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Large Language Models Projects
This book offers you a hands-on experience using models from OpenAI and the Hugging Face library. You will use various tools and work on small projects, gradually applying the new knowledge you gain. The book is divided into three parts. Part one covers techniques and libraries. Here, you'll explore different techniques through small examples, preparing to build projects in the next section. You'll learn to use common libraries in the world of Large Language Models. Topics and technologies covered include chatbots, code generation, OpenAI API, Hugging Face, vector databases, LangChain, fine tuning, PEFT fine tuning, soft prompt tuning, LoRA, QLoRA, evaluating models, and Direct Preference Optimization. Part two focuses on projects. You'll create projects, understanding design decisions. Each project may have more than one possible implementation, as there is often not just one good solution. You'll also explore LLMOps-related topics. Part three delves into enterprise solutions. Large Language Models are not a standalone solution; in large corporate environments, they are one piece of the puzzle. You'll explore how to structure solutions capable of transforming organizations with thousands of employees, highlighting the main role that Large Language Models play in these new solutions. This book equips you to confidently navigate and implement Large Language Models, empowering you to tackle diverse challenges in the evolving landscape of language processing. What You Will Learn Gain practical experience by working with models from OpenAI and the Hugging Face libraryUse essential libraries relevant to Large Language Models, covering topics such as Chatbots, Code Generation, OpenAI API, Hugging Face, and Vector databasesCreate and implement projects using LLM while understanding the design decisions involvedUnderstand the role of Large Language Models in larger corporate settings Who This Book Is For Data analysts, data science, Python developers, and software professionals interested in learning the foundations of NLP, LLMs, and the processes of building modern LLM applications for various tasks
R 1,482.00
Python for Experimental Psychologists
Python for Experimental Psychologists equips researchers who have no prior programming experience with the essential knowledge to independently script experiments and analyses in the programming language Python. The book offers an excellent introduction, whether you are an undergraduate, a PhD candidate, or an established researcher.This updated edition is on Python 3 (the most current version). It starts by teaching the fundamentals of programming in Python, and then offers several chapters on scripting experiments (displaying stimuli, obtaining and logging user input, precision timing, etc.) using the popular PsychoPy package. The remainder of the book is dedicated to data analysis, and includes chapters on reading/writing to text files, time series, eye tracking, data visualisation, and statistics.Access to a companion website enriches the learning experience with colour figures, example stimuli, datasets, scripts, and a portable Windows installation of Python. The book assumes no prior knowledge, and its informal and accessible tone helps readers with backgrounds in experimental psychology and cognitive neuroscience to quickly understand Python. It serves as a useful resource not only for researchers in these fields, but also for lecturers instructing on methodology and data analysis.Python for Experimental Psychologists demystifies programming complexities, and empowers researchers to proficiently conduct experiments and to analyse their results.
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R 611.00
Numerical Python
Learn how to leverage the scientific computing and data analysis capabilities of Python, its standard library, and popular open-source numerical Python packages like NumPy, SymPy, SciPy, matplotlib, and more. This book demonstrates how to work with mathematical modeling and solve problems with numerical, symbolic, and visualization techniques. It explores applications in science, engineering, data analytics, and more.Numerical Python, Third Edition, presents many case study examples of applications in fundamental scientific computing disciplines, as well as in data science and statistics. This fully revised edition, updated for each library's latest version, demonstrates Python's power for rapid development and exploratory computing due to its simple and high-level syntax and many powerful libraries and tools for computation and data analysis. After reading this book, readers will be familiar with many computing techniques, including array-based and symbolic computing, visualization and numerical file I/O, equation solving, optimization, interpolation and integration, and domain-specific computational problems, such as differential equation solving, data analysis, statistical modeling, and machine learning.What You'll LearnWork with vectors and matrices using NumPyReview Symbolic computing with SymPyPlot and visualize data with MatplotlibPerform data analysis tasks with Pandas and SciPyUnderstand statistical modeling and machine learning with statsmodels and scikit-learnOptimize Python code using Numba and CythonWho This Book Is ForDevelopers who want to understand how to use Python and its ecosystem of libraries for scientific computing and data analysis.
R 798.00
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R 763.00
Python for Scientists
The third edition of this practical introduction to Python has been thoroughly updated, with all code migrated to Jupyter notebooks. The notebooks are available online with executable versions of all of the book's content (and more). The text starts with a detailed introduction to the basics of the Python language, without assuming any prior knowledge. Building upon each other, the most important Python packages for numerical math (NumPy), symbolic math (SymPy), and plotting (Matplotlib) are introduced, with brand new chapters covering numerical methods (SciPy) and data handling (Pandas). Further new material includes guidelines for writing efficient Python code and publishing code for other users. Simple and concise code examples, revised for compatibility with Python 3, guide the reader and support the learning process throughout the book. Readers from all of the quantitative sciences, whatever their background, will be able to quickly acquire the skills needed for using Python effectively.
R 1,454.00
Deep Learning with Python
Discover the practical aspects of implementing deep-learning solutions using the rich Python ecosystem. This book bridges the gap between the academic state-of-the-art and the industry state-of-the-practice by introducing you to deep learning frameworks such as Keras, Theano, and Caffe. The practicalities of these frameworks is often acquired by practitioners by reading source code, manuals, and posting questions on community forums, which tends to be a slow and a painful process. Deep Learning with Python allows you to ramp up to such practical know-how in a short period of time and focus more on the domain, models, and algorithms.This book briefly covers the mathematical prerequisites and fundamentals of deep learning, making this book a good starting point for software developers who want to get started in deep learning. A brief survey of deep learning architectures is also included.Deep Learning with Python also introduces you to key concepts of automatic differentiation and GPU computation which, while not central to deep learning, are critical when it comes to conducting large scale experiments. What You Will Learn Leverage deep learning frameworks in Python namely, Keras, Theano, and Caffe Gain the fundamentals of deep learning with mathematical prerequisites Discover the practical considerations of large scale experiments Take deep learning models to productionWho This Book Is ForSoftware developers who want to try out deep learning as a practical solution to a particular problem. Software developers in a data science team who want to take deep learning models developed by data scientists to production.
R 1,965.56
R 415.00
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Introduction to Python
Introduction to Python: with Applications in Optimization, Image and Video Processing, and Machine Learning is intended primarily for advanced undergraduate and graduate students in quantitative sciences such as mathematics, computer science, and engineering. In addition to this, the book is written in such a way that it can also serve as a self-contained handbook for professionals working in quantitative fields including finance, IT, and many other industries where programming is a useful or essential tool.The book is written to be accessible and useful to those with no prior experience of Python, but those who are somewhat more adept will also benefit from the more advanced material that comes later in the book.FeaturesCovers introductory and advanced material. Advanced material includes lists, dictionaries, tuples, arrays, plotting using Matplotlib, object-oriented programmingSuitable as a textbook for advanced undergraduates or postgraduates, or as a reference for researchers and professionalsSolutions manual, code, and additional examples are available for download
R 12,124.40
R 730.00
Python in Excel
Seasoned Experts Liam Bastick and Kathryn Newitt unveil a groundbreaking guide to mastering data analysis and automation with Python in Excel Python in Excel: Unlocking powerful data analysis and automation solutions is a hands-on, practical guide that leverages the authors’ decades of expertise to teach readers how to harness the newly integrated Python capabilities within Microsoft Excel, mapping familiar Excel concepts to Python foundations. Covering essential topics such as Python syntax, data structures, data analysis with pandas, visualization, and the use of AI tools like Copilot, the book empowers professionals to elevate their data analysis and automation skills. Readers will gain the ability to perform advanced forecasting, build predictive models, and streamline workflows, making them more efficient and competitive in today’s data-driven workplace. By reading this book, you will: Understand the fundamentals of Python programming directly within ExcelMap your existing Excel skills to Python concepts for easier learningAutomate repetitive tasks and workflows Analyze and visualize data with powerfulPython libraries like pandas Build advanced forecasting and predictive models without leaving ExcelExplore and manipulate data efficiently using Python’s concise syntaxIntegrate AI tools such as Copilot to assist with coding and analysisConnect Excel with external data sources using Python and Power QueryDevelop custom solutions for business modeling and data scienceCreate a roadmap for further study and mastery of Python in Excel
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R 639.00
Python for Accounting and Finance
Python for Accounting and Finance: A Mind-Mapping Approach is an innovative textbook written for accounting and finance students and professionals with no prior coding experience. It introduces Python programming through a mind mapping approach that presents complex concepts in a clear and structured way to support understanding and retention.The book is organised into four parts: Python Basics, Data Analysis and Visualisation, Automation, and Machine Learning. It places programming within practical accounting and finance contexts so that learners can see the direct application of coding in their field. The textbook uses mind maps as a core instructional method. These visual diagrams show programming concepts and their connections, helping learners to understand, organise and recall information, particularly those who learn through visual representation.Enhanced with Google Colab notebooks, this book creates a highly supportive learning experience, equipping students of accounting and finance with essential programming skills for their speciality.
R 8,572.00
Learning Professional Python
Volume 1 of Learning Professional Python is a resource for students who want to learn Python even if they don’t have any programming knowledge and for teachers who want a comprehensive introduction to Python to use with their students. This book helps the students achieve their dream job in the IT Industry and teaches the students in an easy, understandable manner while strengthening coding skills.Learning Professional Python Objectives, Volume 1:Become familiar with the features of Python programming languageIntroduce the object-oriented programming conceptsDiscover how to write Python code by following the object-oriented programming concepts
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R 834.00
R 165.00
Introduction to Python for Science and Engineering
Introduction to Python for Science and Engineering offers a quick and incisive introduction to the Python programming language for use in any science or engineering discipline. The approach is pedagogical and “bottom up,” which means starting with examples and extracting more general principles from that experience. No prior programming experience is assumed.Readers will learn the basics of Python syntax, data structures, input and output, conditionals and loops, user-defined functions, plotting, animation, and visualization. They will also learn how to use Python for numerical analysis, including curve fitting, random numbers, linear algebra, solutions to nonlinear equations, numerical integration, solutions to differential equations, and fast Fourier transforms.Readers learn how to interact and program with Python using JupyterLab and Spyder, two simple and widely used integrated development environments.All the major Python libraries for science and engineering are covered, including NumPy, SciPy, Matplotlib, and Pandas. Other packages are also introduced, including Numba, which can render Python numerical calculations as fast as compiled computer languages such as C but without their complex overhead.
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NLP at Work
'This book may help you to understand life more clearly' Paul Smith, fashion designerWelcome to THE book on NLP. The essence of NLP is the ability to study and reproduce excellence in yourself and to support others to do the same. NLP AT WORK helps you do that by developing an attitude of curiosity, naivety and learning - and giving you the ability to improvise with skill in real-time. Neuro Linguistic Programming is how you make sense of your world and, most importantly, how you make it what you want it to be: * Neuro: the way you filter and process your experience through your senses. * Linguistic: the way you interpret your experience through language. * Programming: the way you make sense of your experience to create your personal programme. NLP AT WORK is one of the most popular books ever published on the practical skills of NLP and how it can be applied in business. It transformed NLP from a peripheral art into an accessible, practical concept with relevant applications in the areas of influence, communication, negotiation, teamwork and coaching. This major new edition has been revised throughout and expanded to include a new section on coaching with NLP, showing how this approach is so different to traditional methods, and a new chapter on Metamessages. Clear, readable and jargon free, this book will help you get to the essence of what makes you and your business excellent and unique.
R 773.00
R 764.00
R 428.00
Practical Python
The author focuses on mainstream, useful and usable instruction in a popular, open source scripting language.
R 1,140.00
R 489.00
Introduction to Python for Engineers and Scientists
Familiarize yourself with the basics of Python for engineering and scientific computations using this concise, practical tutorial that is focused on writing code to learn concepts. Introduction to Python is useful for industry engineers, researchers, and students who are looking for open-source solutions for numerical computation. In this book you will learn by doing, avoiding technical jargon, which makes the concepts easy to learn. First you’ll see how to run basic calculations, absorbing technical complexities incrementally as you progress toward advanced topics. Throughout, the language is kept simple to ensure that readers at all levels can grasp the concepts. What You'll Learn Understand the fundamentals of the Python programming languageApply Python to numerical computational programming projects in engineering and scienceDiscover the Pythonic way of lifeApply data types, operators, and arrays Carry out plotting for visualizationWork with functions and loops Who This Book Is For Engineers, scientists, researchers, and students who are new to Python. Some prior programming experience would be helpful but not required.
R 1,254.00
Generative AI Apps with LangChain and Python
Future-proof your programming career through practical projects designed to grasp the intricacies of LangChain’s components, from core chains to advanced conversational agents. This hands-on book provides Python developers with the necessary skills to develop real-world Large Language Model (LLM)-based Generative AI applications quickly, regardless of their experience level.Projects throughout the book offer practical LLM solutions for common business issues, such as information overload, internal knowledge access, and enhanced customer communication. Meanwhile, you’ll learn how to optimize workflows, enhance embedding efficiency, select between vector stores, and other optimizations relevant to experienced AI users. The emphasis on real-world applications and practical examples will enable you to customize your own projects to address pain points across various industries.Developing LangChain-based Generative AI LLM Apps with Python employs a focused toolkit (LangChain, Pinecone, and Streamlit LLM integration) to practically showcase how Python developers can leverage existing skills to build Generative AI solutions. By addressing tangible challenges, you’ll learn-by-be doing, enhancing your career possibilities in today’s rapidly evolving landscape.What You Will Learn Understand different types of LLMs and how to select the right ones for responsible AI.Structure effective prompts.Master LangChain concepts, such as chains, models, memory, and agents.Apply embeddings effectively for search, content comparison, and understanding similarity.Setup and integrate Pinecone vector database for indexing, structuring data, and search.Build Q & A applications for multiple doc formats.Develop multi-step AI workflow apps using LangChain agents.Who This Book Is ForPython programmers who aim to develop a basic understanding of AI concepts and move from LLM theory to practical Generative AI application development using LangChain; those seeking a structured guide to enhance their careers by learning to create robust, real-world LLM-powered Generative AI applications; data scientists, analysts, and experienced developers new to LLMs.
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R 916.00
Python Programming for Data Analysis
This textbook grew out of notes for the ECE143 Programming for Data Analysis class that the author has been teaching at University of California, San Diego, which is a requirement for both graduate and undergraduate degrees in Machine Learning and Data Science. This book is ideal for readers with some Python programming experience. The book covers key language concepts that must be understood to program effectively, especially for data analysis applications. Certain low-level language features are discussed in detail, especially Python memory management and data structures. Using Python effectively means taking advantage of its vast ecosystem. The book discusses Python package management and how to use third-party modules as well as how to structure your own Python modules. The section on object-oriented programming explains features of the language that facilitate common programming patterns.After developing the key Python language features, the book moves on to third-party modules that are foundational for effective data analysis, starting with Numpy. The book develops key Numpy concepts and discusses internal Numpy array data structures and memory usage. Then, the author moves onto Pandas and details its many features for data processing and alignment. Because strong visualizations are important for communicating data analysis, key modules such as Matplotlib are developed in detail, along with web-based options such as Bokeh, Holoviews, Altair, and Plotly.The text is sprinkled with many tricks-of-the-trade that help avoid common pitfalls. The author explains the internal logic embodied in the Python language so that readers can get into the Python mindset and make better design choices in their codes, which is especially helpful for newcomers to both Python and data analysis. To get the most out of this book, open a Python interpreter and type along with the many code samples.
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Applied Reinforcement Learning with Python
Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions.What You'll LearnImplement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and KerasDeploy and train reinforcement learning–based solutions via cloud resourcesApply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.
R 1,079.73
R 353.00
NLP for Business Analysts
Method and process are important in business analysis, but knowing how to use them is even more so. Business analysts can increase effectiveness most by developing soft skills. You will find that these skills are transferable across project types and industry sectors. This book illustrates the application of NLP to develop competencies - better equipping you to communicate across cultures, reframe problems, manage stakeholder groups, resolve conflicts, motivate teams and become an even better leader.
R 1,864.58
R 610.00
Python Quick Syntax Reference
The Python Quick Syntax Reference is the "go to" book that contains an easy to read and useguide to Python programming and development. This condensed code and syntaxreference presents the Python language in a well-organized format designed tobe used time and again.You won't find jargon, bloated samples, case studies, or history of Hello Worldand computer theory in this handy reference. This Python syntax reference ispacked with useful information and is a must-have for any Python developer.
R 1,262.73
R 740.48
Python for AI, Machine Learning, and Deep Learning
Learn AI, machine learning, and deep learning with Python from the ground up, then build and tune models with XGBoost, TensorFlow, PyTorch, CNNs, RNNs, transformers, and transfer learning through practical Python projects.Key FeaturesBuild Python, data science, mathematics, and statistics foundations for machine learningTrain, evaluate, and tune regression, classification, ensemble, XGBoost, LightGBM, and CatBoost modelsCreate CNN, RNN, transformer, and transfer learning projects with TensorFlow, Keras, and PyTorchBook DescriptionLearning modern AI often feels fragmented: Python in one resource, mathematics in another, and deep learning or transformers somewhere else. This book brings those foundations into one practical progression, helping you connect the concepts behind machine learning with the code used to build models.You will start with Python programming, NumPy, Pandas, data visualization, exploratory data analysis, linear algebra, calculus, probability, and statistics. From there, you will build supervised learning skills across regression, classification, k-NN, feature engineering, model evaluation, cross-validation, and hyperparameter tuning. You will then work with ensemble methods including random forests, gradient boosting, XGBoost, LightGBM, and CatBoost, while learning to handle imbalanced data and justify modeling decisions.The final part moves into neural networks and deep learning with TensorFlow, Keras, and PyTorch. You will build CNNs for image classification, explore RNNs, LSTMs, GRUs, word embeddings, and sequence-to-sequence models, and understand attention and transformer architectures through BERT and GPT examples. You will finish by applying transfer learning and fine-tuning to computer vision and NLP tasks, supported by projects that turn theory into practical AI and machine learning skills for real-world problem solving.What you will learnUse Python, NumPy, Pandas, Matplotlib, and Seaborn for data analysisApply linear algebra, calculus, probability, and statistics to MLBuild regression, classification, and k-NN modelsEngineer features and evaluate models with cross-validation and hyperparameter tuningTrain ensembles with random forests, XGBoost, LightGBM, and CatBoostBuild neural networks and CNNs with TensorFlow, Keras, and PyTorchDevelop RNNs, LSTMs, GRUs, attention, BERT, and GPT workflows for NLPApply transfer learning and fine-tuning to computer vision and NLPWho this book is forThis book is for aspiring data scientists, machine learning engineers, AI developers, students, and career changers who want one structured path from Python fundamentals to machine learning and deep learning. It suits readers seeking practical skills in data analysis, predictive modeling, computer vision, NLP, transformers, and transfer learning. No prior machine learning or deep learning experience is required. Python is introduced from the fundamentals, although basic programming familiarity and foundational mathematics will help.
R 1,628.00
Object-oriented Python
Object-Oriented Python is an intuitive and thorough guide to mastering object-oriented programming from the ground up. You'll cover the basics of building classes and creating objects, and put theory into practice using the pygame package with clear examples that help visualize the object-oriented style. You'll explore the key concepts of object-oriented programming - encapsulation, polymorphism, and inheritance - and learn not just how to code with objects, but the absolute best practices for doing so. Finally, you'll bring it all together by building a complex video game, complete with full animations and sounds. The book covers two fully functional Python code packages that will speed up development of graphical user interface (GUI) programs in Python.
R 1,391.00
