Machine Learning Step-by-Step : Guide for Students, Entrepreneurs, Business Leaders & the Curious
Machine Learning Step-by-Step : Guide for Students, Entrepreneurs, Business Leaders & the Curious (Step By Step Subject Guides) by Mitchell Ng
English | April 22, 2024 | ISBN: N/A | ASIN: B0D2JP1W8S | PDF | 2.17 Mb
English | April 22, 2024 | ISBN: N/A | ASIN: B0D2JP1W8S | PDF | 2.17 Mb
Have you ever wondered how Netflix or YouTube recommends your next binge-watch or how self-driving cars navigate? The answer lies in Machine Learning (ML). "Machine Learning Step-by-Step" is your guide to understanding and using its incredible potential.__
This handbook is designed for anyone eager to learn, regardless of their background. Whether you're a student, entrepreneur, business leader, beginner, or simply curious, this book provides a clear, engaging path to mastering ML:
- Start from the Ground Up: Gain a solid foundation with easy-to-understand explanations of fundamental concepts, types of ML, algorithms, and data preprocessing techniques.
- Master Essential Techniques: Regression analysis, classification, clustering methods, dimensionality reduction, and neural networks and deep learning.
- Explore Real-World Applications: Discover how ML is transforming industries like finance, asset management, healthcare, retail, and cybersecurity, with practical examples and case studies.
- Implement ML Solutions: Choosing the right tools and libraries, integrating ML into existing systems, managing projects, and teaching the subject to others.
- Navigate Ethical Considerations: Understand issues like bias, fairness, data privacy, and responsible AI development.
- Stay Ahead of the Curve: Explore future trends, disruptive technologies, and how to prepare for the ever-evolving ML landscape.
This book is your complete toolkit:
- 37 comprehensive chapters: from basic concepts to advanced techniques and real-world applications.
- Clear and concise explanations: written in plain English with practical examples and illustrations.
- Practical advice and best practices: for implementing and scaling ML solutions.
- A focus on ethical considerations: ensuring responsible and fair development of AI.
- Audience: Suitable machine learning guide for beginners, kids, students, entrepreneurs, and business leaders.
Categories & Topics:
1. Machine Learning Foundations:
- Concepts: Algorithms, data preprocessing, model evaluation, overfitting, bias-variance tradeoff
- Types: Supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), reinforcement learning
- Algorithms: Linear regression, logistic regression, decision trees, random forests, support vector machines, K-means clustering, principal component analysis, neural networks, deep learning
- Deep Learning Architectures: Convolutional and recurrent neural networks (CNNs) and (RNNs), LSTMs, transformers
- Ensemble Methods: Bagging, boosting, stacking
- AutoML: Automated machine learning, hyperparameter optimization
- Other Techniques: Natural language processing (NLP), reinforcement learning applications, data augmentation, synthetic data generation
- Finance: Algorithmic trading, asset management, risk assessment, fraud detection, personalized banking
- Retail: Customer segmentation, inventory management, recommendation systems, predictive analytics
- Other Industries: Cybersecurity, healthcare, manufacturing, agriculture, education, transportation
- Tools and Libraries: Scikit-learn, TensorFlow, PyTorch, Keras, and others
- Cloud Computing: AWS, Google Cloud, Azure, and their ML services
- Project Management: Agile methodology, team roles, monitoring/reporting
- Big Data: Large datasets, big data technologies, real-time data processing