Python machine learning

本書以案例驅動的方式講解機器學習算法的知識點，並以Python語言作為基礎開發語言實現算法，包括目前機器學習主流算法的原理、算法流程圖、算法的詳細設計步驟、算法實例、算法應用、算法的改進與優化等環節。全書共分 17 章，前兩章介紹機器學習與 Python 語言的相關基礎知識，後面各章以案例的方式分別介紹線性回歸算法、邏輯回歸算法、K *近鄰算法、PCA 降維算法、k-means算法、支持向量機算法

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have

A practical, scenario-based tutorial, this book will help you get to grips with machine learning with Python and start building your own machine learning projects. By the end of the book you will have

Master the essential skills needed to recognize and solve complex problems with machine learning and deep learning. Using real-world examples that leverage the popular Python machine learning ecosyste

Learn a simpler and more effective way to analyze data and predict outcomes with PythonMachine Learning in Python shows you how to successfully analyze data using only two core machine learning algori

Machine Learning with Spark and Python Essential Techniques for Predictive Analytics Second Edition simplifies ML for practical uses by focusing on two key algorithms. This new second edition improves

The Python programming language and its libraries, including pandas and scikit-learn, provide a production-grade environment to help you accomplish a broad range of machine-learning tasks. With this c

Python Machine Learning「機器學習」（machine learning）已是一門改變資料分析方式的重要學科，而本書將引領你進入預測性分析的世界，透過在科學領域已應用得相當廣泛的 Python 程式語言進行實作。藉 由本書，可以幫助你對資料分析的方式做出最佳決策，或是用於提昇機器學習系統的效能。內容包含 scikit-learn、Theano 及 Keras 等一系列強大的 P

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and n

Many Python developers are curious about what machine learning is and how it can be concretely applied to solve issues faced in businesses handling medium to large amount of data.Machine Learning with

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Create AI applications in Python and lay the foundations for your career in data scienceKey FeaturesPractical examples that explain key machine learning algorithmsExplore neural networks in detail wit

The programming landscape of natural language processing has changed dramatically in the past few years. Machine learning approaches now require mature tools like Python’s scikit-learn to apply