Python machine learning deep von samuel burns (5 Ergebnisse)

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Softcover. Zustand: As New. Leichte Kratzer / Abnutzungen / Druckstellen. Want to learn Machine Learning and Deep Learning with Python, Scikit-Learn, and TensorFlow but don't know where to start? This book is perfect for you--no need for boring, expensive textbooks. The author covers everything from the basics in simple language, providing numerous examples and program outputs through screenshots. The content is organized chronologically and step-by-step. The book aims to help you grasp the fundamentals of machine learning and deep learning, understand various algorithm categories, and implement them programmatically in Python. You'll also learn to use Scikit-Learn and TensorFlow libraries, as well as analyze data to uncover patterns and relationships. Target readers include complete beginners, those looking to enhance their Python programming skills for machine learning, data science professionals, educators seeking effective teaching methods, and students focused on neural networks and deep learning. To get started, you need to have Python 3.X, along with Numpy, Pandas, and Matplotlib installed. The author guides you through installing other necessary libraries. Inside, you'll find topics like environment setup, linear regression, k-nearest neighbors, K-means clustering, support vector machines, neural networks, random forests, and recurrent neural networks. This book provides valuable insights into machine learning classifiers, helping you set up a learning pipeline that processes data and ap.…

Softcover. Zustand: As New. Are you eager to become a machine learning expert but unsure where to begin? You don't need a tedious and costly textbook. This guide is perfect for you! It covers machine learning and deep learning from the ground up, using simple language and numerous examples, both theoretical and programmatic, along with helpful screenshots of program outputs. The content is organized chronologically and step-by-step. The book aims to help you grasp the fundamentals of machine learning and deep learning, understand various algorithm categories, and learn how to implement algorithms programmatically in Python. You will also explore the use of Scikit-Learn and TensorFlow libraries, as well as techniques to analyze data for patterns and trends. Target readers include complete beginners in machine learning with Python, those looking to enhance their programming skills, data science professionals, educators seeking effective teaching methods, and students focused on neural networks and deep learning. To get started, ensure you have Python 3.X, along with Numpy, Pandas, and Matplotlib installed. The author provides guidance on installing additional necessary libraries. Inside, you'll find topics such as environment setup, linear regression, k-nearest neighbors, K-means clustering, support vector machines, neural networks, and more. This book equips you to build classifiers using Scikit-Learn and TensorFlow, offering valuable insights for bo.…

Zustand: New.

Zustand: New.