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Hands-On AI Development with Python
Build real-world AI projects with Python, pandas, NumPy, Matplotlib, Seaborn, scikit-learn, NLP, neural networks, sentiment analysis, and web deployment as you move from first code to portfolio-ready AI apps and skills.Key FeaturesBuild portfolio-ready AI projects with Python, data analysis, ML, NLP, and deploymentUse pandas, NumPy, Matplotlib, Seaborn, scikit-learn, and neural networks on real datasetsMove from zero programming to practical AI workflows through guided, hands-on projectsBook DescriptionMany beginners learn Python syntax or AI theory but struggle to build projects they can explain, demonstrate, and add to a portfolio. This book closes that gap by turning AI fundamentals into practical Python projects that move from first code to working AI deployment.You will begin with Python setup and the foundations needed for AI development, including variables, data types, functions, control flow, and libraries. You will then use NumPy and pandas to load, clean, transform, and inspect datasets, before applying EDA with Matplotlib and Seaborn to uncover patterns, relationships, and missing values. With these foundations in place, you will build machine learning models using scikit-learn. You will work through prediction and classification workflows, prepare features, train models, evaluate results, and understand how choices affect accuracy and usefulness. The book introduces neural networks in a beginner-friendly way, showing how layers, training, and performance connect in applied AI work.You will create an NLP sentiment analysis project, turning text into features and classifying opinions. Finally, you will package a trained model as a web service used beyond a notebook. By the end, you will have a practical AI portfolio and a strong foundation for machine learning, data science, and applied AI development.What you will learnSet up Python for hands-on AI and machine learning projectsUse core syntax, data types, control flow, functions, and libraries for AI workClean, transform, analyze, and visualize data with NumPy, pandas, Matplotlib, and SeabornApply EDA to find patterns, missing values, relationships, and featuresBuild prediction and classification models using scikit-learn workflowsUnderstand neural network basics and practical deep learning conceptsCreate an NLP sentiment analysis model from text dataDeploy a trained AI model as a web serviceWho this book is forThis book is for absolute beginners, students, career changers, junior developers, analysts, data enthusiasts, and hobbyists who want a practical entry point into AI development. It is useful for readers building their first AI portfolio, exploring machine learning or data science roles, or learning how trained models become simple applications. No prior programming or AI experience is required.
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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.
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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.
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Programmieren mit Python
Diese einmalige und in sich geschlossene Einführung in die Programmierung verwendet Python um in die wichtigsten Paradigmen der Programmierung einzuführen: die Prozedurale Programmierung, dieObjektorientierte Programmierung und die Funktionale Programmierung und schließlich die Programmierung mit Numpy. Das Buch eignet sich für eine 5 – 7 ECTS Einführungsvorlesung und enthält eine Vielzahl von Aufgaben, die auch von Studierenden im Selbststudium gut zum Üben verwendet werden können.
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Handbook of NLP with Gensim
Elevate your natural language processing skills with Gensim and become proficient in handling a wide range of NLP tasks and projectsKey FeaturesAdvance your NLP skills with this comprehensive guide covering detailed explanations and code practicesBuild real-world topical modeling pipelines and fine-tune hyperparameters to deliver optimal resultsAdhere to the real-world industrial applications of topic modeling in medical, legal, and other fieldsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionNavigating the terrain of NLP research and applying it practically can be a formidable task made easy with The Handbook of NLP with Gensim. This book demystifies NLP and equips you with hands-on strategies spanning healthcare, e-commerce, finance, and more to enable you to leverage Gensim in real-world scenarios.You’ll begin by exploring motives and techniques for extracting text information like bag-of-words, TF-IDF, and word embeddings. This book will then guide you on topic modeling using methods such as Latent Semantic Analysis (LSA) for dimensionality reduction and discovering latent semantic relationships in text data, Latent Dirichlet Allocation (LDA) for probabilistic topic modeling, and Ensemble LDA to enhance topic modeling stability and accuracy.Next, you’ll learn text summarization techniques with Word2Vec and Doc2Vec to build the modeling pipeline and optimize models using hyperparameters. As you get acquainted with practical applications in various industries, this book will inspire you to design innovative projects. Alongside topic modeling, you’ll also explore named entity handling and NER tools, modeling procedures, and tools for effective topic modeling applications.By the end of this book, you’ll have mastered the techniques essential to create applications with Gensim and integrate NLP into your business processes.What you will learnConvert text into numerical values such as bag-of-word, TF-IDF, and word embeddingUse various NLP techniques with Gensim, including Word2Vec, Doc2Vec, LSA, FastText, LDA, and Ensemble LDABuild topical modeling pipelines and visualize the results of topic modelsImplement text summarization for legal, clinical, or other documentsApply core NLP techniques in healthcare, finance, and e-commerceCreate efficient chatbots by harnessing Gensim's NLP capabilitiesWho this book is forThis book is for data scientists and professionals who want to become proficient in topic modeling with Gensim. NLP practitioners can use this book as a code reference, while students or those considering a career transition will find this a valuable resource for advancing in the field of NLP. This book contains real-world applications for biomedical, healthcare, legal, and operations, making it a helpful guide for project managers designing their own topic modeling applications.
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NLP Application
Applying Natural Language Processing (NLP) concepts to help humans in their daily life, this book discusses an automatic translation of an unstructured Natural Language Question (NLQ) into a Structured Query Language (SQL) statement. Using SQL as a Relational DataBase (RDB) interaction language, database administrators or general users with little to no SQL querying abilities are provided with all the knowledge necessary to perform queries on RDBs in an interactive manner. Key Features:Includes extensive and illustrative examples to simplify the discussed conceptsDiscusses a novel, and yet simple, approach to NLPIntroduces a lightweight NLQ into SQL translation approach through the use of RDB MetaTables as a Hash tableExtensive literature review and thorough background information on every tool, concept and technique appliedProviding a unique approach to NLQ into SQL translation, as well as comprising disparate resources on NLP as a whole, this shortform book is of direct use to administrators and general users of databases.
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Deep Reinforcement Learning with Python
Gain a theoretical understanding to the most popular libraries in deep reinforcement learning (deep RL). This new edition focuses on the latest advances in deep RL using a learn-by-coding approach, allowing readers to assimilate and replicate the latest research in this field. New agent environments ranging from games, and robotics to finance are explained to help you try different ways to apply reinforcement learning. A chapter on multi-agent reinforcement learning covers how multiple agents compete, while another chapter focuses on the widely used deep RL algorithm, proximal policy optimization (PPO). You'll see how reinforcement learning with human feedback (RLHF) has been used by chatbots, built using Large Language Models, e.g. ChatGPT to improve conversational capabilities.You'll also review the steps for using the code on multiple cloud systems and deploying models on platforms such as Hugging Face Hub. The code is in Jupyter Notebook, which canbe run on Google Colab, and other similar deep learning cloud platforms, allowing you to tailor the code to your own needs. Whether it’s for applications in gaming, robotics, or Generative AI, Deep Reinforcement Learning with Python will help keep you ahead of the curve.What You'll LearnExplore Python-based RL libraries, including StableBaselines3 and CleanRL Work with diverse RL environments like Gymnasium, Pybullet, and Unity MLUnderstand instruction finetuning of Large Language Models using RLHF and PPOStudy training and optimization techniques using HuggingFace, Weights and Biases, and Optuna Who This Book Is ForSoftware engineers and machine learning developers eager to sharpen their understanding of deep RL and acquire practical skills in implementing RL algorithms fromscratch.
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AI-Assisted Python for Nonprogrammers
It's hard to exaggerate Python's popularity: It's used for everything from data science to servers to the web. It's also famously "easy" to learn. However, learning to program in Python can still be a frustrating experience. AI-Assisted Python for Nonprogrammers is designed to give you the context you need to build a deep understanding not just of Python's syntax but also of the concepts behind it, even if you don't have any previous programming experience.Through clear explanations and explorations, author Reuven Lerner will have you writing simple Python programs in no time. By collaborating with AI chatbots to complete focused hands-on exercises, you'll reinforce core concepts like variables and values, conditions and loops, functions, and modules (while also getting a taste of coding with GenAI). But most importantly, you'll learn how to think about computers and software like a programmer—setting you up to better participate in technical conversations at your job and understand the influence of technology on our world.Write simple Python programs using core data structures, conditions, and loopsWrite and use Python functionsUse Python modulesGain the confidence to continue your Python journey
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NLP Neuro Linguistic Programming for Beginners: Transform Your Life Using NLP Hypnosis
In terms of personality, temperance, attitude, intelligence, technical ability, and beliefs, every human is truly unique. Some people love to be the center of attention while others prefer to retreat into the background. If they're satisfied with that life, there's nothing that can be done, but if they want to break free from that shell, NLP is a legitimate method. Most people who lack confidence are well aware of that fact. They'd like to be more outspoken but simply can't. They don't know how to train themselves to have faith or believe that their opinions have a hefty value. If you are one of those people, this is the perfect opportunity for you. There is a wide range of obscure teachings built specifically for you. Enclosed within the pages of this book, you'll find basic information regarding NLP, or neuro-linguistic programming, an advanced self-help technique that's sure to pick you up from the slumps of self-pity into the realm of confidence and achievement.
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Recent Advances in NLP: The Case of Arabic Language
In light of the rapid rise of new trends and applications in various natural language processing tasks, this book presents high-quality research in the field. Each chapter addresses a common challenge in a theoretical or applied aspect of intelligent natural language processing related to Arabic language. Many challenges encountered during the development of the solutions can be resolved by incorporating language technology and artificial intelligence.The topics covered include machine translation; speech recognition; morphological, syntactic, and semantic processing; information retrieval; text classification; text summarization; sentiment analysis; ontology construction; Arabizi translation; Arabic dialects; Arabic lemmatization; and building and evaluating linguistic resources.This book is a valuable reference for scientists, researchers, and students from academia and industry interested in computational linguistics and artificial intelligence, especially for Arabic linguistics and related areas.
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People’s Web Meets NLP
Collaboratively Constructed Language Resources (CCLRs) such as Wikipedia, Wiktionary, Linked Open Data, and various resources developed using crowdsourcing techniques such as Games with a Purpose and Mechanical Turk have substantially contributed to the research in natural language processing (NLP). Various NLP tasks utilize such resources to substitute for or supplement conventional lexical semantic resources and linguistically annotated corpora. These resources also provide an extensive body of texts from which valuable knowledge is mined. There are an increasing number of community efforts to link and maintain multiple linguistic resources. This book aims offers comprehensive coverage of CCLR-related topics, including their construction, utilization in NLP tasks, and interlinkage and management. Various Bachelor/Master/Ph.D. programs in natural language processing, computational linguistics, and knowledge discovery can use this book both as the main text and as a supplementary reading. The book also provides a valuable reference guide for researchers and professionals for the above topics.
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Hands-On Deep Learning Architectures with Python
Concepts, tools, and techniques to explore deep learning architectures and methodologiesKey FeaturesExplore advanced deep learning architectures using various datasets and frameworksImplement deep architectures for neural network models such as CNN, RNN, GAN, and many moreDiscover design patterns and different challenges for various deep learning architecturesBook DescriptionDeep learning architectures are composed of multilevel nonlinear operations that represent high-level abstractions; this allows you to learn useful feature representations from the data. This book will help you learn and implement deep learning architectures to resolve various deep learning research problems.Hands-On Deep Learning Architectures with Python explains the essential learning algorithms used for deep and shallow architectures. Packed with practical implementations and ideas to help you build efficient artificial intelligence systems (AI), this book will help you learn how neural networks play a major role in building deep architectures. You will understand various deep learning architectures (such as AlexNet, VGG Net, GoogleNet) with easy-to-follow code and diagrams. In addition to this, the book will also guide you in building and training various deep architectures such as the Boltzmann mechanism, autoencoders, convolutional neural networks (CNNs), recurrent neural networks (RNNs), natural language processing (NLP), GAN, and more—all with practical implementations.By the end of this book, you will be able to construct deep models using popular frameworks and datasets with the required design patterns for each architecture. You will be ready to explore the potential of deep architectures in today's world.What you will learnImplement CNNs, RNNs, and other commonly used architectures with PythonExplore architectures such as VGGNet, AlexNet, and GoogLeNetBuild deep learning architectures for AI applications such as face and image recognition, fraud detection, and many moreUnderstand the architectures and applications of Boltzmann machines and autoencoders with concrete examples Master artificial intelligence and neural network concepts and apply them to your architectureUnderstand deep learning architectures for mobile and embedded systemsWho this book is forIf you’re a data scientist, machine learning developer/engineer, or deep learning practitioner, or are curious about AI and want to upgrade your knowledge of various deep learning architectures, this book will appeal to you. You are expected to have some knowledge of statistics and machine learning algorithms to get the best out of this book
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Machine Learning With Python: Theory And Applications
Machine Learning (ML) has become a very important area of research widely used in various industries.This compendium introduces the basic concepts, fundamental theories, essential computational techniques, codes, and applications related to ML models. With a strong foundation, one can comfortably learn related topics, methods, and algorithms. Most importantly, readers with strong fundamentals can even develop innovative and more effective machine models for his/her problems. The book is written to achieve this goal.The useful reference text benefits professionals, academics, researchers, graduate and undergraduate students in AI, ML and neural networks.
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Mastering Reinforcement Learning with Python
Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practicesKey FeaturesUnderstand how large-scale state-of-the-art RL algorithms and approaches workApply RL to solve complex problems in marketing, robotics, supply chain, finance, cybersecurity, and moreExplore tips and best practices from experts that will enable you to overcome real-world RL challengesBook DescriptionReinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation, this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems, Markov decision processes, and dynamic programming, the book provides an in-depth review of the classical RL techniques, such as Monte Carlo methods and temporal-difference learning. After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. Then, you'll be introduced to some of the key approaches behind the most successful RL implementations, such as domain randomization and curiosity-driven learning. As you advance, you’ll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Ray’s RLlib package. You’ll also find out how to implement RL in areas such as robotics, supply chain management, marketing, finance, smart cities, and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book, you’ll have mastered how to train and deploy your own RL agents for solving RL problems.What you will learnModel and solve complex sequential decision-making problems using RLDevelop a solid understanding of how state-of-the-art RL methods workUse Python and TensorFlow to code RL algorithms from scratchParallelize and scale up your RL implementations using Ray's RLlib packageGet in-depth knowledge of a wide variety of RL topicsUnderstand the trade-offs between different RL approachesDiscover and address the challenges of implementing RL in the real worldWho this book is forThis book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
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