Download or read book Machine Learning with MOJO Programming Language written by Edward R DeForest and published by Independently Published. This book was released on 2024-04-08 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Conquer Machine Learning with Mojo: Your Guide to High-Performance Models & Rapid AI Development About the Technology: Mojo is a revolutionary programming language designed to bridge the gap between research and production in machine learning. It offers the ease of use of Python with the lightning-fast performance of C++, making it ideal for building powerful and efficient AI applications. Worried about complex machine learning? Fret no more! This book guides you step-by-step through the Mojo development process, even if you're new to machine learning. Learn how to leverage Mojo's intuitive syntax and powerful features to create sophisticated models without getting bogged down in complex details. Challenges of slow development cycles? Say goodbye to waiting! Mojo's focus on speed allows you to rapidly prototype, train, and deploy your machine learning models. This book equips you with the knowledge and techniques to streamline your development workflow and bring your AI ideas to life faster than ever before. What to Expect: Master the fundamentals: Gain a solid understanding of machine learning concepts and techniques. Harness the power of Mojo: Learn how to leverage Mojo's unique features to build high-performance models. Navigate the development process: Discover practical guidance on data preparation, model training, deployment, and optimization. Real-world applications: Explore how to apply machine learning to solve problems in various domains. Code examples and exercises: Put your learning into practice with hands-on projects that solidify your understanding. Ready to unlock the potential of machine learning with Mojo? This book is your ultimate roadmap to success. Get started today and embark on your journey to becoming a confident and productive machine learning develop

Download or read book Mojo Programming for Beginners written by David A Fitzgerald and published by Independently Published. This book was released on 2024-05-10 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Become an AI Developer: Your Beginner's Guide to Mojo Programming Unleash the Power of AI with Mojo Mojo is a revolutionary programming language designed specifically for Artificial Intelligence. It combines the readability of Python with the lightning-fast performance of C, making it the perfect tool to build cutting-edge AI applications. Even if you're new to coding, Mojo's intuitive syntax and wealth of practical examples will have you creating intelligent programs in no time. Turn Your Ideas into Reality Imagine building AI-powered tools that can: Analyze massive datasets and uncover hidden insights Develop chatbots that can hold natural conversations Create intelligent systems that learn and adapt on their own With Mojo Programming for Beginners, these possibilities become a reality. What You'll Learn Inside: Master Mojo's fundamental concepts, including variables, data types, and control flow. Craft powerful functions and leverage object-oriented programming techniques. Navigate the exciting world of Machine Learning with clear, step-by-step guidance. Debug your code effectively and ensure your AI applications run smoothly. Explore Mojo's unique features like Single Instruction, Multiple Data (SIMD) for unparalleled performance. Benefits You'll Reap: Gain a coveted skillset in high demand within the booming AI industry. Bring your innovative ideas to life with the power of Mojo. Simplify complex AI concepts with a beginner-friendly approach. Stay ahead of the curve in the ever-evolving world of technology. Not Sure if Mojo is Right for You? Whether you're a complete beginner or have some programming experience, Mojo Programming for Beginners is designed to be accessible and engaging. The book provides a solid foundation in Mojo, preparing you to tackle more advanced AI projects. Take Your First Step Towards AI Mastery Don't wait any longer to unlock the potential of AI. Purchase your copy of Mojo Programming for Beginners today and embark on your journey to becoming a successful AI developer!

Download or read book MOJO Programming Beginners Guide written by Edward R DeForest and published by Independently Published. This book was released on 2024-04-11 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unleash Your Mojo: A Beginner's Guide to MOJO Programming About MOJO: MOJO is a revolutionary programming language designed to bridge the gap between user-friendly languages like Python and the high-performance capabilities of systems languages like C++. It offers: Easy-to-learn syntax: If you're familiar with Python, you'll pick up MOJO quickly. Blazing-fast performance: MOJO leverages cutting-edge compilation techniques to deliver impressive speed for AI and machine learning applications. Seamless Python integration: Import and utilize Python libraries within your MOJO programs for added functionality. Worries You Might Have: Is MOJO too new? While still under development, MOJO has garnered significant interest, and its potential is undeniable. Learning MOJO now positions you at the forefront of this exciting technology. Is it hard to learn? MOJO's beginner-friendly syntax makes it approachable, and this book provides a step-by-step guide to ease you into the world of MOJO programming. Challenges You'll Overcome: Grasping the fundamentals: This book will equip you with the core concepts of MOJO, including variables, data types, control flow, and functions. Building real-world applications: As you progress, you'll learn to work with files, leverage object-oriented programming techniques, and tackle practical programming problems with MOJO. What to Expect: A clear and concise roadmap: This book offers a structured learning path, taking you from the basics to more advanced MOJO concepts. Hands-on exercises: Solidify your understanding with engaging exercises that reinforce your learning throughout the book. Invaluable code examples: Numerous code snippets will show you how to apply MOJO concepts in practice. Don't wait! Take charge of your programming journey and become a MOJO pro. This beginner's guide is your key to unlocking the potential of MOJO and revolutionizing your approach to AI and machine learning application development. Get your co

Download or read book Machine Learning For Dummies written by John Paul Mueller and published by John Wiley & Sons. This book was released on 2016-05-31 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: Your no-nonsense guide to making sense of machine learning Machine learning can be a mind-boggling concept for the masses, but those who are in the trenches of computer programming know just how invaluable it is. Without machine learning, fraud detection, web search results, real-time ads on web pages, credit scoring, automation, and email spam filtering wouldn't be possible, and this is only showcasing just a few of its capabilities. Written by two data science experts, Machine Learning For Dummies offers a much-needed entry point for anyone looking to use machine learning to accomplish practical tasks. Covering the entry-level topics needed to get you familiar with the basic concepts of machine learning, this guide quickly helps you make sense of the programming languages and tools you need to turn machine learning-based tasks into a reality. Whether you're maddened by the math behind machine learning, apprehensive about AI, perplexed by preprocessing data—or anything in between—this guide makes it easier to understand and implement machine learning seamlessly. Grasp how day-to-day activities are powered by machine learning Learn to 'speak' certain languages, such as Python and R, to teach machines to perform pattern-oriented tasks and data analysis Learn to code in R using R Studio Find out how to code in Python using Anaconda Dive into this complete beginner's guide so you are armed with all you need to know about machine learning!

Download or read book Exploring Machine Learning A Beginners Perspective written by Dr. Raghuram Bhukya and published by Horizon Books ( A Division of Ignited Minds Edutech P Ltd). This book was released on 2021-04-20 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning is a field of Artificial intelligence that provides algorithms those can learn and improve from experiences. Machine learning algorithms are turned as integral parts of today’s digital life. Its applications include recommender systems, targeted campaigns, text categorization, computer vision and auto security systems etc. Machine learning also considered as essential part of data science due to its capability of providing predictive analytics, capability in handling variety of data and suitability for big data applications. Its capability for predictive analytics resulted of its general structure that is building statistical models out of training data. In other hand easy scalability advantage of machine learning algorithms is making them to be suitable for big data applications. The different types of learning algorithms includes supervised learning, unsupervised learning, reinforcement learning, feature learning, rule based learning, Robot or expert system learning, sparse dictionary and anomaly detection. These learning algorithms can be realized by computing models artificial neural networks, decision trees, support vector machines, regression analysis, Bayesian networks, Genetic algorithms and soft computing. The familiar tools to implement machine learning algorithms include Python, R, Matlab, Scala, Clojure and Ruby. Involving of such open source programming languages, tools and social network communities makes the machine learning most progressing filed of computer science. The machine learning life cycle includes defining project objectives, explore the types and format, modeling data to fit for machine learning algorithms, deciding suitable machine learning model and implement and decide best result from data for decision making. These days, machine learning is observing great interest by the society and it has turned as one of the significant responsibility of top level managers to transform their business in the profitable means by exploring its basic functionalities. The world is at the sheer of realizing a situation where machines will work in agreement with human being to work together, operation, and advertise their services in a novel way which is targeted, valuable, and well-versed. In order to achieve this, they can influence machine learning distinctiveness. Dr. Raghuram Bhukya

Download or read book Mojo Machine Learning Toolbox written by Thomas Hyden and published by Independently Published. This book was released on 2024-06-27 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Unlock the potential of AI with "Mojo Machine Learning Toolbox," an authoritative guide crafted by industry experts. This essential resource demystifies machine learning, providing you with the tools and knowledge to build impactful AI-driven applications. About the Technology: Delve into the world of machine learning, where complex algorithms and data processing techniques come together to solve real-world problems. The book covers state-of-the-art technologies, including neural networks, deep learning, and natural language processing, equipping you with the skills to implement these cutting-edge advancements in your projects. What's Inside: Comprehensive tutorials on essential machine learning techniques Practical examples and hands-on projects to reinforce learning Detailed case studies demonstrating AI applications in various industries Guidance on optimizing and deploying machine learning models Insights into the latest trends and future directions in AI Who this book is for: Whether you're a data scientist, software developer, or AI enthusiast, this book is designed to meet your needs. It's perfect for professionals looking to deepen their understanding of machine learning or newcomers eager to break into the field. Stay ahead of the curve in the rapidly evolving field of AI. "Mojo Machine Learning Toolbox" is your gateway to mastering the techniques that are shaping the future. Don't get left behind-start your journey today. Imagine transforming your projects with AI-driven solutions in just a matter of weeks. With clear, concise instructions and practical exercises, this book accelerates your learning process, enabling you to quickly apply what you've learned. Investing in this book means investing in your future. Gain the expertise needed to leverage AI technologies, enhance your career prospects, and drive innovation in your organization. This book is not just a learning tool-it's a career catalyst. Don't wait to unlock the power of AI. Get your copy of "Mojo Machine Learning Toolbox" today and start building the AI-driven applications that will set you apart in the tech industry. Order now and take the first step towards becoming an AI expert!

Download or read book Deep Learning for Coders with fastai and PyTorch written by Jeremy Howard and published by O'Reilly Media. This book was released on 2020-06-29 with total page 624 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning is often viewed as the exclusive domain of math PhDs and big tech companies. But as this hands-on guide demonstrates, programmers comfortable with Python can achieve impressive results in deep learning with little math background, small amounts of data, and minimal code. How? With fastai, the first library to provide a consistent interface to the most frequently used deep learning applications. Authors Jeremy Howard and Sylvain Gugger, the creators of fastai, show you how to train a model on a wide range of tasks using fastai and PyTorch. You’ll also dive progressively further into deep learning theory to gain a complete understanding of the algorithms behind the scenes. Train models in computer vision, natural language processing, tabular data, and collaborative filtering Learn the latest deep learning techniques that matter most in practice Improve accuracy, speed, and reliability by understanding how deep learning models work Discover how to turn your models into web applications Implement deep learning algorithms from scratch Consider the ethical implications of your work Gain insight from the foreword by PyTorch cofounder, Soumith Chintala

Download or read book Practical Automated Machine Learning Using H2O ai written by Salil Ajgaonkar and published by Packt Publishing Ltd. This book was released on 2022-09-26 with total page 396 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accelerate the adoption of machine learning by automating away the complex parts of the ML pipeline using H2O.ai Key FeaturesLearn how to train the best models with a single click using H2O AutoMLGet a simple explanation of model performance using H2O ExplainabilityEasily deploy your trained models to production using H2O MOJO and POJOBook Description With the huge amount of data being generated over the internet and the benefits that Machine Learning (ML) predictions bring to businesses, ML implementation has become a low-hanging fruit that everyone is striving for. The complex mathematics behind it, however, can be discouraging for a lot of users. This is where H2O comes in – it automates various repetitive steps, and this encapsulation helps developers focus on results rather than handling complexities. You'll begin by understanding how H2O's AutoML simplifies the implementation of ML by providing a simple, easy-to-use interface to train and use ML models. Next, you'll see how AutoML automates the entire process of training multiple models, optimizing their hyperparameters, as well as explaining their performance. As you advance, you'll find out how to leverage a Plain Old Java Object (POJO) and Model Object, Optimized (MOJO) to deploy your models to production. Throughout this book, you'll take a hands-on approach to implementation using H2O that'll enable you to set up your ML systems in no time. By the end of this H2O book, you'll be able to train and use your ML models using H2O AutoML, right from experimentation all the way to production without a single need to understand complex statistics or data science. What you will learnGet to grips with H2O AutoML and learn how to use itExplore the H2O Flow Web UIUnderstand how H2O AutoML trains the best models and automates hyperparameter optimizationFind out how H2O Explainability helps understand model performanceExplore H2O integration with scikit-learn, the Spring Framework, and Apache StormDiscover how to use H2O with Spark using H2O Sparkling WaterWho this book is for This book is for engineers and data scientists who want to quickly adopt machine learning into their products without worrying about the internal intricacies of training ML models. If you're someone who wants to incorporate machine learning into your software system but don't know where to start or don't have much expertise in the domain of ML, then you'll find this book useful. Basic knowledge of statistics and programming is beneficial. Some understanding of ML and Python will be helpful.

Download or read book MOJO Programming for LLM Developers written by Edward R DeForest and published by Independently Published. This book was released on 2024-04-05 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master Large Language Models with MOJO Programming: Build Powerful AI for the Future About the Technology: Large Language Models (LLMs) are revolutionizing the way we interact with machines. These AI models, trained on massive datasets of text and code, can generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way. MOJO is a powerful programming framework designed to streamline LLM development, making it easier for you to bring next-generation AI applications to life. Worries Holding You Back? LLM Development Seems Complex: You're not alone! Traditional LLM development can involve complex infrastructure setup, lengthy training times, and challenges in deployment. MOJO simplifies the process with its efficient tools and modular design. Where Do I Start?: This comprehensive guide provides a step-by-step approach to LLM development with MOJO. You'll gain a solid foundation in the fundamentals and explore practical techniques for building chatbots, virtual assistants, and other innovative AI applications. Challenges You'll Conquer: Efficient Development: MOJO empowers you to overcome hurdles like complex model training and slow deployment. This book equips you with the knowledge and tools to streamline the development process and bring your AI ideas to life faster. Staying Ahead of the Curve: The LLM landscape is constantly evolving. This guide explores emerging trends like lifelong learning LLMs and multimodal learning, ensuring you have the skills to stay at the forefront of this exciting field. What to Expect: Master MOJO: Gain a thorough understanding of MOJO's functionalities and how to leverage them for efficient LLM development. Build Powerful Applications: Explore practical techniques for creating chatbots, virtual assistants, and other AI applications that can transform your industry. Join the Thriving Community: Learn how to tap into the wealth of resources and support offered by the MOJO developer community. Ready to Build the Future of AI? This book is your ultimate companion for mastering MOJO programming and becoming a skilled LLM developer. Get started today and unlock the potential of next-generati

Download or read book AI and Machine Learning for Coders written by Laurence Moroney and published by O'Reilly Media. This book was released on 2020-10-01 with total page 393 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you're looking to make a career move from programmer to AI specialist, this is the ideal place to start. Based on Laurence Moroney's extremely successful AI courses, this introductory book provides a hands-on, code-first approach to help you build confidence while you learn key topics. You'll understand how to implement the most common scenarios in machine learning, such as computer vision, natural language processing (NLP), and sequence modeling for web, mobile, cloud, and embedded runtimes. Most books on machine learning begin with a daunting amount of advanced math. This guide is built on practical lessons that let you work directly with the code. You'll learn: How to build models with TensorFlow using skills that employers desire The basics of machine learning by working with code samples How to implement computer vision, including feature detection in images How to use NLP to tokenize and sequence words and sentences Methods for embedding models in Android and iOS How to serve models over the web and in the cloud with TensorFlow Serving

Download or read book A Greater Foundation for Machine Learning Engineering written by Dr. Ganapathi Pulipaka and published by Xlibris Corporation. This book was released on 2021-10-01 with total page 382 pages. Available in PDF, EPUB and Kindle. Book excerpt: This research scholarly illustrated book has more than 250 illustrations. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics. The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in the US, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, machine learning and deep learning engineers at scale for high-performance computing environments. A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging the JupyterLab environment. Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning. Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. The book's mission is to help build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods. The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow's installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platforms, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms. This book is a marvel that does not leave any application of machine learning and deep learning algorithms. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners. It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.

Download or read book Machine Learning with R written by Dominic Lordy and published by Createspace Independent Publishing Platform. This book was released on 2016-05-27 with total page 114 pages. Available in PDF, EPUB and Kindle. Book excerpt: ***** BUY NOW (Will soon return to 25.59) ******Free eBook for customers who purchase the print book from Amazon****** Are you thinking of learning more about Machine Learning using R? If you are looking for a complete beginners guide to learn Machine Learning using R, in just a few hours, this book is for you. Machine Learning is the practice of transforming data into knowledge, and R is the most popular open-source programming language used for Machine Learning. In this book, we will learn how to use the principles of Machine Learning and the R programming language to answer day-to-day questions about your data. Finally, we'll learn how to make predictions with machine learning. From AI Sciences Publisher Our books may be the best one for beginners; it's a step-by-step guide for any person who wants to start learning Artificial Intelligence and Data Science from scratch. It will help you in preparing a solid foundation and learn any other high-level courses. To get the most out of the concepts that would be covered, readers are advised to adopt hands on approach, which would lead to better mental representations. Several Visual Illustrations and Examples Instead of tough math formulas, this book contains several graphs and images which detail all important R and Machine Learning concepts and their applications. Target Users The book designed for a variety of target audiences. The most suitable users would include: Beginners who want to approach Machine Learning, but are too afraid of complex math to start Newbies in computer science techniques and machine learning Professionals in Machine Learning and social sciences Professors, lecturers or tutors who are looking to find better ways to explain the content to their students in the simplest and easiest way Students and academicians, especially those focusing on Machine Learning What's Inside This Book? Introduction Basic Functions Linear Regression Machine Learning Algorithms Data with R Generating data Graphical functions Programming with R in Practice Opening the Black Box K-nearest Neighbors Neural Networks Trees and Forests Standard Linear Model Logistic Regression Support Vector Machine using R Frequently Asked Questions Help! I got an error, what did I do wrong? Useful References Frequently Asked Questions Q: Is this book for me and do I need programming experience? A: f you want to smash Machine Learning from scratch, this book is for you. Little programming experience is required. If you already wrote a few lines of code and recognize basic programming statements, you'll be OK. Q: Can I loan this book to friends? A: Yes. Under Amazon's Kindle Book Lending program, you can lend this book to friends and family for a duration of 14 days. Q: Does this book include everything I need to become a Machine Learning expert? A: Unfortunately, no. This book is designed for readers taking their first steps in Machine Learning and further learning will be required beyond this book to master all aspects of Machine Learning. Q: Can I have a refund if this book is not fitted for me? A: Yes, Amazon refund you if you aren't satisfied, for more information about the amazon refund service please go to the amazon help platform. We will also be happy to help you if you send us an email at [email protected]. If you need to see the quality of our job, AI Sciences Company offering you a free eBook in Machine Learning with Python written by the data scientist Alain Kaufmann at https: //aisciences.lpages.co/ai-sciences-data-science-with-r/

Download or read book An Introduction to Machine Learning written by Gopinath Rebala and published by Springer. This book was released on 2019-05-07 with total page 263 pages. Available in PDF, EPUB and Kindle. Book excerpt: Just like electricity, Machine Learning will revolutionize our life in many ways – some of which are not even conceivable today. This book provides a thorough conceptual understanding of Machine Learning techniques and algorithms. Many of the mathematical concepts are explained in an intuitive manner. The book starts with an overview of machine learning and the underlying Mathematical and Statistical concepts before moving onto machine learning topics. It gradually builds up the depth, covering many of the present day machine learning algorithms, ending in Deep Learning and Reinforcement Learning algorithms. The book also covers some of the popular Machine Learning applications. The material in this book is agnostic to any specific programming language or hardware so that readers can try these concepts on whichever platforms they are already familiar with. Offers a comprehensive introduction to Machine Learning, while not assuming any prior knowledge of the topic; Provides a complete overview of available techniques and algorithms in conceptual terms, covering various application domains of machine learning; Not tied to any specific software language or hardware implementation.

Download or read book Machine Learning written by Ethem Mining and published by . This book was released on 2020-10-25 with total page 496 pages. Available in PDF, EPUB and Kindle. Book excerpt: If you are looking for a comprehensive guide that explains in a simple way how to manage machine learning and AI, please keep reading. What do you need to learn to move from being a complete beginner to someone with advanced knowledge of machine learning? Have you ever wondered how to leverage big data from big tech companies (Google, Facebook e Amazon) to reach your objectives? Do you want to understand which ones are the best libraries to use and why is Python considered the best language for machine learning? The term Machine Learning refers to the capability of a machine to learn something without any pre existing program. Automatic learning is a way to educate an algorithm to learn from various environmental situations. Machine learning involves the usage of enormous quantities of data and an efficient algorithm enabled to adapt and enhance its capabilities according to recurring situations. From banking operations to online shopping and also on social media, we daily use machine learning data algorithms to make our experience more efficient, simple and secure. Machine learning and its capabilities are rapidly becoming popular - we have just discovered part of its potential. This bundle will give you all the information you need in order to leverage your knowledge and give you an excellent level of education. All the subjects will be supported by examples and practical exercises that will enable you to reinforce your level of knowledge Specifically you will learn What does Machine Learning and Artificial Intelligence mean Machine Learning evolution Machine learning applications Difference between AI and Machine Learning Big Data Connection between Machine Learning and Big Data How to use Big Data from large size companies to make your business scalable How to acquire new customers via simple marketing strategies Python Programming Advanced programming techniques and much more. This manual has been written to meet all levels of education. If your level of knowledge is low and you don't have any previous experience, this book will empower you to learn key functionalities and navigate through various subjects smoothly. If you have already a good understanding, you will find useful insights that will help to enhance your competences. If you want to learn Machine Learning but don't know where to start... Buy Now to get started!

Download or read book Machine Learning written by Thomas Farth and published by . This book was released on 2018-11-14 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning: Beginner to Intermediate's Guide in Python/R/Scala Are you thinking of learning more about Machine Learning? Want to learn Machine Learning Algoirthms? Do you want to explore Python/R/Scala libraries for Machine Learning? Then You are at right place A Bundle of Two Awesome Books for Machine Learning Lovers This bundle and the accompanying examples, you would be well suited to tackle problems which enhance your interests using machine learning. The title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into introducing mathematical concepts, specific algorithms, introduction to programming languages, best programming languages for Machine Learning and libraries of Python applied in Machine Learning. It would seek to explain common terms and algorithms in an intuitive way. The authors used a progressive approach whereby we start out slowly and improve on the complexity of our solutions.This bundle and the accompanying examples, you would be well suited to tackle problems which enhance your interests using machine learning. The title opens with a general introduction to machine learning from a macro level. The second half of the book is more practical and dives into Data Processing, Regression, Classification, Clustering, Natural Language Processing and Deep Learning. Just a few more benefits this bundle will provide: Introduction to Programming Language Tools for Machine Learning Introduction to MLlib (Apache Spark) Libraries of Python Master Machine Learning on Python & R. Have a Great intuition of many Machine Learning models Regression & Classification Handle specific topics like NLP, Clustering & Deep Learning Download your copy now so you can get started on what is promising to be a most amazing future. Copyright:© 2018 by Thomas Farth, All rights reserved.

Download or read book Programming Machine Learning written by Paolo Perrotta and published by Pragmatic Bookshelf. This book was released on 2020-03-31 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool. But where to start? It's easy to be intimidated, even as a software developer. The good news is that it doesn't have to be that hard. Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system. Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty. Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning. Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work. Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on. Iterate on your design, and add layers of complexity as you go. Build an image recognition application from scratch with supervised learning. Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning. Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets. Train and refine those networks with backpropagation and batching. Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system. Start from the beginning and code your way to machine learning mastery. What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly. Apart from that, you'll only need your computer, and your code-adept brain.

Download or read book Hands On Machine Learning with scikit learn and Scientific Python Toolkits written by Tarek Amr and published by Packt Publishing Ltd. This book was released on 2020-07-24 with total page 368 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrate scikit-learn with various tools such as NumPy, pandas, imbalanced-learn, and scikit-surprise and use it to solve real-world machine learning problems Key FeaturesDelve into machine learning with this comprehensive guide to scikit-learn and scientific PythonMaster the art of data-driven problem-solving with hands-on examplesFoster your theoretical and practical knowledge of supervised and unsupervised machine learning algorithmsBook Description Machine learning is applied everywhere, from business to research and academia, while scikit-learn is a versatile library that is popular among machine learning practitioners. This book serves as a practical guide for anyone looking to provide hands-on machine learning solutions with scikit-learn and Python toolkits. The book begins with an explanation of machine learning concepts and fundamentals, and strikes a balance between theoretical concepts and their applications. Each chapter covers a different set of algorithms, and shows you how to use them to solve real-life problems. You’ll also learn about various key supervised and unsupervised machine learning algorithms using practical examples. Whether it is an instance-based learning algorithm, Bayesian estimation, a deep neural network, a tree-based ensemble, or a recommendation system, you’ll gain a thorough understanding of its theory and learn when to apply it. As you advance, you’ll learn how to deal with unlabeled data and when to use different clustering and anomaly detection algorithms. By the end of this machine learning book, you’ll have learned how to take a data-driven approach to provide end-to-end machine learning solutions. You’ll also have discovered how to formulate the problem at hand, prepare required data, and evaluate and deploy models in production. What you will learnUnderstand when to use supervised, unsupervised, or reinforcement learning algorithmsFind out how to collect and prepare your data for machine learning tasksTackle imbalanced data and optimize your algorithm for a bias or variance tradeoffApply supervised and unsupervised algorithms to overcome various machine learning challengesEmploy best practices for tuning your algorithm’s hyper parametersDiscover how to use neural networks for classification and regressionBuild, evaluate, and deploy your machine learning solutions to productionWho this book is for This book is for data scientists, machine learning practitioners, and anyone who wants to learn how machine learning algorithms work and to build different machine learning models using the Python ecosystem. The book will help you take your knowledge of machine learning to the next level by grasping its ins and outs and tailoring it to your needs. Working knowledge of Python and a basic understanding of underlying mathematical and statistical concepts is required.