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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-data-science-python-practical-hands-on/
课程评论：没有评论
课程名称：机器学习与深度学习：Python实用操作 课程概述：如果您对机器学习领域感兴趣，那么本课程就是为您量身打造的！该课程由拥有15年以上相关经验的AI解决方案专家设计和制作，提供完整的Python AI模型开发实操体验。 课程内容包含： - 深入理解机器学习的基本原理与简单流程 - 基础的机器学习知识 - 深度学习神经网络的原理与实际示例 - 图像识别与自编码器的理解 - 机器学习项目生命周期 - 监督学习与非监督学习 - 数据预处理 - 算法选择 - 数据采样与交叉验证 - 特征工程 - 模型训练与验证 - K最近邻算法（KNN） - K-Means算法 - 准确性确定 - 使用Seaborn进行可视化 您将学习开发各种监督和非监督的方法算法，例如KNN、K-Means、随机森林、XGBoost模型的构建。课程将帮助您理解机器学习模型构建过程的基本概念及其验证与准确性度量的计算，同时帮助您确定最佳模型和算法。您还将理解交叉验证和采样方法，数据处理概念的实操指导和代码示例也会提供。特征工程作为关键的机器学习过程将以简单且有效的方式进行解释。 我们将一步步引导您进入机器学习的世界。通过每个教程，您将发展新技能，提升对这一具挑战性但又极具回报的数据科学子领域的理解。此外，课程中充满了基于真实案例的实操练习，这样您不仅能学习理论知识，还能亲手实践构建自己的模型。
Interested in the field of Machine Learning? Then this course is for you!Designed & Crafted by AI Solution Expert with 15 + years of relevant and hands on experience into Training , Coaching and Development.Complete Hands-on AI Model Development with Python. Course Contents are:Understand Machine Learning in depth and in simple process. Fundamentals of Machine LearningUnderstand the Deep Learning Neural Nets with Practical Examples.Understand Image Recognition and Auto Encoders.Machine learning project Life CycleSupervised & Unsupervised LearningData Pre-ProcessingAlgorithm SelectionData Sampling and Cross ValidationFeature EngineeringModel Training and ValidationK -Nearest Neighbor AlgorithmK- Means AlgorithmAccuracy DeterminationVisualization using SeabornYou will be trained to develop various algorithms for supervised & unsupervised methods such as KNN , K-Means , Random Forest, XGBoost model development. Understanding the fundamentals and core concepts of machine learning model building process with validation and accuracy metric calculation. Determining the optimum model and algorithm. Cross validation and sampling methods would be understood. Data processing concepts with practical guidance and code examples provided through the course. Feature Engineering as critical machine learning process would be explained in easy to understand and yet effective manner.We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models.
