designed to teach machine learning in a way that is easy to understand and apply. It assumes only a basic knowledge of technology, making it an ideal resource for students and professionals, including those who are new to computer science. All the necessary topics are covered, including supervised and unsupervised learning, neural networks, reinforcement learning, cloud-based services, and the ethical issues still posing problems within the industry. While Python is used as the primary language, many exercises will also have the solutions provided in R for greater versatility. A suite of online resources is available to support teaching across a range of different courses, including example syllabi, a solutions manual, and lecture slides. Datasets and code are also available online for students, giving them everything they need to practice the examples and problems in the book.
R 2,591.00
Introduction to Machine Learning
Aimed at undergraduate students, this text guides readers through the methods and principles of machine learning in an approachable manner without sacrificing mathematical precision or notation. Step-by-step explanations allow students to grasp complicated mathematical calculations and translate the theory and mathematics into programming and applications. The text presents machine learning concepts visually, and uses example datasets from fictional hippopotamuses and illustrations to explain the material in a unique, but easily understood and engaging way. Worked examples connect the mathematics and algorithms to real-world applications and enable students to utilize this technology in new and ever-changing circumstances. Topics covered include Bayesian reasoning, linear regression and classification, margin-based classification, cross-validation, neural networks, decision trees, clustering and dimensionality reduction. End-of-chapter mathematical exercises and additional coding projects reinforce application and decision-making skills.
R 3,829.00
Introduction to Machine Learning
Aimed at undergraduate students, this text guides readers through the methods and principles of machine learning in an approachable manner without sacrificing mathematical precision or notation. Step-by-step explanations allow students to grasp complicated mathematical calculations and translate the theory and mathematics into programming and applications. The text presents machine learning concepts visually, and uses example datasets from fictional hippopotamuses and illustrations to explain the material in a unique, but easily understood and engaging way. Worked examples connect the mathematics and algorithms to real-world applications and enable students to utilize this technology in new and ever-changing circumstances. Topics covered include Bayesian reasoning, linear regression and classification, margin-based classification, cross-validation, neural networks, decision trees, clustering and dimensionality reduction. End-of-chapter mathematical exercises and additional coding projects reinforce application and decision-making skills.
R 1,547.00
R 226.00
Practical Introduction to Computer Vision with OpenCV
Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV libraries Computer Vision is a rapidly expanding area and it is becoming progressively easier for developers to make use of this field due to the ready availability of high quality libraries (such as OpenCV 2). This text is intended to facilitate the practical use of computer vision with the goal being to bridge the gap between the theory and the practical implementation of computer vision. The book will explain how to use the relevant OpenCV library routines and will be accompanied by a full working program including the code snippets from the text. This textbook is a heavily illustrated, practical introduction to an exciting field, the applications of which are becoming almost ubiquitous. We are now surrounded by cameras, for example cameras on computers & tablets/ cameras built into our mobile phones/ cameras in games consoles; cameras imaging difficult modalities (such as ultrasound, X-ray, MRI) in hospitals, and surveillance cameras. This book is concerned with helping the next generation of computer developers to make use of all these images in order to develop systems which are more intuitive and interact with us in more intelligent ways. Explains the theory behind basic computer vision and provides a bridge from the theory to practical implementation using the industry standard OpenCV librariesOffers an introduction to computer vision, with enough theory to make clear how the various algorithms work but with an emphasis on practical programming issuesProvides enough material for a one semester course in computer vision at senior undergraduate and Masters levels Includes the basics of cameras and images and image processing to remove noise, before moving on to topics such as image histogramming; binary imaging; video processing to detect and model moving objects; geometric operations & camera models; edge detection; features detection; recognition in imagesContains a large number of vision application problems to provide students with the opportunity to solve real problems. Images or videos for these problems are provided in the resources associated with this book which include an enhanced eBook
R 2,235.00
