Applied Machine Learning: A practical guide from Novice to ProКНИГИ » ПРОГРАММИНГ
Название: Applied Machine Learning: A practical guide from Novice to Pro Автор: Chigozie Anyasor Издательство: Independently published Год: 2024 Страниц: 331 Язык: английский Формат: epub Размер: 20.8 MB
Many believe that a Machine Learning model, once trained, can act autonomously. This misconception has hindered innovation in ML/AI for far too long. In reality, ML models require integration within a comprehensive system encompassing inputs, processing, and outputs.
My new book, "Applied Machine Learning: A Practical Guide from Novice to Pro," is designed to reshape your understanding of Machine Learning and its practical applications. This hands-on guide will help you comprehend what Machine Learning truly is and how it can transform your business operations.
Machine Learning is an immensely intriguing field that involves harnessing computational algorithms to glean insights and make predictions from data without explicit programming. It's a subset of artificial intelligence focused on creating systems capable of learning and improving autonomously from experience. To wield machine learning effectively, data scientists, or aspiring ones, must grasp its principles and inner workings thoroughly.
At its core, Machine Learning entails feeding an algorithm or model with pertinent data to tackle a business problem, allowing it to learn and improve from patterns and relationships within the data. As more data is ingested into the algorithm or model, it can leverage its accrued knowledge (the training data) to predict new data points. Consequently, machine learning empowers businesses to make informed decisions and predictions grounded in observed patterns.
While predictions play a pivotal role in leveraging Machine Learning for informed decision-making, it's crucial to realize that simply possessing a prediction model isn't enough. What truly matters is how you act upon those predictions. Businesses must take ownership of a model's outcomes and grasp its limitations. This underscores the pivotal role of a data scientist or chief data officer in comprehending a model's capabilities to dispel misconceptions and effectively leverage Machine Learning to address business challenges.
This book serves as a comprehensive guide to various Machine Learning techniques widely employed in data science and analytics. It traverses the entire machine learning pipeline, commencing from data preprocessing and c ulminating in model selection. The text delves into diverse Regression models, including Simple, Multiple, Polynomial, Support vector, Decision tree, and Random forest regression models, alongside evaluating these models and selecting the optimal regression model for a given task.
Furthermore, it explores Classification techniques such as Logistic Regression, K-nearest Neighbor, Support vector machines, Naive Bayes, Decision trees, and Random forest classification, providing a nuanced understanding of evaluating classification models and discerning the most suitable one for a specific objective.
Additionally, the book delves into Clustering methods like K-means. Finally, it covers how to operationalize your Machine Learning models. Altogether, this book serves as an indispensable resource for anyone striving to master Machine Learning techniques and apply them in real-world scenarios.
What You'll Learn:
Foundation in Statistics: Understand why statistics is crucial in ML and how to apply it effectively. Model Exploration: Dive into both supervised and unsupervised models, learning the nuances and applications of each. Practical Integration: Learn how to integrate ML models into broader systems, ensuring they work seamlessly within your business infrastructure. Model Selection: Gain insights into selecting the best model based on specific parameters and the problem you're aiming to solve. Operationalization: Discover how to operationalize your ML models, turning them into reliable components of your business processes. Hosting and Deployment: Learn the steps to host and deploy your ML models, making them accessible and usable within your organization's environment. Real-World Applications: Discover real-world examples and case studies that illustrate how ML can drive innovation and efficiency in various business scenarios.
Why This Book?
From Novice to Pro: Whether you’re just starting or looking to deepen your expertise, this book is structured to take you from basic concepts to advanced applications. Hands-On Approach: Practical exercises and examples throughout the book will reinforce your learning and provide you with the skills to apply ML in real-world situations. Business Focus: Learn how to leverage ML to make data-driven decisions, optimize operations, and create competitive advantages for your business. Future-Proof Your Skills: Stay ahead in the rapidly evolving field of ML/AI by equipping yourself with the latest knowledge and techniques.
By reading this book, you’ll be equipped to transition from a novice to a pro, with a solid understanding of Machine Learning and the ability to harness its power for innovative solutions. Don’t let outdated notions hold you back. Unlock your potential and propel your business to the next level with applied Machine Learning.
"Applied Machine Learning" delves into the practical applications of Machine Learning, offering a roadmap for individuals and organizations to leverage its potential. Tailored for both newcomers and seasoned practitioners, this book covers foundational principles alongside real-world scenarios.
Whether you're a student delving into the world of Machine Learning, a professional seeking to enhance your skills, or an industry leader navigating the integration of Machine Learning within your organization, this book serves as a compass to unlock the transformative power of applied Machine Learning. Let the journey commence!
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Applied Machine Learning: A practical guide from Novice to Pro 
