Mastering Machine Learning With Core Ml And Python
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Mastering Machine Learning With Core Ml And Python
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Author : Vardhan Agrawal
language : en
Publisher: AppCoda
Release Date : 2020-08-13
Mastering Machine Learning With Core Ml And Python written by Vardhan Agrawal and has been published by AppCoda this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-13 with Computers categories.
Machine learning, now more than ever, plays a pivotal role in almost everything we do in our digital lives. Whether it’s interacting with a virtual assistant like Siri or typing out a message to a friend, machine learning is the technology facilitating those actions. It’s clear that machine learning is here to stay, and as such, it’s a vital skill to have in the upcoming decades. This book covers Core ML in-depth. You will learn how to create and deploy your own machine learning model. On top of that, you will learn about Turi Create, Create ML, Keras, Firebase, and Jupyter Notebooks, just to name a few. These are a few examples of professional tools which are staples for many machine learning experts. By going through this book, you’ll also become proficient with Python, the language that’s most frequently used for machine learning. Plus, you would have created a handful of ready-to-use apps such as barcode scanners, image classifiers, and language translators. Most importantly, you will master the ins-and-outs of Core ML.
Machine Learning Projects For Mobile Applications
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Author : Karthikeyan NG
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-10-31
Machine Learning Projects For Mobile Applications written by Karthikeyan NG and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-31 with Computers categories.
Bring magic to your mobile apps using TensorFlow Lite and Core ML Key FeaturesExplore machine learning using classification, analytics, and detection tasks.Work with image, text and video datasets to delve into real-world tasksBuild apps for Android and iOS using Caffe, Core ML and Tensorflow LiteBook Description Machine learning is a technique that focuses on developing computer programs that can be modified when exposed to new data. We can make use of it for our mobile applications and this book will show you how to do so. The book starts with the basics of machine learning concepts for mobile applications and how to get well equipped for further tasks. You will start by developing an app to classify age and gender using Core ML and Tensorflow Lite. You will explore neural style transfer and get familiar with how deep CNNs work. We will also take a closer look at Google’s ML Kit for the Firebase SDK for mobile applications. You will learn how to detect handwritten text on mobile. You will also learn how to create your own Snapchat filter by making use of facial attributes and OpenCV. You will learn how to train your own food classification model on your mobile; all of this will be done with the help of deep learning techniques. Lastly, you will build an image classifier on your mobile, compare its performance, and analyze the results on both mobile and cloud using TensorFlow Lite with an RCNN. By the end of this book, you will not only have mastered the concepts of machine learning but also learned how to resolve problems faced while building powerful apps on mobiles using TensorFlow Lite, Caffe2, and Core ML. What you will learnDemystify the machine learning landscape on mobileAge and gender detection using TensorFlow Lite and Core MLUse ML Kit for Firebase for in-text detection, face detection, and barcode scanningCreate a digit classifier using adversarial learningBuild a cross-platform application with face filters using OpenCVClassify food using deep CNNs and TensorFlow Lite on iOS Who this book is for Machine Learning Projects for Mobile Applications is for you if you are a data scientist, machine learning expert, deep learning, or AI enthusiast who fancies mastering machine learning and deep learning implementation with practical examples using TensorFlow Lite and CoreML. Basic knowledge of Python programming language would be an added advantage.
Machine Learning For Ios Developers
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Author : Abhishek Mishra
language : en
Publisher: John Wiley & Sons
Release Date : 2020-02-12
Machine Learning For Ios Developers written by Abhishek Mishra and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-12 with Computers categories.
Harness the power of Apple iOS machine learning (ML) capabilities and learn the concepts and techniques necessary to be a successful Apple iOS machine learning practitioner! Machine earning (ML) is the science of getting computers to act without being explicitly programmed. A branch of Artificial Intelligence (AI), machine learning techniques offer ways to identify trends, forecast behavior, and make recommendations. The Apple iOS Software Development Kit (SDK) allows developers to integrate ML services, such as speech recognition and language translation, into mobile devices, most of which can be used in multi-cloud settings. Focusing on Apple’s ML services, Machine Learning for iOS Developers is an up-to-date introduction to the field, instructing readers to implement machine learning in iOS applications. Assuming no prior experience with machine learning, this reader-friendly guide offers expert instruction and practical examples of ML integration in iOS. Organized into two sections, the book’s clearly-written chapters first cover fundamental ML concepts, the different types of ML systems, their practical uses, and the potential challenges of ML solutions. The second section teaches readers to use models—both pre-trained and user-built—with Apple’s CoreML framework. Source code examples are provided for readers to download and use in their own projects. This book helps readers: Understand the theoretical concepts and practical applications of machine learning used in predictive data analytics Build, deploy, and maintain ML systems for tasks such as model validation, optimization, scalability, and real-time streaming Develop skills in data acquisition and modeling, classification, and regression. Compare traditional vs. ML approaches, and machine learning on handsets vs. machine learning as a service (MLaaS) Implement decision tree based models, an instance-based machine learning system, and integrate Scikit-learn & Keras models with CoreML Machine Learning for iOS Developers is a must-have resource software engineers and mobile solutions architects wishing to learn ML concepts and implement machine learning on iOS Apps.
Foundations Of Machine Learning Classification For Transforming Data Into Smart Decisions
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Author : Dr. Poornima G. Naik Dr. Girish R. Naik
language : en
Publisher: Shashwat Publication
Release Date : 2025-10-17
Foundations Of Machine Learning Classification For Transforming Data Into Smart Decisions written by Dr. Poornima G. Naik Dr. Girish R. Naik and has been published by Shashwat Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-10-17 with Computers categories.
Foundations of Machine Learning Classification for Transforming Data into Smart Decisions (Bridging Fundamentals and Applications) is designed as a gateway into the world of machine learning, with a sharp focus on classification—the backbone of intelligent decision-making systems. It takes readers on a structured journey from the fundamentals of machine learning, to understanding classification theory, and finally to mastering logistic regression and Naïve Bayes with real-world examples and Python implementations. Machine Learning (ML) has become one of the most transformative technologies, powering applications from spam filtering and medical diagnosis to recommendation systems and fraud detection. Unlike traditional programming, where every rule must be explicitly defined, ML enables systems to learn patterns from data and make intelligent predictions or decisions. With the explosion of big data, the demand for ML skills has grown across industries such as IT, finance, healthcare, e-commerce, and manufacturing. For students, mastering ML provides not just technical knowledge but also the ability to solve real-world problems through data-driven decision-making. Understanding concepts like classification, supervised learning, and probabilistic models equips learners with the foundation to explore advanced domains such as deep learning and AI.
100 Steps To Learn Ai A Journey From Curiosity To Mastery
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Author : Dr. Gurram Veera Raghavaiah
language : en
Publisher: Dr. Gurram Veera Raghavaiah
Release Date : 2025-12-27
100 Steps To Learn Ai A Journey From Curiosity To Mastery written by Dr. Gurram Veera Raghavaiah and has been published by Dr. Gurram Veera Raghavaiah this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-12-27 with Antiques & Collectibles categories.
This book offers a transformative 100-step roadmap to holistic AI mastery across six phases, blending technical skills, ethics, and stewardship. Prologue: Awakening (Steps 1-5) begins with "What is intelligence?", contextualizing AI/ML/DL in daily life and inspiring vision. Phase I: Foundation (6-20) builds Python fluency with NumPy, Pandas, Matplotlib; revives Linear Algebra, Stats, Calculus as model language; sets up environments, version control, first predictive program, and early ethics. Phase II: ML Engine (21-40) covers supervised/unsupervised/RL algorithms (regression, KNN, trees, SVMs, clustering), Scikit-learn workflows, metrics (accuracy, F1, RMSE), bias-variance tradeoff, and end-to-end projects. Phase III: Deep Dive (41-60) explores perceptrons to backprop, TensorFlow/PyTorch, CNNs/RNNs/LSTMs, Dropout/Transfer Learning, GANs/VAEs, and NLP basics. Phase IV: Frontier (61-80) introduces Transformers/Hugging Face, RL (Q-Learning/DQNs), Docker/cloud deployment, Kaggle/ArXiv engagement, and portfolio building. Phase V: Integration (81-95) specializes in Vision/NLP/RL, masters MLOps, XAI (SHAP/LIME), bias/fairness, interdisciplinary fusion, and communication/mentoring for T-shaped professionals. Phase VI: Ascent (96-100) demands novel projects, open sharing, and "nurture the garden" stewardship. This narrative expedition cultivates wise practitioners to integrate AI responsibly into society
Python Machine Learning
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Author : Andrew Park
language : en
Publisher: Andrew Park
Release Date : 2021-04-27
Python Machine Learning written by Andrew Park and has been published by Andrew Park this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-27 with categories.
★ 55% OFF for Bookstores! NOW at $ 17.99 instead of $ 39.97! LAST DAYS! ★ Do you want to learn how to design and master different Machine Learning algorithms quickly and easily?Your Customers Will Love This Amazing Guide! Today, we live in the era of Artificial Intelligence. Self-driving cars, customized product recommendations, real-time pricing, speech and facial recognition are just a few examples proving this truth. Also, think about medical diagnostics or automation of mundane and repetitive labor tasks; all these highlight the fact that we live in interesting times. From research topics to projects and applications in different stages of production, there is a lot going on in the world of Machine Learning. Machines and automation represent a huge part of our daily life. They are becoming part of our experience and existence. This is Machine Learning. Artificial Intelligence is currently one of the most thriving fields any programmer would wish to delve into, and for a good reason: this is the future! Simply put, Machine Learning is about teaching machines to think and make decisions as we would. The difference between the way machines learn and the way we do is that while for the most part we learn from experiences, machines learn from data. Starting from scratch, Python Machine Learning explains how this happens, how machines build their experience and compounding knowledge. Data forms the core of Machine Learning because within data lie truths whose depths exceed our imagination. The computations machines can perform on data are incredible, beyond anything a human brain could do. Once we introduce data to a machine learning model, we must create an environment where we update the data stream frequently. This builds the machine's learning ability. The more data Machine Learning models are exposed to, the easier it is for these models to expand their potential. Some of the topics that we will discuss inside include: What is Machine Learning and how it is applied in real-world situations Understanding the differences between Machine Learning, Deep Learning, and Artificial Intelligence Supervised learning, unsupervised learning, and semi-supervised learning The place of Regression techniques in Machine Learning, including Linear Regression in Python Machine learning training models How to use Lists and Modules in Python The 12 essential libraries for Machine Learning in Python What is the Tensorflow library Artificial Neural Networks And Much More! While most books only focus on widespread details without going deeper into the different models and techniques, Python Machine Learning explains how to master the concepts of Machine Learning technology and helps you to understand how researchers are breaking the boundaries of Data Science to mimic human intelligence in machines using various Machine Learning algorithms. Even if some concepts of Machine Learning algorithms can appear complex to most computer programming beginners, this book takes the time to explain them in a simple and concise way. Would You Like To Know More? Buy It NOW And Let Your Customers Get Addicted To This Amazing Book!
Core Ml Model Conversion Essentials
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Author : William Smith
language : en
Publisher: HiTeX Press
Release Date : 2025-08-19
Core Ml Model Conversion Essentials written by William Smith and has been published by HiTeX Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-19 with Computers categories.
"Core ML Model Conversion Essentials" "Core ML Model Conversion Essentials" is a comprehensive guide for developers and machine learning engineers seeking to master the intricacies of adapting machine learning models to Apple’s Core ML platform. The book meticulously explores the architecture of Core ML, delving into its supported model types and seamless integration with the wider Apple ecosystem. Readers are introduced to essential workflows tailored for iOS, macOS, and other Apple platforms, underpinned by insightful discussions on model formats, compatibility challenges, and the motivations for converting models to Core ML for innovative, real-world applications. Organized into practical chapters, the text walks through every phase of the model conversion pipeline—from preparing models with appropriate preprocessing and feature engineering, to optimizing, exporting, and validating within the unique constraints of Core ML. The book offers in-depth coverage of popular frameworks such as TensorFlow, PyTorch, ONNX, XGBoost, and scikit-learn, providing actionable strategies for handling complex architectures, non-standard layers, and scalable batch conversion scenarios. Advanced tooling, including coremltools and other third-party utilities, is dissected, empowering readers to customize, debug, and maintain robust conversion pipelines. Beyond the conversion process itself, the guide equips practitioners with critical strategies for model validation, optimization, security, and privacy in production deployments. Through detailed chapters on device-level verification, regulatory compliance, threat modeling, and performance tuning, readers gain the knowledge needed to deliver efficient, secure, and privacy-preserving machine learning experiences on Apple hardware. The book concludes with industry best practices, emerging trends, and inspiring case studies, establishing itself as an indispensable resource for anyone committed to delivering state-of-the-art Core ML solutions.
Machine Learning Python For Absolute Beginners
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Author : Oliver Theobald
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-08-20
Machine Learning Python For Absolute Beginners written by Oliver Theobald and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-20 with Computers categories.
A clear and beginner-focused guide to Python and ML fundamentals. Covers coding basics, OOP, and core machine learning methods in a friendly, structured format. Key Features A two-part structure combining Python basics and machine learning for seamless skill-building Logical progression designed to reduce learning friction and build strong conceptual clarity Hands-on practice with Jupyter notebooks and real datasets to reinforce every key concept taught Book DescriptionStarting with Python syntax and data types, this guide builds toward implementing key machine learning models. Learn about loops, functions, OOP, and data cleaning, then transition into algorithms like regression, KNN, and neural networks. A final section walks you through model optimization and building projects in Python. The book is split into two major sections—foundational Python programming and introductory machine learning. Readers are guided through essential concepts such as data types, variables, control flow, object-oriented programming, and using libraries like pandas for data manipulation. In the machine learning section, topics like model selection, supervised vs unsupervised learning, bias-variance, and common algorithms are demystified with practical coding examples. It’s a structured, clear roadmap to mastering both programming and applied ML from zero knowledge.What you will learn Master Python syntax, variables, and basic data structures Build control flows using conditionals, loops, and functions Implement object-oriented concepts like classes and objects Analyze and clean datasets using pandas and Python tools Train supervised and unsupervised machine learning models Evaluate and optimize models for better prediction accuracy Who this book is for This book is perfect for beginners with little to no coding or data science background. It assumes no prior experience with Python or machine learning. Ideal for aspiring data analysts, tech learners, and students transitioning into AI and programming roles.
Intelligent Projects Using Python
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Author : Santanu Pattanayak
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-01-31
Intelligent Projects Using Python written by Santanu Pattanayak and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-31 with Computers categories.
Implement machine learning and deep learning methodologies to build smart, cognitive AI projects using Python Key FeaturesA go-to guide to help you master AI algorithms and concepts8 real-world projects tackling different challenges in healthcare, e-commerce, and surveillanceUse TensorFlow, Keras, and other Python libraries to implement smart AI applicationsBook Description This book will be a perfect companion if you want to build insightful projects from leading AI domains using Python. The book covers detailed implementation of projects from all the core disciplines of AI. We start by covering the basics of how to create smart systems using machine learning and deep learning techniques. You will assimilate various neural network architectures such as CNN, RNN, LSTM, to solve critical new world challenges. You will learn to train a model to detect diabetic retinopathy conditions in the human eye and create an intelligent system for performing a video-to-text translation. You will use the transfer learning technique in the healthcare domain and implement style transfer using GANs. Later you will learn to build AI-based recommendation systems, a mobile app for sentiment analysis and a powerful chatbot for carrying customer services. You will implement AI techniques in the cybersecurity domain to generate Captchas. Later you will train and build autonomous vehicles to self-drive using reinforcement learning. You will be using libraries from the Python ecosystem such as TensorFlow, Keras and more to bring the core aspects of machine learning, deep learning, and AI. By the end of this book, you will be skilled to build your own smart models for tackling any kind of AI problems without any hassle. What you will learnBuild an intelligent machine translation system using seq-2-seq neural translation machinesCreate AI applications using GAN and deploy smart mobile apps using TensorFlowTranslate videos into text using CNN and RNNImplement smart AI Chatbots, and integrate and extend them in several domainsCreate smart reinforcement, learning-based applications using Q-LearningBreak and generate CAPTCHA using Deep Learning and Adversarial Learning Who this book is for This book is intended for data scientists, machine learning professionals, and deep learning practitioners who are ready to extend their knowledge and potential in AI. If you want to build real-life smart systems to play a crucial role in every complex domain, then this book is what you need. Knowledge of Python programming and a familiarity with basic machine learning and deep learning concepts are expected to help you get the most out of the book
Mastering Ai And Machine Learning With Python
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Author : Anshuman Mishra
language : en
Publisher: Independently Published
Release Date : 2025-05-12
Mastering Ai And Machine Learning With Python written by Anshuman Mishra and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-05-12 with Computers categories.
This ambitious two-volume work, "Mastering AI and Machine Learning with Python: From Fundamentals to Advanced Deep Learning," aims to be a definitive guide for anyone seeking to understand, implement, and master the intricate world of Artificial Intelligence (AI) and Machine Learning (ML) using the versatile Python programming language. Spanning a projected 10,000 words across both volumes (with Volume 1 detailed below), this book meticulously progresses from foundational concepts to cutting-edge deep learning techniques, providing readers with a robust theoretical understanding coupled with practical implementation skills. Volume 1: Foundations and Core Machine Learning Techniques Volume 1 lays the essential groundwork for embarking on the journey of AI and ML. It is structured to take individuals with varying levels of prior knowledge - from complete beginners to those with some programming experience - and equip them with the core competencies required to understand and apply fundamental machine learning algorithms. Chapter 1: Introduction to AI and Machine Learning This introductory chapter serves as a compass, orienting the reader within the broad landscape of AI and its subfields. It begins by clearly delineating the concepts of Artificial Intelligence, Machine Learning, and Deep Learning, highlighting their relationships and distinctions. Understanding AI, Machine Learning, and Deep Learning: This section meticulously unpacks these often-interchangeable terms. It defines AI as the overarching field focused on creating intelligent agents capable of performing tasks that typically require human intelligence. Machine Learning is then presented as a subset of AI, where systems learn from data without being explicitly programmed. Finally, Deep Learning is introduced as a subfield of ML that utilizes artificial neural networks with multiple layers (deep neural networks) to extract complex patterns from large datasets. The chapter will use analogies and real-world examples to solidify these definitions, ensuring a clear understanding of the hierarchy and unique characteristics of each field. Real-World Applications of AI: To underscore the practical relevance and transformative power of AI, this section delves into a diverse range of real-world applications. It will explore how AI is revolutionizing industries such as healthcare (diagnosis, drug discovery), finance (fraud detection, algorithmic trading), transportation (autonomous vehicles), entertainment (recommendation systems), manufacturing (predictive maintenance), and customer service (chatbots). Each application will be briefly described, highlighting the specific AI techniques employed and the tangible benefits realized. This section aims to inspire the reader and contextualize the learning journey ahead. The Role of Python in AI Development: This crucial segment emphasizes why Python has emerged as the lingua franca of AI and ML. It will discuss Python's key advantages, including its clear and concise syntax, extensive ecosystem of powerful libraries (such as NumPy, Pandas, Scikit-learn, TensorFlow, and PyTorch), large and active community support, and its versatility for various stages of the AI development lifecycle - from data preprocessing to model deployment. The chapter will briefly introduce some of these key libraries, setting the stage for their detailed exploration in subsequent chapters. Overview of TensorFlow and PyTorch: As two of the most prominent deep learning frameworks, TensorFlow and PyTorch are introduced in this section. The chapter will provide a high-level overview of their functionalities, key features, and their respective strengths and weaknesses. It will touch upon their roles in building and training neural networks, their support for hardware acceleration (GPUs), and their growing adoption in both research and industry.