Machine Learning 101
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Machine Learning 101
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Author : Moss Adelle Louise
language : en
Publisher: Moss Adelle Louise
Release Date : 2024-03-19
Machine Learning 101 written by Moss Adelle Louise and has been published by Moss Adelle Louise this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-03-19 with Computers categories.
Introducing "Machine Learning 101: An Easy-to-Follow Beginner's Tutorial" Have you ever wondered how Google can predict what you're searching for as you type? Or how social media platforms suggest friends for you to connect with? The answer lies in machine learning, a fascinating field that has taken numerous industries by storm. If you've been itching to learn more about this revolutionary technology but feel intimidated by the complex jargon and overwhelming concepts, fear not! "Machine Learning 101: An Easy-to-Follow Beginner's Tutorial" is here to guide you on your transformative journey. Written with clarity and simplicity, this comprehensive book aims to provide an effortless introduction to machine learning concepts, techniques, and applications for beginners. Whether you have a background in programming or are entirely new to the world of data science, this tutorial will equip you with a solid foundation to comprehend, utilize, and appreciate the power of machine learning algorithms. Inside "Machine Learning 101," you'll embark on an enlightening adventure as we peel back the layers of this groundbreaking technology. In each chapter, we dive deep into fundamental concepts, illustrating them with relatable examples and intuitive explanations. We'll cover crucial topics such as supervised and unsupervised learning, decision trees, neural networks, and more, all in a pragmatic and concise manner. Building on that foundation, we then explore real-life applications of machine learning across various industries. From healthcare and finance to marketing and transportation, we peel away the mystery surrounding how these algorithms are transforming the way we work and live. You'll discover the immense potential of machine learning to revolutionize image recognition, speech synthesis, fraud detection, and countless other fields. By the end, you'll understand how machine learning's wide-ranging impact is reshaping our future. What sets "Machine Learning 101" apart is its commitment to fostering hands-on learning. As you journey through the book, you'll find numerous coding examples and exercises that allow you to implement machine learning algorithms yourself. Don't worry if you're new to coding; we provide gentle introductions to popular programming languages like Python and R, empowering you to practice and build confidence in your skills. The simplicity of our writing style ensures that even the most complex concepts are approachable. We've stripped away the unnecessarily technical jargon that often intimidates beginners, replacing it with a conversational tone that anyone can comprehend. Rather than overwhelming you with mathematical formulas, we focus on delivering intuitive explanations and easy-to-grasp visuals, making machine learning accessible to all knowledge levels. In addition, "Machine Learning 101" includes strategically placed callouts and summaries, providing quick reference points throughout your learning journey. Whether you need a refresher on an algorithm or a reminder of key concepts, these features ensure that you can progress smoothly through the book and confidently absorb the information as you go.
Machine Learning 101
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Author : William Owen Ph D
language : en
Publisher:
Release Date : 2021-05-18
Machine Learning 101 written by William Owen Ph D and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-18 with categories.
Althоugh machine lеаrnіng іѕ a fіеld within соmрutеr ѕсіеnсе, it dіffеrѕ frоm traditional соmрutаtіоnаl approaches. In traditional соmрutіng, algorithms аrе ѕеtѕ оf еxрlісіtlу programmed instructions used by computers tо саlсulаtе оr рrоblеm ѕоlvе Mасhіnе lеаrnіng іѕ a ѕubfіеld оf artificial intelligence (AI). The goal оf machine learning generally іѕ tо understand thе ѕtruсturе оf data аnd fit thаt dаtа іntо mоdеlѕ thаt саn be understood аnd utіlіzеd by реорlе.
Machine Learning 101
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Author : GILBERT. GUTIERREZ
language : en
Publisher: Independently Published
Release Date : 2025-02-04
Machine Learning 101 written by GILBERT. GUTIERREZ and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-04 with Computers categories.
(Book 4 of AI from Scratch: Step-by-Step Guide to Mastering Artificial Intelligence) Unlock the Power of Machine Learning-From Absolute Beginner to ML Expert Are you eager to understand Machine Learning (ML) but unsure where to start? Do terms like neural networks, supervised learning, and model optimization seem intimidating? Machine Learning 101: From Zero to Hero is the ultimate beginner-friendly guide designed to take you from a complete novice to a confident ML practitioner-step by step. This book is the fourth installment in the AI from Scratch series, offering an easy-to-follow, hands-on approach to mastering machine learning concepts and applications. Whether you're a student, developer, entrepreneur, or AI enthusiast, this book provides real-world examples, practical exercises, and Python-based coding tutorials to help you grasp machine learning fundamentals and apply them effectively. What You Will Learn Machine Learning Fundamentals What is Machine Learning? How does it work? The difference between supervised, unsupervised, and reinforcement learning Essential mathematical concepts (Linear Algebra, Probability, Statistics) How to collect, clean, and preprocess data for ML models Supervised & Unsupervised Learning Building Regression Models (Linear, Polynomial, Ridge, Lasso) Mastering Classification Algorithms (Decision Trees, Logistic Regression, SVMs) Clustering techniques like K-Means, DBSCAN, and Hierarchical Clustering Reducing data complexity with PCA, t-SNE, and Autoencoders Deep Learning & Advanced ML Understanding Artificial Neural Networks (ANNs) and Deep Learning Implementing CNNs for Image Processing and RNNs for Time-Series Data Optimizing models with Hyperparameter Tuning, Cross-Validation, and Regularization Hands-on coding with Scikit-learn, TensorFlow, and PyTorch Real-World Applications & Career Guidance Machine Learning in Healthcare, Finance, and E-Commerce Ethical considerations & avoiding bias in AI models The future of ML: AutoML, Federated Learning, and Quantum AI Career roadmap: ML certifications, job roles, and portfolio building Who Is This Book For? Beginners & Students - No prior ML knowledge required! Learn step by step. Software Developers & Engineers - Transition into AI with hands-on coding exercises. Entrepreneurs & Business Leaders - Discover how ML can drive business success. AI Enthusiasts & Researchers - Gain a structured foundation to advance your AI journey. Why Read This Book? Step-by-step approach - Learn ML concepts in a structured and beginner-friendly manner Hands-on coding exercises - Build real ML models using Python, TensorFlow, and Scikit-learn Industry case studies - See how ML is applied in finance, healthcare, and tech Actionable learning - Work on practical projects and develop an ML portfolio Up-to-date content - Covers modern ML trends like AutoML and Quantum AI Your Journey from Zero to Hero Starts Here! Machine Learning is transforming industries, and the demand for ML skills is higher than ever. Whether you're looking to start a career in AI, optimize business processes, or simply expand your technical knowledge, Machine Learning 101: From Zero to Hero will guide you every step of the way. Start your ML journey today-unlock the potential of AI and become an ML hero!
Machine Learning 101 With Scikit Learn And Statsmodels
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Author : 365 Careers
language : en
Publisher:
Release Date : 2019
Machine Learning 101 With Scikit Learn And Statsmodels written by 365 Careers and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
New to machine learning? This is the place to start: Linear regression, Logistic regression, and Cluster Analysis About This Video Learn machine learning with StatsModels and sklearn Apply machine learning skills to solve real-world business cases Get started with linear regression, logistic regression, and cluster analysis In Detail Machine Learning is one of the fundamental skills you need to become a data scientist. It's the steppingstone that will help you understand deep learning and modern data analysis techniques. In this course, you'll explore the three fundamental machine learning topics - linear regression, logistic regression, and cluster analysis. Even neural networks geeks (like us) can't help but admit that it's these three simple methods that data science revolves around. So, in this course, we will make the otherwise complex subject matter easy to understand and apply in practice. This course supports statistics theory with practical application of these quantitative methods in Python to help you develop skills in the context of data science. We've developed this course with not one but two machine learning libraries: StatsModels and sklearn. You'll be eager to complete this course and get ready to become a successful data scientist! Downloading the example code for this course: You can download the example code files for this course on GitHub at the following link: https://github.com/PacktPublishing/Machine-Learning-101-with-Scikit-learn-and-StatsModels . If you require support please email: [email protected].
Deep Learning 101
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Author : Scott Derek
language : en
Publisher:
Release Date : 2021-04-16
Deep Learning 101 written by Scott Derek and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-16 with categories.
Deep learning is one of today's hottest fields. This approach to machine learning is achieving breakthrough results in some of today's highest profile applications, in organizations ranging from Google to Tesla, Facebook to Apple. Thousands of technical professionals and students want to start leveraging its power, but previous books on deep learning have often been non-intuitive, inaccessible, and dry. In Deep Learning 101 Illustrated, Scott Derek the instructors and practitioner present a uniquely visual, intuitive, and accessible high-level introduction to the techniques and applications of deep learning. Packed with vibrant, full-color illustrations, it abstracts away much of the complexity of building deep learning models, making the field more fun to learn, and accessible to a far wider audience.Deep learning is rapidly becoming the most preferred way of solving data problems. This is thanks, in part, to its huge variety of mathematical algorithms and their ability to find patterns that are otherwise invisible to us.Who this book is forDeep Learning 101 is designed for data scientists, data analysts, and developers who want to use deep learning techniques to develop efficient solutions. This book is ideal for those who want a deeper understanding as well as an overview of the technologies.
Data Science 101
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Author : Andrew Park
language : en
Publisher: Andrew Park
Release Date : 2021-02-13
Data Science 101 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 2021-02-13 with categories.
★ 55% OFF for Bookstores! NOW at $ 21,97 instead of $31.97! LAST DAYS! ★ Your Customers Will Never Stop To Use This Amazing Guide! Do you want to know how Data science helps in business? This book will discuss everything that we need to know when it comes to data science and how to complete the process of data science with Python. There are so many different parts that come together when we work on data science, but if you are able to put it all together, and work to really analyze the information that you have to beat out the competition, you will find that data science with Python can be the right move for you. We will explore how so many businesses will take the time to gather up information, usually from a variety of sources, and then will be unsure of what they should do with that information once they have collected it. We can then take a look at the data life cycle and how we can take that information, clean it off, analyze it, and come up with insights and predictions that help grow our business more than ever before. We will spend this time looking what Python is about, how to download the program on your chosen operating system, and some of the basics that come with coding in Python. This guidebook went through all of the steps that you need to know in order to get started with data science and some of the basic parts of the Python code. We can then put all of this together in order to create the right analytical algorithm that, once it is trained properly and tested with the right kinds of data, will work to make predictions, provide information, and even show us insights that were never possible before. And all that you need to do to get this information is to use the steps that we outline and discuss in this guidebook. There is a lot of buzz in the business world, no matter what industry it is, about machine learning, the Python language, and of course, data science, and being able to put these terms together and learn how they work can make a big difference in how well your business will do now and in the future. There are already a ton of companies out there who have been able to gain a competitive edge with data science and the various models and algorithms of Python that go with it, and you can as well. This book covers: What is Data Science? The Python Coding Language Some of the Basic Coding in Python The Best Python Libraries to Use with Data Science The Basics of Jupyter and Why We Should Use It Working with Anaconda in Python The Basics of the Pandas Library What is WinPython and How Can We Use It? Common Tasks to Do in Info Science Different Data Types to Work With The Future of Data Science and Where It Will Go from Here There are so many great ways that you can use the data you have been collecting for some time now and being able to complete the process of data visualization will ensure that you get it all done. When you are ready to get started with Python data science, make sure to check out this guidebook to learn how. There is so much that can come into play when we work with data science, and it is one of the best ways for a business to differentiate from the competition and actually see some results in the process. And the Python language is a great option to learn to help us analyze and create a model that works with the info that we have. When we are ready to learn more about data science, and how to use the Python coding language to go with it, make sure to check out this guidebook to help you get started. Buy it NOW and let your customers get addicted to this amazing book!
Mastering Artificial Intelligence For Beginner S 101
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Author : Azai Hung
language : en
Publisher: Independently Published
Release Date : 2024-07-21
Mastering Artificial Intelligence For Beginner S 101 written by Azai Hung and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-21 with Computers categories.
Mastering Artificial Intelligence Discover the secrets of AI and transform your career with this comprehensive step-by-step guide. Learn the fundamentals of Machine Learning, Deep Learning, and Neural Networks from scratch. Perfect for beginners, this tutorial takes you by the hand and walks you through the basics of AI, from understanding the concepts to building your own intelligent systems. With this tutorial, you'll learn: - The basics of Artificial Intelligence and its applications - How to build intelligent systems using Machine Learning and Deep Learning - The fundamentals of Neural Networks and how to implement them - How to get started with AI and start building your own projects Don't miss out on this opportunity to unlock the power of AI and transform your career. Start learning today and become an AI expert in no time!
Machine Learning Ecml
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Author :
language : en
Publisher:
Release Date : 2003
Machine Learning Ecml written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003 with Induction (Logic) categories.
Machine Learning
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Author : Ivan Bratko
language : en
Publisher: Morgan Kaufmann
Release Date : 1999
Machine Learning written by Ivan Bratko and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999 with Computers categories.
The Sixteenth International Conference on Machine Learning (ICML-99) was held June 27-30, 1999 in Bled, Slovenia. It was co-located with the Ninth International Workshop on Inductive Logic Programming (ILP-99). These are the papers from this conference covering topics on empirical, theoretical, and cognitive-modelling research in all areas of machine learning.
Machine Learning
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Author : D. Sleeman
language : en
Publisher: Morgan Kaufmann
Release Date : 1992
Machine Learning written by D. Sleeman and has been published by Morgan Kaufmann this book supported file pdf, txt, epub, kindle and other format this book has been release on 1992 with Computers categories.
Machine Learning Proceedings 1992.