Scikit Learn Essentials

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Scikit Learn Essentials

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Author : Dhiraj Kumar
language : en
Publisher:
Release Date : 2019

Scikit Learn Essentials written by Dhiraj Kumar 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.

"Master scikit-learn through a combination of lecture and hands-on (via Jupyter) in this eight-part video series: Scikit-learn Overview; Installing Scikit-learn; Loading Data Sets using Scikit-learn; Pre-processing Data using Scikit-learn; Splitting Data into Train Sets and Test Sets in Scikit-learn; Linear Regression using Scikit-learn; Naïve Bayes using Scikit-learn; SVM using Scikit-learn."--Resource description page.

Machine Learning Essentials And Applications

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Author : Mrs. N. Jayasri
language : en
Publisher: RK Publication
Release Date : 2024-07-27

Machine Learning Essentials And Applications written by Mrs. N. Jayasri and has been published by RK Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-07-27 with Computers categories.

Machine Learning Essentials and Applications a comprehensive of machine learning's core principles, methodologies, and real-world applications. This book is designed for both beginners and professionals, covering essential topics like supervised and unsupervised learning, neural networks, and deep learning. With clear explanations and practical examples, it connects theory to practice, showcasing machine learning’s impact across industries such as healthcare, finance, and technology. Ideal for readers seeking foundational knowledge and insights into the transformative potential of machine learning in various fields.

Machine Learning Essentials

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Author : Barrett Williams
language : en
Publisher: Barrett Williams
Release Date : 2024-12-01

Machine Learning Essentials written by Barrett Williams and has been published by Barrett Williams this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-01 with Computers categories.

Unlock the potential of data and step into the future with "Machine Learning Essentials," the ultimate guide for mastering predictive analytics. Whether you're a newcomer or looking to deepen your understanding, this comprehensive eBook is designed to equip you with the tools and knowledge you need to excel in the dynamic field of machine learning. Begin your journey by exploring the foundational principles of machine learning and its transformative impact on predictive analytics. Learn how to expertly prepare and engineer your data, selecting and extracting the features that matter most. Dive into handling imbalanced data with precision, ensuring your models are accurate and robust. Discover the power of classification algorithms with insights into decision trees, random forests, support vector machines, and logistic regression. Transition smoothly into regression techniques, harnessing the potential of linear, polynomial, and regularization methods. Explore the realm of unsupervised learning to unveil predictive insights using clustering methods, dimensionality reduction techniques, and anomaly detection. Evaluate model performance like a pro with cross-validation strategies, confusion matrices, and ROC/AUC metrics. Venture into neural networks, unlocking the basics of their architecture, activation functions, and training methodologies. Delve into advanced deep learning topics with convolutional, recurrent, and generative adversarial networks. Optimize your models through hyperparameter tuning and feature importance analysis, selecting the most effective techniques for your goals. Gain practical business insights by implementing machine learning in marketing analytics, risk assessment, and fraud detection. Familiarize yourself with essential tools and libraries like Python, Scikit-Learn, TensorFlow, and PyTorch. Learn from real-world case studies in retail, healthcare, and finance, and tackle ethical considerations in algorithmic bias and data security. Prepare for the future with insights into automated machine learning, IoT, and evolving AI technologies. Take practical steps to launch your machine learning journey, setting up your environment and connecting with a vibrant community of practitioners. "Machine Learning Essentials" is your all-in-one resource for gaining actionable expertise and driving innovation in today's data-driven world. Start your learning adventure today and transform your career with this indispensable guide.

Artificial Intelligence And Machine Learning Essentials

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Author : Kiran Kumar Pappula
language : en
Publisher: Academic Guru Publishing House
Release Date : 2025-02-06

Artificial Intelligence And Machine Learning Essentials written by Kiran Kumar Pappula and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-06 with Study Aids categories.

Artificial Intelligence and Machine Learning Essentials is a comprehensive guide tailored for beginners and early-stage learners eager to explore the fascinating world of Al and ML. The book covers key concepts, techniques, and tools across eight well-structured chapters, offering readers a clear pathway from fundamental understanding to practical knowledge. Beginning with the basics of Artificial Intelligence, the book introduces readers to its history, types, and applications across different industries. It then delves into the core principles of Machine Learning, detailing the various types, algorithms, and workflows essential for building intelligent systems. Readers will gain insights into critical data preprocessing techniques that ensure high-quality input for ML models. The book further explores popular supervised and unsupervised learning algorithms, including linear regression, decision trees, K-means, and PCA, making it easier to grasp both the theoretical and practical aspects. Reinforcement Learning, Deep Learning models like CNNs and RNNs, and Natural Language Processing techniques are also thoroughly explained with real-life relevance. Written in simple and accessible language, the book makes complex topics easy to understand, making it suitable for university students, tech enthusiasts, and professionals from non-technical backgrounds. With a strong emphasison clarity and practical understanding, this book serves as a stepping stone into one of the most promising areas of modern technology.

Machine Learning Essentials And It S Application

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Author : Prof. Yogendra Kumar
language : en
Publisher: Academic Guru Publishing House
Release Date : 2024-08-05

Machine Learning Essentials And It S Application written by Prof. Yogendra Kumar and has been published by Academic Guru Publishing House this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-05 with Study Aids categories.

The book "Machine Learning Essentials and Its Applications" is an informative investigation of the basic concepts of machine learning as well as the many applications of this fascinating field. The fundamental ideas, methods, and algorithms that provide the foundation of machine learning are presented in this book in a format that is designed to lead readers through the process. In order to ensure that the reader has a complete grasp of the discipline, it covers a broad variety of topics, such as supervised and unsupervised learning, neural networks, natural language processing, and computer vision. In addition to providing theoretical information, the book has an emphasis on practical applications, demonstrating how machine learning can be used in a variety of fields, including healthcare, finance, transportation, and entertainment, among others. Every chapter contains case studies and hands-on activities to help readers get a more in-depth grasp of the subject matter and to motivate them to apply what they have learnt in the classroom to situations that they will encounter in the real world. The purpose of this book is to serve as a vital resource for everyone who is interested in understanding the transformational potential of machine learning. It was designed for students, instructors, and industry experts. The book "Machine Learning Essentials and Its Applications" is a necessary travel companion on iv the path to becoming an expert in this rapidly evolving topic since it provides lucid explanations, examples that illustrate the concepts, and important insights.

Artificial Intelligence And Machine Learning A Precise Book To Learn Basics

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Author : pc
language : en
Publisher: by Mocktime Publication
Release Date :

Artificial Intelligence And Machine Learning A Precise Book To Learn Basics written by pc and has been published by by Mocktime Publication this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.

Artificial Intelligence and Machine Learning - A Precise Book to Learn Basics Table of Contents 1. Introduction to Artificial Intelligence and Machine Learning 1.1 What is Artificial Intelligence? 1.2 The Evolution of Artificial Intelligence 1.3 What is Machine Learning? 1.4 How Machine Learning Differs from Traditional Programming 1.5 The Importance of Artificial Intelligence and Machine Learning 2. Foundations of Machine Learning 2.1 Supervised Learning 2.1.1 Linear Regression 2.1.2 Logistic Regression 2.1.3 Decision Trees 2.2 Unsupervised Learning 2.2.1 Clustering 2.2.2 Dimensionality Reduction 2.3 Reinforcement Learning 2.3.1 Markov Decision Process 2.3.2 Q-Learning 3. Neural Networks and Deep Learning 3.1 Introduction to Neural Networks 3.2 Artificial Neural Networks 3.2.1 The Perceptron 3.2.2 Multi-Layer Perceptron 3.3 Convolutional Neural Networks 3.4 Recurrent Neural Networks 3.5 Generative Adversarial Networks 4. Natural Language Processing 4.1 Introduction to Natural Language Processing 4.2 Preprocessing and Text Representation 4.3 Sentiment Analysis 4.4 Named Entity Recognition 4.5 Text Summarization 5. Computer Vision 5.1 Introduction to Computer Vision 5.2 Image Processing 5.3 Object Detection 5.4 Image Segmentation 5.5 Face Recognition 6. Reinforcement Learning Applications 6.1 Reinforcement Learning in Robotics 6.2 Reinforcement Learning in Games 6.3 Reinforcement Learning in Finance 6.4 Reinforcement Learning in Healthcare 7. Ethics and Social Implications of Artificial Intelligence 7.1 Bias in Artificial Intelligence 7.2 The Future of Work 7.3 Privacy and Security 7.4 The Impact of AI on Society 8. Machine Learning Infrastructure 8.1 Cloud Infrastructure for Machine Learning 8.2 Distributed Machine Learning 8.3 DevOps for Machine Learning 9. Machine Learning Tools 9.1 Introduction to Machine Learning Tools 9.2 Python Libraries for Machine Learning 9.3 TensorFlow 9.4 Keras 9.5 PyTorch 10. Building and Deploying Machine Learning Models 10.1 Building a Machine Learning Model 10.2 Hyperparameter Tuning 10.3 Model Evaluation 10.4 Deployment Considerations 11. Time Series Analysis and Forecasting 11.1 Introduction to Time Series Analysis 11.2 ARIMA 11.3 Exponential Smoothing 11.4 Deep Learning for Time Series 12. Bayesian Machine Learning 12.1 Introduction to Bayesian Machine Learning 12.2 Bayesian Regression 12.3 Bayesian Classification 12.4 Bayesian Model Averaging 13. Anomaly Detection 13.1 Introduction to Anomaly Detection 13.2 Unsupervised Anomaly Detection 13.3 Supervised Anomaly Detection 13.4 Deep Learning for Anomaly Detection 14. Machine Learning in Healthcare 14.1 Introduction to Machine Learning in Healthcare 14.2 Electronic Health Records 14.3 Medical Image Analysis 14.4 Personalized Medicine 15. Recommender Systems 15.1 Introduction to Recommender Systems 15.2 Collaborative Filtering 15.3 Content-Based Filtering 15.4 Hybrid Recommender Systems 16. Transfer Learning 16.1 Introduction to Transfer Learning 16.2 Fine-Tuning 16.3 Domain Adaptation 16.4 Multi-Task Learning 17. Deep Reinforcement Learning 17.1 Introduction to Deep Reinforcement Learning 17.2 Deep Q-Networks 17.3 Actor-Critic Methods 17.4 Deep Reinforcement Learning Applications 18. Adversarial Machine Learning 18.1 Introduction to Adversarial Machine Learning 18.2 Adversarial Attacks 18.3 Adversarial Defenses 18.4 Adversarial Machine Learning Applications 19. Quantum Machine Learning 19.1 Introduction to Quantum Computing 19.2 Quantum Machine Learning 19.3 Quantum Computing Hardware 19.4 Quantum Machine Learning Applications 20. Machine Learning in Cybersecurity 20.1 Introduction to Machine Learning in Cybersecurity 20.2 Intrusion Detection 20.3 Malware Detection 20.4 Network Traffic Analysis 21. Future Directions in Artificial Intelligence and Machine Learning 21.1 Reinforcement Learning in Real-World Applications 21.2 Explainable Artificial Intelligence 21.3 Quantum Machine Learning 21.4 Autonomous Systems 22. Conclusion 22.1 Summary 22.2 Key Takeaways 22.3 Future Directions 22.4 Call to Action

Python Machine Learning

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Author : Rajender Kumar
language : en
Publisher: Jamba Academy
Release Date : 2023-03-02

Python Machine Learning written by Rajender Kumar and has been published by Jamba Academy this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-03-02 with Computers categories.

Are you ready to dive into the world of Python machine learning? Look no further! "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is the perfect guide for you. Written by experienced data scientist, Rajender Kumar, this book takes you on a journey through the basics of machine learning and the powerful Scikit-learn library. Key Features: Detailed introduction to the fundamentals of machine learning and the Scikit-Learn library. Comprehensive coverage of essential concepts such as data preprocessing, model selection, evaluation, and optimization. Hands-on experience with real-world datasets and practical projects that will help you develop the skills you need to succeed in machine learning. Easy-to-follow explanations and step-by-step examples that make it easy for beginners to get started and advanced users to take their skills to the next level. See how machine learning is being used to solve problems in industries such as healthcare, finance and more. This book is perfect for beginners who are new to machine learning and want to learn Scikit-Learn from scratch. It is also ideal for intermediate and advanced users who want to expand their knowledge and build more complex models. Outcome: Unlock the earning potential of up to $300k in job after reading the book. Boosting your resume. Opening doors to new opportunities. What other people says: Don't just take our word for it - see what other readers have said: "I was able to understand machine learning concepts and implement them easily with the help of this book." "Rajender Kumar's writing style made the complex concepts easy to understand." "I highly recommend this book to anyone looking to learn machine learning with Python." Don't miss out on this opportunity to master the art of Python machine learning with "Python Machine Learning: A Beginner's Guide to Scikit-Learn". Get your copy today and start building your own intelligent systems! WHO THIS BOOK IS FOR? "Python Machine Learning: A Beginner's Guide to Scikit-Learn" is intended for a wide range of readers, including: Individuals who are new to the field of machine learning and want to gain a solid understanding of the basics and how to apply them using the popular scikit-learn library in Python. Data scientists, statisticians, and analysts who are familiar with machine learning concepts but want to learn how to implement them using Python and scikit-learn. Developers and engineers who want to add machine learning to their skill set and build intelligent applications using Python. Students and researchers who are studying machine learning and want to learn how to apply it using a widely used and accessible library like scikit-learn. Table of Contents Introduction to Machine Learning Python: A Beginner's Overview Data Preparation Supervised Learning Unsupervised Learning Deep Learning Model Selection and Evaluation The Power of Combining: Ensemble Learning Methods Real-World Applications of Machine Learning Future Directions in Python Machine Learning Additional Resources Tools and Frameworks Datasets Career Resources Glossary

Learn Scikit Learn

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Author : Studiod21 Smart Tech Content
language : en
Publisher: Independently Published
Release Date : 2025-04-25

Learn Scikit Learn written by Studiod21 Smart Tech Content and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-25 with Computers categories.

LEARN Scikit-Learn: Essential Machine Learning for Data Science This book is the key to mastering Scikit-Learn and building market-ready Machine Learning projects. Ideal for professionals and students who seek fast, direct, and applicable results. Learn high-impact techniques, design intelligent solutions, and boost your career in data science. Includes: - Quick setup of a productive environment with Python and Scikit-Learn - Data preprocessing and full pipeline automation - Fine-tuning models with cross-validation and hyperparameters - Practical application of regression, classification, and clustering - Model deployment integrated with Big Data and MLOps environments Master the most widely used algorithms in the market and accelerate your entry into data science projects with Scikit-Learn. scikit-learn, machine learning, data science, smart pipelines, model automation, cross-validation, machine learning deployment, big data integration, mlops, spark

Python Data Science Essentials

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Author : Alberto Boschetti
language : en
Publisher: Packt Publishing Ltd
Release Date : 2016-10-28

Python Data Science Essentials written by Alberto Boschetti 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 2016-10-28 with Computers categories.

Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Mastering Machine Learning With Scikit Learn

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Author : Dr Benjamin Neudorf
language : en
Publisher: Independently Published
Release Date : 2025-08-22

Mastering Machine Learning With Scikit Learn written by Dr Benjamin Neudorf and has been published by Independently Published this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-22 with Computers categories.

Feeling overwhelmed by the idea of machine learning? Worried that coding or data science is just "too advanced" for you? You're not alone-and this book is your perfect starting point. Mastering Machine Learning with Scikit-Learn welcomes absolute beginners, guiding you gently from first steps to real-world results, no prior experience required. A Friendly Pathway to Modern Machine Learning If you've ever stared at lines of code and felt lost in jargon, you'll find a supportive companion here. Dr. Benjamin Neudorf draws on personal experience and a passion for teaching, transforming intimidating topics into simple, manageable lessons. You'll be gently introduced to machine learning and the powerful Scikit-Learn library, one of the most trusted tools in Python data science. What You'll Gain: Step-by-Step Confidence: Every chapter breaks big concepts into small, achievable actions, so you'll never feel stuck or left behind. Hands-On Projects: Build real machine learning models using practical examples, classic datasets, and clear explanations that demystify the process. Beginner-Friendly Explanations: No complex math or background needed-just curiosity and the willingness to learn at your own pace. Troubleshooting Support: Benefit from practical tips, quick references, and reassuring advice to help you overcome common challenges and celebrate progress. Real-World Skills: Learn how to prepare and clean data, choose and evaluate algorithms, interpret results, and build projects you'll be proud to share. Key Takeaways Include: Setting up your Python environment and installing essential tools with ease Understanding the core machine learning workflow: from raw data to working model Mastering data preparation, feature engineering, and encoding techniques Building and tuning supervised and unsupervised models (regression, classification, clustering) Evaluating and improving your models with industry-standard metrics and best practices Exploring ethical ML, avoiding common pitfalls, and growing your data science skills step by step Why This Book? Mistakes are part of the journey, and every small win is worth celebrating. This book normalizes learning curves, encourages experimentation, and helps you develop the confidence to ask questions and try new things. You'll finish not just knowing "what to do," but "why" it matters, and how to keep learning beyond these pages. Ready to unlock your potential? Start your empowering coding adventure today-discover just how approachable, practical, and even fun machine learning can be. Your journey into data science begins here, with a mentor who believes in you every step of the way.