Machine Learning Fundamentals Use Python and scikit-learn to get up and running with the hottest developments in machine learning【電子書籍】[ Hyatt Saleh ]

＜p＞＜strong＞With the flexibility and features of scikit-learn and Python, build machine learning algorithms that optimize the programming process and take application performance to a whole new level＜/strong＞＜/p＞ ＜h4＞Key Features＜/h4＞ ＜ul＞ ＜li＞Explore scikit-learn uniform API and its application into any type of model＜/li＞ ＜li＞Understand the difference between supervised and unsupervised models＜/li＞ ＜li＞Learn the usage of machine learning through real-world examples＜/li＞ ＜/ul＞ ＜h4＞Book Description＜/h4＞ ＜p＞As machine learning algorithms become popular, new tools that optimize these algorithms are also developed. Machine Learning Fundamentals explains you how to use the syntax of scikit-learn. You'll study the difference between supervised and unsupervised models, as well as the importance of choosing the appropriate algorithm for each dataset. You'll apply unsupervised clustering algorithms over real-world datasets, to discover patterns and profiles, and explore the process to solve an unsupervised machine learning problem.＜/p＞ ＜p＞The focus of the book then shifts to supervised learning algorithms. You'll learn to implement different supervised algorithms and develop neural network structures using the scikit-learn package. You'll also learn how to perform coherent result analysis to improve the performance of the algorithm by tuning hyperparameters.＜/p＞ ＜p＞By the end of this book, you will have gain all the skills required to start programming machine learning algorithms.＜/p＞ ＜h4＞What you will learn＜/h4＞ ＜ul＞ ＜li＞Understand the importance of data representation＜/li＞ ＜li＞Gain insights into the differences between supervised and unsupervised models＜/li＞ ＜li＞Explore data using the Matplotlib library＜/li＞ ＜li＞Study popular algorithms, such as k-means, Mean-Shift, and DBSCAN＜/li＞ ＜li＞Measure model performance through different metrics＜/li＞ ＜li＞Implement a confusion matrix using scikit-learn＜/li＞ ＜li＞Study popular algorithms, such as Na?ve-Bayes, Decision Tree, and SVM＜/li＞ ＜li＞Perform error analysis to improve the performance of the model＜/li＞ ＜li＞Learn to build a comprehensive machine learning program＜/li＞ ＜/ul＞ ＜h4＞Who this book is for＜/h4＞ ＜p＞Machine Learning Fundamentals is designed for developers who are new to the field of machine learning and want to learn how to use the scikit-learn library to develop machine learning algorithms. You must have some knowledge and experience in Python programming, but you do not need any prior knowledge of scikit-learn or machine learning algorithms.＜/p＞画面が切り替わりますので、しばらくお待ち下さい。 ※ご購入は、楽天kobo商品ページからお願いします。※切り替わらない場合は、こちら をクリックして下さい。 ※このページからは注文できません。