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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-in-python-unsupervised-learning/
课程评论：没有评论
课程名称：Python 数据科学：无监督机器学习 课程概述：本课程是一个实践性和项目导向的课程，旨在帮助您掌握Python中无监督机器学习的基础知识。课程将从回顾Python数据科学工作流程开始，讨论无监督学习的技术与应用，并逐步演示建模所需的数据准备步骤。您将学习如何设定正确的行粒度进行建模，应用特征工程技术，选择相关特征，以及使用标准化和归一化来对数据进行缩放。 课程内容包括： - **聚类技术**：使用scikit-learn拟合、调优和解释三种流行的聚类模型，包括K-Means聚类、层次聚类和DBSCAN，使用不同的可视化工具来帮助解释和选择聚类的数量。 - **异常检测**：学习无监督学习模型在识别离群点和异常模式中的应用，使用DBSCAN和孤立森林方法进行异常检测，并使用成对图形可视化异常结果。 - **降维技术**：讨论降维的好处，介绍主成分分析（PCA）和t-SNE等常用技术，帮助您在不丢失信息的情况下减少数据集中的特征数量。 - **推荐系统**：掌握创建基于内容和协同过滤的推荐引擎，学习使用余弦相似度和奇异值分解（SVD）等技术。 在整个课程中，您将充当软件公司人力资源分析团队的助理数据科学家，旨在提高员工留存率。您将使用所学知识来对员工进行细分、可视化聚类，并推荐下一步以提高员工留存率。 课程大纲包括： 1. Python 数据科学概论 2. 无监督学习基础 3. 建模前的数据准备 4. 聚类技术的应用 5. 异常检测理解与应用 6. 降维技术 7. 推荐系统的认识与实践 课程包括16.5小时的优质视频、22个作业、7个测验、3个项目、一本350多页的电子书和可下载的项目文件与解决方案，确保您更有效地学习。 如果您是寻求无监督学习技术实践概述的商业智能专业人士或数据科学家，本课程将非常适合您。立即加入，享有终身访问权限，开始您的学习之旅！
This is a hands-on, project-based course designed to help you master the foundations for unsupervised machine learning in Python.We'll start by reviewing the Python data science workflow, discussing the techniques & applications of unsupervised learning, and walking through the data prep steps required for modeling. You'll learn how to set the correct row granularity for modeling, apply feature engineering techniques, select relevant features, and scale your data using normalization and standardization.From there we'll fit, tune, and interpret 3 popular clustering models using scikit-learn. We'll start with K-Means Clustering, learn to interpret the output's cluster centers, and use inertia plots to select the right number of clusters. Next, we'll cover Hierarchical Clustering, where we'll use dendrograms to identify clusters and cluster maps to interpret them. Finally, we'll use DBSCAN to detect clusters and noise points and evaluate the models using their silhouette score.We'll also use DBSCAN and Isolation Forests for anomaly detection, a common application of unsupervised learning models for identifying outliers and anomalous patterns. You'll learn to tune and interpret the results of each model and visualize the anomalies using pair plots.Next, we'll introduce the concept of dimensionality reduction, discuss its benefits for data science, and explore the stages in the data science workflow in which it can be applied. We'll then cover two popular techniques: Principal Component Analysis, which is great for both feature extraction and data visualization, and t-SNE, which is ideal for data visualization.Last but not least, we'll introduce recommendation engines, and you'll practice creating both content-based and collaborative filtering recommenders using techniques such as Cosine Similarity and Singular Value Decomposition.Throughout the course you'll play the role of an Associate Data Scientist for the HR Analytics team at a software company trying to increase employee retention. Using the skills you learn throughout the course, you'll use Python to segment the employees, visualize the clusters, and recommend next steps to increase retention.COURSE OUTLINE:Intro to Data Science in PythonIntroduce the fields of data science and machine learning, review essential skills, and introduce each phase of the data science workflowUnsupervised Learning 101Review the basics of unsupervised learning, including key concepts, types of techniques and applications, and its place in the data science workflowPre-Modeling Data PrepRecap the data prep steps required to apply unsupervised learning models, including restructuring data, engineering & scaling features, and moreClusteringApply three different clustering techniques in Python and learn to interpret their results using metrics, visualizations, and domain expertiseAnomaly DetectionUnderstand where anomaly detection fits in the data science workflow, and apply techniques like Isolation Forests and DBSCAN in PythonDimensionality ReductionUse techniques like Principal Component Analysis (PCA) and t-SNE in Python to reduce the number of features in a data set without losing informationRecommendersRecognize the variety of approaches for creating recommenders, then apply unsupervised learning techniques in Python, including Cosine Similarity and Singular Vector Decomposition (SVD)__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:16.5 hours of high-quality video22 homework assignments7 quizzes3 projectsPython Data Science: Unsupervised Learning ebook (350+ pages)Downloadable project files & solutionsExpert support and Q & A forum30-day Udemy satisfaction guaranteeIf you're a business intelligence professional or data scientist looking for a practical overview of unsupervised learning techniques in Python with a focus on interpretation, this is the course for you.Happy learning!-Alice Zhao (Python Expert & Data Science Instructor, Maven Analytics)__________Looking for our full business intelligence stack? Search for "Maven Analytics" to browse our full course library, including Excel, Power BI, MySQL, Tableau and Machine Learning courses!See why our courses are among the TOP-RATED on Udemy:"Some of the BEST courses I've ever taken. I've studied several programming languages, Excel, VBA and web dev, and Maven is among the very best I've seen!" Russ C."This is my fourth course from Maven Analytics and my fourth 5-star review, so I'm running out of things to say. I wish Maven was in my life earlier!" Tatsiana M."Maven Analytics should become the new standard for all courses taught on Udemy!" Jonah M.
