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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-with-python-scikit-learn-tensorflow/
课程评论：没有评论
课程名称：使用Python、scikit-learn和TensorFlow的机器学习 课程概述：机器学习结合了计算机科学和统计学，以建立智能高效的模型。通过机器学习提供的强大技术，您将处理数据驱动的问题。本课程将机器学习与Python、scikit-learn和TensorFlow有效结合，帮助您实现实际问题的解决方案并自动化分析模型。这个综合性的3合1课程是您掌握机器学习算法及其实现的一站式解决方案。您将学习机器学习的基础知识，构建自己的智能应用程序，探索流行的机器学习模型，包括k最近邻、随机森林、逻辑回归、k均值、朴素贝叶斯和人工神经网络。 课程内容与概述：该培训计划包括三个完整的课程，经过精心选择，以提供最全面的培训。此课程将通过Python、scikit-learn和TensorFlow教您机器学习的实用建模方法，帮助您发现机器学习这一神秘的黑盒。第一个课程“逐步机器学习与Python”涵盖易于遵循的示例，使您能够快速开始机器学习。在这个课程中，您将学习所有重要概念，如探索性数据分析、数据预处理、特征提取、数据可视化、聚类、分类、回归和模型性能评估，并从零开始构建自己的模型。第二个课程“使用Scikit-learn进行机器学习”讨论了使用scikit-learn解决现实问题的有效学习算法。您将构建系统来分类文档、识别图像、检测广告等。您将学习如何使用scikit-learn的API从分类变量、文本和图像中提取特征，评估模型性能，并培养如何改进模型性能的直觉。第三个课程“使用TensorFlow进行机器学习”涵盖了通过Python进行机器学习的实践示例。您将了解该库的独特功能，如数据流图、训练和性能可视化（使用TensorBoard）——所有这些都在多个来源问题的丰富示例上下文中进行。重点是在每个部分中通过编码和解决问题来介绍新概念。到培训计划结束时，您将能够处理数据驱动的问题并实施解决方案，同时利用Python、scikit-learn和TensorFlow的强大而简单的功能构建高效的模型。 关于作者：Yuxi (Hayden) Liu目前是一名应用研究科学家，专注于为特定学习任务开发机器学习模型和系统。他有几年数据科学家的工作经验，曾在计算广告领域运用他的机器学习专长。他在多伦多大学获得学位，并在研究期间发表了五篇首作者的IEEE期刊和会议论文。他的第一本书《Python机器学习示例》在2017年被评为亚马逊印度的畅销书。他也是机器学习教育的热心倡导者。Shams Ul Azeem是一名来自巴基斯坦伊斯兰堡国立科学与技术大学的电气工程本科生。他对计算机科学领域有浓厚兴趣，最初从事Android开发。现在，他正在追求机器学习的职业生涯，特别是通过与不同公司的医学相关自由职业项目进行深度学习。他还是国立科学与技术大学RISE实验室的成员，并以共同作者身份参与了IEEE国际会议ROBIO的发表。
Machine learning brings together computer science and statistics to build smart, efficient models. Using powerful techniques offered by machine learning, you'll tackle data-driven problems. The effective blend of Machine Learning with Python, scikit-learn, and TensorFlow, helps in implementing solutions to real-world problems as well as automating analytical model. This comprehensive 3-in-1 course is your one-stop solution in mastering machine learning algorithms and their implementation. Learn the fundamentals of machine learning and build your own intelligent applications. Explore popular machine learning models including k-nearest neighbors, random forests, logistic regression, k-means, naive Bayes, and artificial neural networks Contents and Overview This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible. This course will help you discover the magical black box that is Machine Learning by teaching a practical approach to modeling using Python, scikit-learn and TensorFlow. The first course, Step-by-Step Machine Learning with Python, covers easy-to-follow examples that get you up and running with machine learning. In this course, you'll learn all the important concepts such as exploratory data analysis, data preprocessing, feature extraction, data visualization and clustering, classification, regression, and model performance evaluation. You'll build your own models from scratch. The second course, Machine Learning with Scikit-learn, covers effective learning algorithms to real-world problems using scikit-learn. You'll build systems that classify documents, recognize images, detect ads, and more. You'll learn to use scikit-learn's API to extract features from categorical variables, text and images; evaluate model performance; and develop an intuition for how to improve your model's performance. The third course, Machine Learning with TensorFlow, covers hands-on examples with machine learning using Python. You'll cover the unique features of the library such as data flow Graphs, training, and visualization of performance with TensorBoard-all within an example-rich context using problems from multiple sources.. The focus is on introducing new concepts through problems that are coded and solved over the course of each section. By the end of this training program you'll be able to tackle data-driven problems and implement your solutions as well as build efficient models with the powerful yet simple features of Python, scikit-learn and TensorFlow. About the Authors Yuxi (Hayden) Liu is currently an applied research scientist focused on developing machine learning models and systems for given learning tasks. He has worked for a few years as a data scientist, and applied his machine learning expertise in computational advertising. He earned his degree from the University of Toronto, and published five first-authored IEEE transaction and conference papers during his research. His first book, titled Python Machine Learning By Example, was ranked the #1 bestseller in Amazon India in 2017. He is also a machine learning education enthusiast. Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad, Pakistan. He has a great interest in the computer science field, and he started his journey with Android development. Now, he's pursuing his career in Machine Learning, particularly in deep learning, by doing medical-related freelancing projects with different companies. He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEE International Conference, ROBIO as a co-author of Designing of motions for humanoid goalkeeper robots.
