Hands On Ai Building Ml Models With Python
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Hands On Ai Building Ml Models With Python
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Author : Anand Vemula
language : en
Publisher: Anand Vemula
Release Date :
Hands On Ai Building Ml Models With Python written by Anand Vemula and has been published by Anand Vemula this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
"Hands-On AI: Building ML Models with Python" provides a comprehensive guide to understanding and applying machine learning (ML) using Python. The book covers the fundamental concepts, mathematical foundations, and the essential tools necessary for building successful ML models. It begins with an introduction to machine learning, explaining the basics and setting up the Python environment for AI development. The book then delves into data preparation and feature engineering, exploring techniques for data cleaning, wrangling, and visualization, all of which are crucial for effective model training. The book also addresses core machine learning algorithms, including supervised and unsupervised learning, regression models, classification models, and ensemble methods. Advanced topics such as deep learning, natural language processing (NLP), reinforcement learning, and time series forecasting are also discussed in detail. Practical applications and real-world examples are integrated throughout, allowing readers to see how theoretical concepts are applied in industry scenarios. Additionally, the book explores model evaluation, optimization, and deployment, including how to build and deploy end-to-end ML pipelines. Readers will gain insights into scaling models, automating workflows, and implementing CI/CD for machine learning. With a focus on hands-on experience, the book is designed for practitioners who want to enhance their skills and develop practical, deployable machine learning models. It serves as both an introductory and advanced reference, offering invaluable knowledge for those looking to pursue careers in machine learning and AI.
Machine Learning With Python
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Author : Cassia Hawke
language : en
Publisher: Independently Published
Release Date : 2025-06-10
Machine Learning With Python written by Cassia Hawke and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-06-10 with Computers categories.
Unlock the Secrets of Artificial Intelligence with Python! Dive into the world of Machine Learning and AI with this comprehensive, step-by-step guide designed for both beginners and seasoned developers. Whether you're looking to enhance your data science skills or jumpstart your journey into the fascinating realm of AI, this book is your ultimate companion. Packed with hands-on projects, clear explanations, and real-world examples, you'll master the core concepts of machine learning, including: Supervised and Unsupervised Learning Neural Networks and Deep Learning Reinforcement Learning Natural Language Processing (NLP) Generative Models & GANs Building and Evaluating Models with Python's most popular libraries like NumPy, Pandas, Scikit-learn, and TensorFlow Real-World Applications: From healthcare to finance, marketing, and robotics, you'll explore how machine learning is transforming industries. Build practical applications like a recommendation system, sentiment analysis model, and even develop a simple Q-learning model to play Tic-Tac-Toe. Why This Book? Comprehensive Coverage: Learn from the basics of Python to advanced machine learning algorithms. Hands-On Projects: Learn by doing. Build your own AI models from scratch! AI Ethics: Understand the ethical considerations surrounding machine learning, from bias to data privacy. Practical Approach: Focus on real-world applications to give you a competitive edge in your career. Start building your own AI models today! Master Python and machine learning with this beginner-friendly yet comprehensive guide.
Getting Started With Ai And Machine Learning In Python
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Author : ROGERS. ISAACSON
language : en
Publisher: Independently Published
Release Date : 2025-04-11
Getting Started With Ai And Machine Learning In Python written by ROGERS. ISAACSON and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-11 with Computers categories.
Begin your journey into the world of Artificial Intelligence (AI) and Machine Learning (ML) with Getting Started with AI and Machine Learning in Python. This beginner-friendly guide will teach you the core concepts of AI and ML and show you how to build your first machine learning models using Python. Whether you're new to programming or have some experience with Python, this book offers a practical and approachable way to dive into the fascinating world of AI. Python is the most popular language for AI and machine learning because of its simplicity and the powerful libraries available, such as Scikit-learn, TensorFlow, and Keras. This book will introduce you to the foundational principles of AI and guide you through building and evaluating your first machine learning models. You'll gain hands-on experience by working through projects that involve real-world datasets and learn how to use AI to solve practical problems. Inside, you'll learn: The fundamentals of AI and machine learning, including supervised and unsupervised learning How to set up your Python environment for AI and ML development The basics of data preprocessing, including cleaning and transforming data for use in machine learning models How to build simple machine learning models with Scikit-learn, including linear regression, decision trees, and k-nearest neighbors Techniques for model evaluation, including cross-validation, confusion matrices, and performance metrics like accuracy and precision How to visualize data and results with Python's popular libraries like Matplotlib and Seaborn A brief introduction to deep learning concepts and how to implement neural networks using Keras Best practices for training, testing, and tuning machine learning models to improve accuracy By the end of this book, you'll have a solid understanding of AI and machine learning concepts and practical experience building models in Python. Whether you're looking to pursue a career in data science, work on personal projects, or simply learn more about AI, Getting Started with AI and Machine Learning in Python is the perfect guide to get you started. Key Features: Learn the core concepts of AI and machine learning in an easy-to-understand format Step-by-step instructions for building your first machine learning models in Python Hands-on projects using real-world datasets to practice your skills Best practices for evaluating, tuning, and improving your machine learning models Introduction to deep learning and neural networks with Keras Start learning AI and machine learning today with Getting Started with AI and Machine Learning in Python and build the foundation for your AI-driven projects.
Hands On Deep Learning Architectures With Python
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Author : Yuxi (Hayden) Liu
language : en
Publisher: Packt Publishing Ltd
Release Date : 2019-04-30
Hands On Deep Learning Architectures With Python written by Yuxi (Hayden) Liu 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 2019-04-30 with Computers categories.
Concepts, tools, and techniques to explore deep learning architectures and methodologies Key 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 Description Deep 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 for If 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
Hands On Explainable Ai Xai With Python
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Author : Denis Rothman
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-07-31
Hands On Explainable Ai Xai With Python written by Denis Rothman 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 2020-07-31 with Computers categories.
Resolve the black box models in your AI applications to make them fair, trustworthy, and secure. Familiarize yourself with the basic principles and tools to deploy Explainable AI (XAI) into your apps and reporting interfaces. Key FeaturesLearn explainable AI tools and techniques to process trustworthy AI resultsUnderstand how to detect, handle, and avoid common issues with AI ethics and biasIntegrate fair AI into popular apps and reporting tools to deliver business value using Python and associated toolsBook Description Effectively translating AI insights to business stakeholders requires careful planning, design, and visualization choices. Describing the problem, the model, and the relationships among variables and their findings are often subtle, surprising, and technically complex. Hands-On Explainable AI (XAI) with Python will see you work with specific hands-on machine learning Python projects that are strategically arranged to enhance your grasp on AI results analysis. You will be building models, interpreting results with visualizations, and integrating XAI reporting tools and different applications. You will build XAI solutions in Python, TensorFlow 2, Google Cloud’s XAI platform, Google Colaboratory, and other frameworks to open up the black box of machine learning models. The book will introduce you to several open-source XAI tools for Python that can be used throughout the machine learning project life cycle. You will learn how to explore machine learning model results, review key influencing variables and variable relationships, detect and handle bias and ethics issues, and integrate predictions using Python along with supporting the visualization of machine learning models into user explainable interfaces. By the end of this AI book, you will possess an in-depth understanding of the core concepts of XAI. What you will learnPlan for XAI through the different stages of the machine learning life cycleEstimate the strengths and weaknesses of popular open-source XAI applicationsExamine how to detect and handle bias issues in machine learning dataReview ethics considerations and tools to address common problems in machine learning dataShare XAI design and visualization best practicesIntegrate explainable AI results using Python modelsUse XAI toolkits for Python in machine learning life cycles to solve business problemsWho this book is for This book is not an introduction to Python programming or machine learning concepts. You must have some foundational knowledge and/or experience with machine learning libraries such as scikit-learn to make the most out of this book. Some of the potential readers of this book include: Professionals who already use Python for as data science, machine learning, research, and analysisData analysts and data scientists who want an introduction into explainable AI tools and techniquesAI Project managers who must face the contractual and legal obligations of AI Explainability for the acceptance phase of their applications
Hands On Ai Programming With Python
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Author : Khushabu Gupta
language : en
Publisher: Subrat Gupta
Release Date : 2025-09-30
Hands On Ai Programming With Python written by Khushabu Gupta and has been published by Subrat Gupta this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-09-30 with Computers categories.
Unlock the full potential of artificial intelligence with 'Hands-On AI Programming with Python.' This comprehensive guide empowers beginners and seasoned developers alike to master modern AI techniques from the ground up. Dive into practical, real-world projects that cover machine learning, deep learning, and generative AI using powerful frameworks like TensorFlow, PyTorch, and FastAPI. Learn to build, train, and deploy smarter applications using Python, tackle hands-on projects such as image recognition, natural language processing, and AI-powered APIs, and grasp industry best practices for performance and scalability. This 2025 edition is updated to reflect the latest trends, tools, and workflows in the rapidly-evolving AI landscape. With step-by-step instructions, code examples, and expert insights, you’ll develop the confidence to innovate and create robust AI solutions. Whether you're an aspiring data scientist, an AI enthusiast, or a developer seeking to expand your skill set, this book is the key to mastering applied AI programming and advancing your career in today’s tech-driven world.
Hands On Ai Trading With Python Quantconnect And Aws
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Author : Jiri Pik
language : en
Publisher: John Wiley & Sons
Release Date : 2025-01-22
Hands On Ai Trading With Python Quantconnect And Aws written by Jiri Pik 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 2025-01-22 with Business & Economics categories.
Master the art of AI-driven algorithmic trading strategies through hands-on examples, in-depth insights, and step-by-step guidance Hands-On AI Trading with Python, QuantConnect, and AWS explores real-world applications of AI technologies in algorithmic trading. It provides practical examples with complete code, allowing readers to understand and expand their AI toolbelt. Unlike other books, this one focuses on designing actual trading strategies rather than setting up backtesting infrastructure. It utilizes QuantConnect, providing access to key market data from Algoseek and others. Examples are available on the book's GitHub repository, written in Python, and include performance tearsheets or research Jupyter notebooks. The book starts with an overview of financial trading and QuantConnect's platform, organized by AI technology used: Examples include constructing portfolios with regression models, predicting dividend yields, and safeguarding against market volatility using machine learning packages like SKLearn and MLFinLab. Use principal component analysis to reduce model features, identify pairs for trading, and run statistical arbitrage with packages like LightGBM. Predict market volatility regimes and allocate funds accordingly. Predict daily returns of tech stocks using classifiers. Forecast Forex pairs' future prices using Support Vector Machines and wavelets. Predict trading day momentum or reversion risk using TensorFlow and temporal CNNs. Apply large language models (LLMs) for stock research analysis, including prompt engineering and building RAG applications. Perform sentiment analysis on real-time news feeds and train time-series forecasting models for portfolio optimization. Better Hedging by Reinforcement Learning and AI: Implement reinforcement learning models for hedging options and derivatives with PyTorch. AI for Risk Management and Optimization: Use corrective AI and conditional portfolio optimization techniques for risk management and capital allocation. Written by domain experts, including Jiri Pik, Ernest Chan, Philip Sun, Vivek Singh, and Jared Broad, this book is essential for hedge fund professionals, traders, asset managers, and finance students. Integrate AI into your next algorithmic trading strategy with Hands-On AI Trading with Python, QuantConnect, and AWS.
Python Ai For Beginners
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Author : THOMPSON. CARTER
language : en
Publisher: Independently Published
Release Date : 2025-01-29
Python Ai For Beginners written by THOMPSON. CARTER and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-29 with Computers categories.
Python AI for Beginners: Build Your First Machine Learning Project Today Dive into the world of artificial intelligence and machine learning with Python AI for Beginners, the perfect guide to kickstart your journey into building smart systems. Designed for beginners with no prior experience in AI, this book simplifies complex concepts and provides a hands-on approach to building your first machine learning project using Python. Whether you're a student, a coding enthusiast, or someone curious about AI, this guide equips you with the tools, techniques, and foundational knowledge to start creating AI-powered applications today. What You'll Learn: Introduction to Artificial Intelligence: Understand what AI is, how it works, and its impact on industries. Python Essentials for AI: Learn the basics of Python, including syntax, data types, and libraries used in machine learning. Getting Started with Machine Learning: Discover the fundamentals of machine learning, including supervised and unsupervised learning. Data Preprocessing: Clean, transform, and prepare your data for machine learning models. Using Popular Libraries: Get hands-on with essential Python libraries like NumPy, pandas, and Matplotlib for data manipulation and visualization. Building Your First Model: Create a simple machine learning project, such as a classification model, using scikit-learn. Evaluating Model Performance: Understand key metrics like accuracy, precision, recall, and F1-score to assess your model's effectiveness. Feature Engineering: Learn how to select and engineer features to improve model accuracy. Intro to Neural Networks: Get a glimpse into neural networks and how they power AI systems. Real-World Applications: Explore practical AI use cases, including recommendation systems, image classification, and sentiment analysis. Ethics in AI: Learn the importance of ethical considerations in building AI systems. Step-by-Step Project: Follow a detailed guide to build and deploy your first AI-powered Python project from start to finish. Who Is This Book For? This book is ideal for beginners, students, hobbyists, and anyone eager to explore AI and machine learning with Python. Why Choose This Book? With its beginner-friendly approach, practical examples, and hands-on projects, Python AI for Beginners makes learning AI accessible, empowering you to start building intelligent systems right away. Start your AI journey today with Python AI for Beginners: Build Your First Machine Learning Project Today-your gateway to the exciting world of artificial intelligence and machine learning.
Hands On Transfer Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar 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 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Machine Learning Engineering With Python
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Author : Andrew P. McMahon
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-11-05
Machine Learning Engineering With Python written by Andrew P. McMahon 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 2021-11-05 with Computers categories.
Supercharge the value of your machine learning models by building scalable and robust solutions that can serve them in production environments Key Features Explore hyperparameter optimization and model management tools Learn object-oriented programming and functional programming in Python to build your own ML libraries and packages Explore key ML engineering patterns like microservices and the Extract Transform Machine Learn (ETML) pattern with use cases Book DescriptionMachine learning engineering is a thriving discipline at the interface of software development and machine learning. This book will help developers working with machine learning and Python to put their knowledge to work and create high-quality machine learning products and services. Machine Learning Engineering with Python takes a hands-on approach to help you get to grips with essential technical concepts, implementation patterns, and development methodologies to have you up and running in no time. You'll begin by understanding key steps of the machine learning development life cycle before moving on to practical illustrations and getting to grips with building and deploying robust machine learning solutions. As you advance, you'll explore how to create your own toolsets for training and deployment across all your projects in a consistent way. The book will also help you get hands-on with deployment architectures and discover methods for scaling up your solutions while building a solid understanding of how to use cloud-based tools effectively. Finally, you'll work through examples to help you solve typical business problems. By the end of this book, you'll be able to build end-to-end machine learning services using a variety of techniques and design your own processes for consistently performant machine learning engineering.What you will learn Find out what an effective ML engineering process looks like Uncover options for automating training and deployment and learn how to use them Discover how to build your own wrapper libraries for encapsulating your data science and machine learning logic and solutions Understand what aspects of software engineering you can bring to machine learning Gain insights into adapting software engineering for machine learning using appropriate cloud technologies Perform hyperparameter tuning in a relatively automated way Who this book is for This book is for machine learning engineers, data scientists, and software developers who want to build robust software solutions with machine learning components. If you're someone who manages or wants to understand the production life cycle of these systems, you'll find this book useful. Intermediate-level knowledge of Python is necessary.