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Pro Machine Learning Algorithms
Bridge the gap between a high-level understanding of how an algorithm works and knowing the nuts and bolts to tune your models better. This book will give you the confidence and skills when developing all the major machine learning models. In Pro Machine Learning Algorithms, you will first develop the algorithm in Excel so that you get a practical understanding of all the levers that can be tuned in a model, before implementing the models in Python/R.You will cover all the major algorithms: supervised and unsupervised learning, which include linear/logistic regression; k-means clustering; PCA; recommender system; decision tree; random forest; GBM; and neural networks. You will also be exposed to the latest in deep learning through CNNs, RNNs, and word2vec for text mining. You will be learning not only the algorithms, but also the concepts of feature engineering to maximize the performance of a model. You will see the theory along with case studies, such as sentiment classification, fraud detection, recommender systems, and image recognition, so that you get the best of both theory and practice for the vast majority of the machine learning algorithms used in industry. Along with learning the algorithms, you will also be exposed to running machine-learning models on all the major cloud service providers.You are expected to have minimal knowledge of statistics/software programming and by the end of this book you should be able to work on a machine learning project with confidence. What You Will LearnGet an in-depth understanding of all the major machine learning and deep learning algorithms Fully appreciate the pitfalls to avoid while building modelsImplement machine learning algorithms in the cloud Follow a hands-on approach through case studies for each algorithmGain the tricks of ensemble learning to build more accurate modelsDiscover the basics of programming in R/Python and the Keras framework for deep learningWho This Book Is ForBusiness analysts/ IT professionals who want to transition into data science roles. Data scientists who want to solidify their knowledge in machine learning.
R 1,736.45
Beyond Databases, Architectures and Structures. Facing the Challenges of Data Proliferation and Growing Variety
This book constitutes the refereed proceedings of the 14th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2018, held in Poznań, Poland, in September 2018, during the IFIP World Computer Congress.It consists of 38 carefully reviewed papers selected from 102 submissions. The papers are organized in topical sections, namely big data and cloud computing; architectures, structures and algorithms for efficient data processing; artificial intelligence, data mining and knowledge discovery; text mining, natural language processing, ontologies and semantic web; image analysis and multimedia mining.
R 2,210.03
Algorithms and Computation
This book constitutes the refereed proceedings of the 22nd International Symposium on Algorithms and Computation, ISAAC 2011, held in Yokohama, Japan in December 2011. The 76 revised full papers presented together with two invited talks were carefully reviewed and selected from 187 submissions for inclusion in the book. This volume contains topics such as approximation algorithms; computational geometry; computational biology; computational complexity; data structures; distributed systems; graph algorithms; graph drawing and information visualization; optimization; online and streaming algorithms; parallel and external memory algorithms; parameterized algorithms; game theory and internet algorithms; randomized algorithms; and string algorithms.
R 2,253.69
Applied Supervised Learning with Python
Explore the exciting world of machine learning with the fastest growing technology in the worldKey FeaturesUnderstand various machine learning concepts with real-world examplesImplement a supervised machine learning pipeline from data ingestion to validationGain insights into how you can use machine learning in everyday lifeBook DescriptionMachine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support.With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data.By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own!What you will learnUnderstand the concept of supervised learning and its applicationsImplement common supervised learning algorithms using machine learning Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine learning modelsWho this book is forApplied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
R 1,498.00
Bioinformatics Algorithms
Presents algorithmic techniques for solving problems in bioinformatics, including applications that shed new light on molecular biology This book introduces algorithmic techniques in bioinformatics, emphasizing their application to solving novel problems in post-genomic molecular biology. Beginning with a thought-provoking discussion on the role of algorithms in twenty-first-century bioinformatics education, Bioinformatics Algorithms covers: General algorithmic techniques, including dynamic programming, graph-theoretical methods, hidden Markov models, the fast Fourier transform, seeding, and approximation algorithms Algorithms and tools for genome and sequence analysis, including formal and approximate models for gene clusters, advanced algorithms for non-overlapping local alignments and genome tilings, multiplex PCR primer set selection, and sequence/network motif finding Microarray design and analysis, including algorithms for microarray physical design, missing value imputation, and meta-analysis of gene expression data Algorithmic issues arising in the analysis of genetic variation across human population, including computational inference of haplotypes from genotype data and disease association search in case/control epidemiologic studies Algorithmic approaches in structural and systems biology, including topological and structural classification in biochemistry, and prediction of protein-protein and domain-domain interactions Each chapter begins with a self-contained introduction to a computational problem; continues with a brief review of the existing literature on the subject and an in-depth description of recent algorithmic and methodological developments; and concludes with a brief experimental study and a discussion of open research challenges. This clear and approachable presentation makes the book appropriate for researchers, practitioners, and graduate students alike.
R 6,446.00
Hands-On Machine Learning with scikit-learn and Scientific Python Toolkits
Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problemsKey FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook DescriptionMachine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits.The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms.By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production.What you will learnUnderstand when to use supervised, unsupervised, or reinforcement learning algorithmsFind out how to collect and prepare your data for machine learning tasksTackle imbalanced data and optimize your algorithm for a bias or variance tradeoffApply supervised and unsupervised algorithms to overcome various machine learning challengesEmploy best practices for tuning your algorithm’s hyper parametersDiscover how to use neural networks for classification and regressionBuild, evaluate, and deploy your machine learning solutions to productionWho this book is forThis book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.
R 1,498.00
Neural Network Projects with Python
Build your Machine Learning portfolio by creating 6 cutting-edge Artificial Intelligence projects using neural networks in PythonKey FeaturesDiscover neural network architectures (like CNN and LSTM) that are driving recent advancements in AIBuild expert neural networks in Python using popular libraries such as KerasIncludes projects such as object detection, face identification, sentiment analysis, and moreBook DescriptionNeural networks are at the core of recent AI advances, providing some of the best resolutions to many real-world problems, including image recognition, medical diagnosis, text analysis, and more. This book goes through some basic neural network and deep learning concepts, as well as some popular libraries in Python for implementing them.It contains practical demonstrations of neural networks in domains such as fare prediction, image classification, sentiment analysis, and more. In each case, the book provides a problem statement, the specific neural network architecture required to tackle that problem, the reasoning behind the algorithm used, and the associated Python code to implement the solution from scratch. In the process, you will gain hands-on experience with using popular Python libraries such as Keras to build and train your own neural networks from scratch.By the end of this book, you will have mastered the different neural network architectures and created cutting-edge AI projects in Python that will immediately strengthen your machine learning portfolio.What you will learnLearn various neural network architectures and its advancements in AIMaster deep learning in Python by building and training neural networkMaster neural networks for regression and classificationDiscover convolutional neural networks for image recognitionLearn sentiment analysis on textual data using Long Short-Term MemoryBuild and train a highly accurate facial recognition security systemWho this book is forThis book is a perfect match for data scientists, machine learning engineers, and deep learning enthusiasts who wish to create practical neural network projects in Python. Readers should already have some basic knowledge of machine learning and neural networks.
R 1,498.00
Practical Natural Language Processing with Python
Work with natural language tools and techniques to solve real-world problems. This book focuses on how natural language processing (NLP) is used in various industries. Each chapter describes the problem and solution strategy, then provides an intuitive explanation of how different algorithms work and a deeper dive on code and output in Python. Practical Natural Language Processing with Python follows a case study-based approach. Each chapter is devoted to an industry or a use case, where you address the real business problems in that industry and the various ways to solve them. You start with various types of text data before focusing on the customer service industry, the type of data available in that domain, and the common NLP problems encountered. Here you cover the bag-of-words model supervised learning technique as you try to solve the case studies. Similar depth is given to other use cases such as online reviews, bots, finance, and so on. As you cover the problems in these industries you’ll also cover sentiment analysis, named entity recognition, word2vec, word similarities, topic modeling, deep learning, and sequence to sequence modelling. By the end of the book, you will be able to handle all types of NLP problems independently. You will also be able to think in different ways to solve language problems. Code and techniques for all the problems are provided in the book.What You Will LearnBuild an understanding of NLP problems in industryGain the know-how to solve a typical NLP problem using language-based models and machine learningDiscover the best methods to solve a business problem using NLP - the tried and tested onesUnderstand the business problems that are tough to solve Who This Book Is ForAnalytics and data science professionals who want to kick start NLP, and NLP professionals who want to get new ideas to solve the problems at hand.
R 1,254.00
Hands-On Meta Learning with Python
Explore a diverse set of meta-learning algorithms and techniques to enable human-like cognition for your machine learning models using various Python frameworksKey FeaturesUnderstand the foundations of meta learning algorithmsExplore practical examples to explore various one-shot learning algorithms with its applications in TensorFlowMaster state of the art meta learning algorithms like MAML, reptile, meta SGDBook DescriptionMeta learning is an exciting research trend in machine learning, which enables a model to understand the learning process. Unlike other ML paradigms, with meta learning you can learn from small datasets faster.Hands-On Meta Learning with Python starts by explaining the fundamentals of meta learning and helps you understand the concept of learning to learn. You will delve into various one-shot learning algorithms, like siamese, prototypical, relation and memory-augmented networks by implementing them in TensorFlow and Keras. As you make your way through the book, you will dive into state-of-the-art meta learning algorithms such as MAML, Reptile, and CAML. You will then explore how to learn quickly with Meta-SGD and discover how you can perform unsupervised learning using meta learning with CACTUs. In the concluding chapters, you will work through recent trends in meta learning such as adversarial meta learning, task agnostic meta learning, and meta imitation learning.By the end of this book, you will be familiar with state-of-the-art meta learning algorithms and able to enable human-like cognition for your machine learning models.What you will learnUnderstand the basics of meta learning methods, algorithms, and typesBuild voice and face recognition models using a siamese networkLearn the prototypical network along with its variantsBuild relation networks and matching networks from scratchImplement MAML and Reptile algorithms from scratch in PythonWork through imitation learning and adversarial meta learningExplore task agnostic meta learning and deep meta learningWho this book is forHands-On Meta Learning with Python is for machine learning enthusiasts, AI researchers, and data scientists who want to explore meta learning as an advanced approach for training machine learning models. Working knowledge of machine learning concepts and Python programming is necessary.
R 1,498.00
Modern Algorithms of Cluster Analysis
This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.
R 7,730.84
Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery
This book constitutes the refereed proceedings of the 12th International Conference entitled Beyond Databases, Architectures and Structures, BDAS 2016, held in Ustroń, Poland, in May/June 2016.It consists of 57 carefully reviewed papers selected from 152 submissions. The papers are organized in topical sections, namely artificial intelligence, data mining and knowledge discovery; architectures, structures and algorithms for efficient data processing; data warehousing and OLAP; natural language processing, ontologies and semantic Web; bioinformatics and biomedical data analysis; data processing tools; novel applications of database systems.
R 4,346.87
Combinatorial Algorithms
This book constitutes the refereed proceedings of the 33rd International Workshop on Combinatorial Algorithms, IWOCA 2022, which took place as a hybrid event in Trier, Germany, during June 7-9, 2022.The 35 papers presented in these proceedings were carefully reviewed and selected from 86 submissions. They deal with diverse topics related to combinatorial algorithms, such as algorithms and data structures; algorithmic and combinatorical aspects of cryptography and information security; algorithmic game theory and complexity of games; approximation algorithms; complexity theory; combinatorics and graph theory; combinatorial generation, enumeration and counting; combinatorial optimization; combinatorics of words; computational biology; computational geometry; decompositions and combinatorial designs; distributed and network algorithms; experimental combinatorics; fine-grained complexity; graph algorithms and modelling with graphs; graph drawing and graph labelling; network theory and temporal graphs; quantum computing and algorithms for quantum computers; online algorithms; parameterized and exact algorithms; probabilistic andrandomized algorithms; and streaming algorithms.
R 3,930.54
Computational Intelligence-based Optimization Algorithms
Computational intelligence-based optimization methods, also known as metaheuristic optimization algorithms, are a popular topic in mathematical programming.These methods have bridged the gap between various approaches and created a new school of thought to solve real-world optimization problems. In this book, we have selected some of the most effective and renowned algorithms in the literature. These algorithms are not only practical but also provide thought-provoking theoretical ideas to help readers understand how they solve optimization problems. Each chapter includes a brief review of the algorithm’s background and the fields it has been used in.Additionally, Python code is provided for all algorithms at the end of each chapter, making this book a valuable resource for beginner and intermediate programmers looking to understand these algorithms.
R 3,041.00
Algorithms for Graphics and Image Processing
The technological developments of the last ten years have made com puter graphics and image processing by computer popular. Pictorial pat tern recognition has also shown significant progress. Clearly, there exist overlapping interests among the three areas of research. Graphic displays are of concern to anyone involved in image processing or pic torial pattern recognition and many problems in graphics require methodologies from image processing for their solutions. The data structures used in all three areas are similar. It seems that there is a common body of knowledge underlying all three areas, pictorial informa tion processing by computer. The novelty of these fields makes it difficult to design a course or to a write a book covering their basic concepts. Some of the treatises on graphics focus on the hardware and methods of current interest while treatises on image processing often emphasize applications and classical signal processing. The fast evolution of technology causes such material to lose its relevance. For example, the development of optical fibers has reduced the importance of bandwidth compression.
R 3,064.65
WALCOM: Algorithms and Computation
WALCOM 2010, the 4th International Workshop on Algorithms and Com- tation, held during February 10-12, 2010 in Dhaka, Bangladesh, covered the areas of approximation algorithms, combinatorial algorithms, combinatorial - timization, computational Biology, computational geometry, data structures, graph algorithms, graph drawing, parallel and distributed algorithms, par- eterized complexity, network optimization, online algorithms, randomized - gorithms and string algorithms. The workshop was organized jointly by the Bangladesh Academy of Sciences (BAS) and Bangladesh University of En- neering and Technology (BUET), and the quality of the workshop was ensured by a ProgramCommittee comprising 25 researchersof internationalrepute from Australia,Bangladesh,Canada,France,Germany,Greece,HongKong,Hungary, India, Italy, Japan, Switzerland, Taiwan, UK and USA. Thisvolumecontains23contributedpapersandfourinvitedpaperspresented atWALCOM2010.TheCallforPapersreceivedanenthusiasticresponse,resu- ing in 60 submissions from 21 countries.The Program Committee thoroughly reviewed each of the 60 submissions and accepted 23 of them for presentation in the workshop after elaborate discussions on review reports. The image of the workshopwas highly enhanced by the four invited talks of eminent and we- knownresearchersTetsuoAsanoofJAIST,Japan,SubirKumarGhoshofTIFR, India, Giuseppe Liotta of University of Perugia, Italy and J' anos Pach of EPFL Lausanne, Switzerland and R' enyi Institute Budapest, Hungary.
R 2,015.00
Algorithms - ESA 2014
This book constitutes the refereed proceedings of the 22st Annual European Symposium on Algorithms, ESA 2014, held in Wrocław, Poland, in September 2014, as part of ALGO 2014. The 69 revised full papers presented were carefully reviewed and selected from 269 initial submissions: 57 out of 221 in Track A, Design and Analysis, and 12 out of 48 in Track B, Engineering and Applications. The papers present original research in the areas of design and mathematical analysis of algorithms; engineering, experimental analysis, and real-world applications of algorithms and data structures.
R 1,649.99
Hands-On Image Processing with Python
Explore the mathematical computations and algorithms for image processing using popular Python tools and frameworks.Key FeaturesPractical coverage of every image processing task with popular Python librariesIncludes topics such as pseudo-coloring, noise smoothing, computing image descriptorsCovers popular machine learning and deep learning techniques for complex image processing tasksBook DescriptionImage processing plays an important role in our daily lives with various applications such as in social media (face detection), medical imaging (X-ray, CT-scan), security (fingerprint recognition) to robotics & space. This book will touch the core of image processing, from concepts to code using Python.The book will start from the classical image processing techniques and explore the evolution of image processing algorithms up to the recent advances in image processing or computer vision with deep learning. We will learn how to use image processing libraries such as PIL, scikit-mage, and scipy ndimage in Python. This book will enable us to write code snippets in Python 3 and quickly implement complex image processing algorithms such as image enhancement, filtering, segmentation, object detection, and classification. We will be able to use machine learning models using the scikit-learn library and later explore deep CNN, such as VGG-19 with Keras, and we will also use an end-to-end deep learning model called YOLO for object detection. We will also cover a few advanced problems, such as image inpainting, gradient blending, variational denoising, seam carving, quilting, and morphing.By the end of this book, we will have learned to implement various algorithms for efficient image processing.What you will learnPerform basic data pre-processing tasks such as image denoising and spatial filtering in PythonImplement Fast Fourier Transform (FFT) and Frequency domain filters (e.g., Weiner) in PythonDo morphological image processing and segment images with different algorithmsLearn techniques to extract features from images and match imagesWrite Python code to implement supervised / unsupervised machine learning algorithms for image processingUse deep learning models for image classification, segmentation, object detection and style transferWho this book is forThis book is for Computer Vision Engineers, and machine learning developers who are good with Python programming and want to explore details and complexities of image processing. No prior knowledge of the image processing techniques is expected.
R 1,661.00
Machine Learning with Spark and Python
Machine Learning with Spark and Python Essential Techniques for Predictive Analytics, Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves with the addition of Spark—a ML framework from the Apache foundation. By implementing Spark, machine learning students can easily process much large data sets and call the spark algorithms using ordinary Python code. Machine Learning with Spark and Python focuses on two algorithm families (linear methods and ensemble methods) that effectively predict outcomes. This type of problem covers many use cases such as what ad to place on a web page, predicting prices in securities markets, or detecting credit card fraud. The focus on two families gives enough room for full descriptions of the mechanisms at work in the algorithms. Then the code examples serve to illustrate the workings of the machinery with specific hackable code.
R 1,140.00
Stability Analysis and Design of Structures
This advanced and graduate-level text and self-tutorial teaches readers to understand and to apply analytical design principles across the breadth of the engineering sciences. Emphasizing fundamentals, the book addresses the stability of key engineering elements such as rigid-body assemblage, beam-column, beam, rigid frame, thin plate, arch, ring, and shell. Each chapter contains numerous worked-out problems that clarify practical application and aid comprehension of the basics of stability theory, plus end-of-chapter review exercises. Others key features are the citing and comparison of different national building standards, use of non-dimensional parameters, and many tables with much practical data and simplified formula, that enable readers to use them in the design of structural components.First six chapters most suitable for undergraduate-level study and remaining chapters for graduate-level courses.
R 3,078.15
Applied Data Science in FinTech
This textbook offers a comprehensive introduction to data science and financial technology, with a focus on advanced tools, data modeling, and their applications in FinTech. Adopting an inquiry-based approach, it integrates detailed case studies, clear definitions of financial terms, and practical examples to guide readers through core concepts and methods. Step-by-step illustrations demonstrate how programs are developed, making the material accessible for students. Dedicated chapters explore cutting-edge applications such as AdviceTech, AgTech, PropTech, chatbots, and sentiment analytics. To support hands-on learning, the book also provides sample code and data sets, enabling readers to experiment, practice, and ultimately design their own programs. Designed for those with a basic foundation in programming, this book is an ideal companion for applying data science techniques to financial and technological contexts. It is particularly valuable for postgraduate and advanced students in FinTech, Business Analytics, and Data Science programs.
R 8,572.00
