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所在平台: Udemy |
课程主页: https://www.udemy.com/course/visual-guide-to-machine-learning/
课程评论：没有评论
课程名称：机器学习与数据科学：完整的视觉指南 课程概述： 本课程为普通人提供直观且易于入门的机器学习和数据科学介绍。通过逐步演示，建立信心，从基础开始学习核心技能。我们将分解和探讨机器学习技巧，帮助你理解其运作原理，而无需记忆复杂的数学或学习新的编码语言。课程将通过简单的视觉示例和用户友好的基于Excel的模型，教授诸如线性回归、逻辑回归、决策树、KNN、朴素贝叶斯、层次聚类、情感分析等主题，且不需要写一行代码。 课程内容： 本课程结合了Maven Analytics的四个畅销课程，组成一个全面的课程体系： ### 第一部分：单变量与多变量分析 介绍机器学习工作流程及清理和准备原始数据的常用技术。通过频率表、直方图、核密度及多维度分析工具（如热图、箱形图、散点图）来执行单变量与多变量分析。 ### 第二部分：分类建模 探讨监督学习及分类工作流程，涵盖依赖与独立变量、特征工程、数据划分及过拟合等主题。学习K近邻（KNN）、朴素贝叶斯、决策树等常见分类模型，并了解模型评分、选择与优化的方法。 ### 第三部分：回归与预测 介绍线性关系及最小平方误差，应用于各种回归模型。学习诊断指标（如R平方、均方误差等）及时间序列预测技术，以识别季节性趋势和评估商业决策的影响。 ### 第四部分：无监督学习 探索监督与无监督学习的区别，介绍聚类分析、关联挖掘、异常检测等无监督技术。深入理解K-means聚类、主成分分析等模型的基本原理及其应用。 课程特点： - 9小时的在线视频学习内容 - 350多页的《机器学习基础》电子书 - 可下载的Excel项目文件 - 专家问答论坛 - 30天退款保障 无论你是分析师还是希望成为数据专业人员，本课程都能帮助你为成功的机器学习或数据科学职业奠定基础，快来加入我们，开始学习吧！
This course is for everyday people looking for an intuitive, beginner-friendly introduction to the world of machine learning and data science.Build confidence with guided, step-by-step demos, and learn foundational skills from the ground up. Instead of memorizing complex math or learning a new coding language, we'll break down and explore machine learning techniques to help you understand exactly how and why they work.Follow along with simple, visual examples and interact with user-friendly, Excel-based models to learn topics like linear and logistic regression, decision trees, KNN, naïve bayes, hierarchical clustering, sentiment analysis, and more - without writing a SINGLE LINE of code.This course combines 4 best-selling courses from Maven Analytics into a single masterclass:PART 1: Univariate & Multivariate ProfilingPART 2: Classification ModelingPART 3: Regression & ForecastingPART 4: Unsupervised LearningPART 1: Univariate & Multivariate ProfilingIn Part 1 we'll introduce the machine learning workflow and common techniques for cleaning and preparing raw data for analysis. We'll explore univariate analysis with frequency tables, histograms, kernel densities, and profiling metrics, then dive into multivariate profiling tools like heat maps, violin & box plots, scatter plots, and correlation:Section 1: Machine Learning Intro & LandscapeMachine learning process, definition, and landscapeSection 2: Preliminary Data QAVariable types, empty values, range & count calculations, left/right censoring, etc.Section 3: Univariate ProfilingHistograms, frequency tables, mean, median, mode, variance, skewness, etc.Section 4: Multivariate ProfilingViolin & box plots, kernel densities, heat maps, correlation, etc.Throughout the course, we'll introduce real-world scenarios to solidify key concepts and simulate actual data science and business intelligence cases. You'll use profiling metrics to clean up product inventory data for a local grocery, explore Olympic athlete demographics with histograms and kernel densities, visualize traffic accident frequency with heat maps, and more.PART 2: Classification ModelingIn Part 2 we'll introduce the supervised learning landscape, review the classification workflow, and address key topics like dependent vs. independent variables, feature engineering, data splitting and overfitting. From there we'll review common classification models like K-Nearest Neighbors (KNN), Naïve Bayes, Decision Trees, Random Forests, Logistic Regression and Sentiment Analysis, and share tips for model scoring, selection, and optimization:Section 1: Intro to ClassificationSupervised learning & classification workflow, feature engineering, splitting, overfitting & underfittingSection 2: Classification ModelsK-nearest neighbors, naïve bayes, decision trees, random forests, logistic regression, sentiment analysisSection 3: Model Selection & TuningHyperparameter tuning, imbalanced classes, confusion matrices, accuracy, precision & recall, model driftYou'll help build a simple recommendation engine for Spotify, analyze customer purchase behavior for a retail shop, predict subscriptions for an online travel company, extract sentiment from a sample of book reviews, and more.PART 3: Regression & ForecastingIn Part 3 we'll introduce core building blocks like linear relationships and least squared error, and practice applying them to univariate, multivariate, and non-linear regression models. We'll review diagnostic metrics like R-squared, mean error, F-significance, and P-Values, then use time-series forecasting techniques to identify seasonality, predict nonlinear trends, and measure the impact of key business decisions using intervention analysis:Section 1: Intro to RegressionSupervised learning landscape, regression vs. classification, prediction vs. root-cause analysisSection 2: Regression Modeling 101Linear relationships, least squared error, univariate & multivariate regression, nonlinear transformationSection 3: Model DiagnosticsR-squared, mean error, null hypothesis, F-significance, T & P-values, homoskedasticity, multicollinearitySection 4: Time-Series ForecastingSeasonality, auto correlation, linear trending, non-linear models, intervention analysisYou'll see how regression analysis can be used to estimate property prices, forecast seasonal trends, predict sales for a new product launch, and even measure the business impact of a new website design.PART 4: Unsupervised LearningIn Part 4 we'll explore the differences between supervised and unsupervised machine learning and introduce several common unsupervised techniques, including cluster analysis, association mining, outlier detection and dimensionality reduction. We'll break down each model in simple terms and help you build an intuition for how they work, from K-means and apriori to outlier detection, principal component analysis, and more:Section 1: Intro to Unsupervised Machine LearningUnsupervised learning landscape & workflow, common unsupervised techniques, feature engineeringSection 2: Clustering & SegmentationClustering basics, K-means, elbow plots, hierarchical clustering, dendogramsSection 3: Association MiningAssociation mining basics, apriori, basket analysis, minimum support thresholds, markov chainsSection 4: Outlier DetectionOutlier detection basics, cross-sectional outliers, nearest neighbors, time-series outliers, residual distributionSection 5: Dimensionality ReductionDimensionality reduction basics, principle component analysis (PCA), scree plots, advanced techniquesYou'll see how K-means can help identify customer segments, how apriori can be used for basket analysis and recommendation engines, and how outlier detection can spot anomalies in cross-sectional or time-series datasets.__________Ready to dive in? Join today and get immediate, LIFETIME access to the following:9+ hours of on-demand videoML Foundations ebook (350+ pages)Downloadable Excel project filesExpert Q & A forum30-day money-back guaranteeIf you're an analyst or aspiring data professional looking to build the foundation for a successful career in machine learning or data science, you've come to the right place.Happy learning!-Josh & Chris__________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.
