Biznes IT

The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions

Konrad Banachewicz, Luca Massaron

More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they’ve come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist.In this book, you’ll get up close and personal with four extensive case studies based on past Kaggle competitions. You’ll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering.You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor.

The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition

David Ping

David Ping, Head of GenAI and ML Solution Architecture for global industries at AWS, provides expert insights and practical examples to help you become a proficient ML solutions architect, linking technical architecture to business-related skills.You'll learn about ML algorithms, cloud infrastructure, system design, MLOps , and how to apply ML to solve real-world business problems. David explains the generative AI project lifecycle and examines Retrieval Augmented Generation (RAG), an effective architecture pattern for generative AI applications. You’ll also learn about open-source technologies, such as Kubernetes/Kubeflow, for building a data science environment and ML pipelines before building an enterprise ML architecture using AWS. As well as ML risk management and the different stages of AI/ML adoption, the biggest new addition to the handbook is the deep exploration of generative AI.By the end of this book , you’ll have gained a comprehensive understanding of AI/ML across all key aspects, including business use cases, data science, real-world solution architecture, risk management, and governance. You’ll possess the skills to design and construct ML solutions that effectively cater to common use cases and follow established ML architecture patterns, enabling you to excel as a true professional in the field.

The Machine Learning Workshop. Get ready to develop your own high-performance machine learning algorithms with scikit-learn - Second Edition

Hyatt Saleh

Machine learning algorithms are an integral part of almost all modern applications. To make the learning process faster and more accurate, you need a tool flexible and powerful enough to help you build machine learning algorithms quickly and easily. With The Machine Learning Workshop, you'll master the scikit-learn library and become proficient in developing clever machine learning algorithms.The Machine Learning Workshop begins by demonstrating how unsupervised and supervised learning algorithms work by analyzing a real-world dataset of wholesale customers. Once you've got to grips with the basics, you'll develop an artificial neural network using scikit-learn and then improve its performance by fine-tuning hyperparameters. Towards the end of the workshop, you'll study the dataset of a bank's marketing activities and build machine learning models that can list clients who are likely to subscribe to a term deposit. You'll also learn how to compare these models and select the optimal one.By the end of The Machine Learning Workshop, you'll not only have learned the difference between supervised and unsupervised models and their applications in the real world, but you'll also have developed the skills required to get started with programming your very own machine learning algorithms.

The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide

Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar,...

Do you want to learn how to communicate with computer systems using Natural Language Processing (NLP) techniques, or make a machine understand human sentiments? Do you want to build applications like Siri, Alexa, or chatbots, even if you’ve never done it before?With The Natural Language Processing Workshop, you can expect to make consistent progress as a beginner, and get up to speed in an interactive way, with the help of hands-on activities and fun exercises.The book starts with an introduction to NLP. You’ll study different approaches to NLP tasks, and perform exercises in Python to understand the process of preparing datasets for NLP models. Next, you’ll use advanced NLP algorithms and visualization techniques to collect datasets from open websites, and to summarize and generate random text from a document. In the final chapters, you’ll use NLP to create a chatbot that detects positive or negative sentiment in text documents such as movie reviews.By the end of this book, you’ll be equipped with the essential NLP tools and techniques you need to solve common business problems that involve processing text.

The Power Platform Playbook for Digital Transformation. Implement strategy, automation, and AI for scalable digital transformation

Victor Dantas, Chris Huntingford

Written by Victor Dantas, a five-time Microsoft MVP, and Chris Huntingford, MVP, Microsoft Certified Trainer, and FastTrack Recognized Solution Architect, this book distills the collective wisdom of two global thought leaders at the forefront of technological innovation. The Power Platform Playbook for Digital Transformation is your guide to strategizing, implementing, and scaling digital transformation using Power Apps, Power Automate, Power BI, Power Pages, and Copilot Studio. It equips you with tools to streamline operations, enhance decision-making, and scale automation without deep coding expertise.Unlike technical manuals that focus only on implementation, this book takes a holistic approach, combining strategy and execution. You’ll learn how to build enterprise-grade applications, automate repetitive tasks, and integrate Power Platform with Microsoft 365, Dynamics 365, and third-party services. Real-world case studies, best practices, and step-by-step guidance equip you to implement governance, security, and compliance while ensuring long-term success.Whether you’re a business leader driving transformation, an IT professional managing enterprise automation, or a business analyst optimizing processes, this book provides a clear roadmap to leveraging Power Platform for scalable, AI-enhanced digital innovation.

The Statistics and Machine Learning with R Workshop. Unlock the power of efficient data science modeling with this hands-on guide

Liu Peng

The Statistics and Machine Learning with R Workshop is a comprehensive resource packed with insights into statistics and machine learning, along with a deep dive into R libraries. The learning experience is further enhanced by practical examples and hands-on exercises that provide explanations of key concepts.Starting with the fundamentals, you’ll explore the complete model development process, covering everything from data pre-processing to model development. In addition to machine learning, you’ll also delve into R's statistical capabilities, learning to manipulate various data types and tackle complex mathematical challenges from algebra and calculus to probability and Bayesian statistics. You’ll discover linear regression techniques and more advanced statistical methodologies to hone your skills and advance your career.By the end of this book, you'll have a robust foundational understanding of statistics and machine learning. You’ll also be proficient in using R's extensive libraries for tasks such as data processing and model training and be well-equipped to leverage the full potential of R in your future projects.

The Successful Software Manager. The definitive guide to growing from developer to manager

Herman Fung

The Successful Software Manager is a comprehensive and practical guide to managing software developers, software customers, and the process of deciding what software needs to be built. It explains in detail how to develop a management mindset, lead a high-performing developer team, and meet all the expectations of a good manager.The book will help you whether you’ve chosen to pursue a career in management or have been asked to act up as a manager. Whether you’re a Development Manager, Product Manager, Team Leader, Solution Architect, or IT Director, this is your indispensable guide to all aspects of running your team and working within an organization and dealing with colleagues, customers, potential customers, and technologists, to ensure you build the product your organization needs.This book is the must-have authoritative guide to managing projects, managing people, and preparing yourself to be an effective manager. The intuitive real-life examples will act as a desk companion for any day-to-day challenge, and beyond that, Herman will show you how to prepare for the next stages and how to achieve career success.

The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition

Blaine Bateman, Ashish Ranjan Jha, Benjamin Johnston,...

Would you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society.Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you’ll learn how supervised learning, when used with Python, can produce efficient predictive models.Starting with the fundamentals of supervised learning, you’ll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you’ll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you’ll grasp advanced ensemble techniques such as boosting and random forests. Finally, you’ll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks.By the end of this book, you’ll have the skills you need to work on your real-life supervised learning Python projects.