Practical Machine Learning with Python A Problem-Solver's Guide to Building Real-World Intelligent Systems "Data is the new oil" is a saying which you must have heard by now along with the huge interest building up around Big Data and Machine Learning in the recent past along with Artificial...
Design, develop, and validate machine learning models with streaming data using the Scikit-Multiflow framework. This book is a quick start guide for data scientists and machine learning engineers looking to implement machine learning models for streaming data with Python to generate real-time insights...
Practical Machine Learning with Python follows a structured and comprehensive three-tiered approach packed with hands-on examples and code.Part 1 focuses on understanding machine learning concepts and tools. This includes machine learning basics with a broad overview of algorithms, techniques, concepts ...
In the preceding chapters, you saw how you can extract, process, and transform data to convert it to a form suitable for learning using Machine Learning algorithms. This chapter deals with the most important part of using that processed data, to learn a model that you can then use to ...
使用Python进行的实际机器学习遵循结构化和全面的三层方法,其中包含了实践示例和代码。 第1部分侧重于理解机器学习的概念和工具。这包括机器学习基础,对算法、技术、概念和应用程序的广泛概述,然后介绍整个Python机器学习生态系统。还包括有用的机器学习工具、库和框架的简要指南。
The book teaches readers the vital skills required to understand and solve different problems with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book ...
In this section, readers will learn several important machine learning algorithms and techniques through the process of solving real-world problems. The journey of learning machine learning by example includes mining natural language text data with dimensionality reduction and clustering algorithms, content...
You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementati...
Chapter 2: Using Python Chapter 3: Using NumPy Chapter 4: Working With Data Chapter 5: Building Datasets Chapter 6: Classical Machine Learning Chapter 7: Experiments with Classical Models Chapter 8: Introduction to Neural Networks Chapter 9: Training A Neural Network Chapter 10: Experiments with ...
with machine learning. It teaches machine learning techniques necessary to become a successful practitioner, through the presentation of real-world case studies in Python machine learning ecosystems. The book also focuses on building a foundation of machine learning knowledge to solve different real-...