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所在平台: Udemy |
课程主页: https://www.udemy.com/course/datascience-knime/
课程评论：没有评论
Coursera 课程“使用 KNIME 进行端到端数据科学实操”课程总结 本课程采用自上而下的方式，介绍完整的数据科学项目流程。课程将遵循 CRISP-DM 方法论的六个阶段。 **1. 业务理解（Business Understanding）：** 探讨现实生活中的各种问题类型和业务流程。 **2. 数据理解（Data Understanding）：** 学习数据类型和常见的数据问题，并通过数据可视化来探索数据。 **3. 数据预处理（Data Preprocessing）：** 解决数据中的经典问题，包括处理噪声、脏数据和缺失值。将学习行/列过滤、数据连接和合并，以及数据转换技术，如离散化、归一化或透视。 **4. 机器学习（Machine Learning）：** * **分类算法：** 涵盖朴素贝叶斯、决策树、逻辑回归、K-NN 等。 * **预测/回归算法：** 学习线性回归、多项式回归、决策树回归等。 * **无监督学习：** 探索聚类（K-Means、层次聚类）和关联规则学习（Apriori 算法）。 * **集成技术：** 在 KNIME 中应用集成学习方法。 **5. 模型评估（Evaluation）：** * **分类评估：** 学习混淆矩阵、准确率、召回率、灵敏度、特异度等指标。 * **聚类评估：** 了解纯度（Purity）、Rand Index 等。 * **回归/预测评估：** 掌握 RMSE、RMAE、MSE、MAE 等指标。 **奖励课程（Bonus Classes）：** * **人工智能神经网络和深度学习：** 专注于图像处理问题。 **请注意：** 本课程仍在建设中，视频上传需要时间，敬请谅解。
The course starts with a top down approach to data science projects. The first step is covering data science project management techniques and we follow CRISP-DM methodology with 6 steps below:Business Understanding: We cover the types of problems and business processes in real lifeData Understanding: We cover the data types and data problems. We also try to visualize data to discover. Data Preprocessing: We cover the classical problems on data and also handling the problems like noisy or dirty data and missing values. Row or column filtering, data integration with concatenation and joins. We cover the data transformation such as discretization, normalization, or pivoting. Machine Learning: we cover the classification algorithms such as Naive Bayes, Decision Trees, Logistic Regression or K-NN. We also cover prediction / regression algorithms like linear regression, polynomial regression or decision tree regression. We also cover unsupervised learning problems like clustering and association rule learning with k-means or hierarchical clustering, and a priori algorithms. Finally we cover ensemble techniques in Knime.Evaluation: In the final step of data science, we study the metrics of success via Confusion Matrix, Precision, Recall, Sensitivity, Specificity for classification; purity , randindex for Clustering and rmse, rmae, mse, mae for Regression / Prediction problems with Knime.BONUS CLASSESWe also have bonus classes for artificial neural network and deep learning on image processing problems. Warning: We are still building the course and it will take time to upload all the videos. Thanks for your understanding.
