Practical Machine Learning Techniques Cookbook

Description

Machine Learning Cookbook

The Machine Learning Cookbook provides a practical guide for data scientists looking to enhance their skills and tackle complex data challenges. This book focuses on implementing a wide range of algorithms and techniques, helping you improve predictions and recommendations for better accuracy, and optimizing the performance of your machine-learning systems.

Key Features:

• Implement a wide range of algorithms and techniques for tackling complex data.
• Improve predictions and recommendations to achieve better levels of accuracy.
• Optimize the performance of your machine-learning systems.

Book Description:

Machine learning has become an essential discipline in today’s data-driven world. With the explosion of data from both legacy systems and incoming structured and unstructured datasets, the complexity of discovering, understanding, performing analysis, and predicting outcomes using machine learning algorithms has increased significantly. This cookbook addresses everyday challenges faced by data scientists and provides practical solutions.

The first half of the book offers recipes for fairly complex machine-learning systems. Here, you’ll explore innovative applications of machine learning and learn to improve its efficiency. Topics include classification, neural networks, unsupervised and supervised learning, deep learning, reinforcement learning, and much more.

The second half of the book focuses on three distinct machine-learning case studies, each based on real-world data, providing solutions and addressing specific machine-learning issues encountered in practice. These case studies allow you to apply what you’ve learned and understand the practical implications of machine learning techniques.

What You Will Learn:

• Get equipped with a deeper understanding of how to apply machine-learning techniques effectively.
• Implement advanced machine-learning techniques to solve real-life problems.
• Gain hands-on experience in problem-solving for your machine-learning systems.
• Understand the processes of collecting data, preparing it for use, training models, and evaluating and improving model performance.

About the Author:

Atul Tripathi has over 11 years of experience in machine learning and quantitative finance, with a total of 14 years in software development and research. He has worked extensively on advanced machine learning techniques, such as neural networks and Markov models, solving problems related to image processing, telecommunications, human speech recognition, and natural language processing. His expertise in quantitative finance includes developing models for Value at Risk, Extreme Value Theorem, Option Pricing, and Energy Derivatives using Monte Carlo simulation techniques.

Table of Contents:

1. Introduction to Machine Learning
2. Classification
3. Clustering
4. Model Selection and Regularization
5. Nonlinearity
6. Supervised Learning
7. Unsupervised Learning
8. Reinforcement Learning
9. Structured Prediction
10. Neural Networks
11. Deep Learning
12. Case Study – Exploring World Bank Data
13. Case Study – Pricing Reinsurance Contracts
14. Case Study – Forecast of Electricity Consumption

Explore These Valuable Resources:

• O’Reilly – Machine Learning Cookbook
• Towards Data Science – Machine Learning in Practice
• Machine Learning Mastery – Python Techniques

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• Machine Learning Courses
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• Algorithms Courses