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所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-diabetes-prediction-model-building-deployment/
课程评论：没有评论
Coursera 上的“数据科学：糖尿病预测 - 模型构建与部署”课程是一门实操性强的项目课程，旨在教授学员如何利用机器学习模型预测糖尿病。 课程内容全面，从初始数据探索、数据分析，到数据准备、模型构建、评估，再到最终的云平台部署，都将进行详细讲解。学员将学习多种机器学习算法，并选择性能最佳的模型。 课程还将指导学员创建用户界面，以便客户能与模型进行交互。最后，课程将演示如何将模型部署到云平台，让项目上线。 **课程主要任务包括：** * **环境设置与数据准备:** 安装必要的软件包、导入库、加载数据、进行 Pandas Profiling、理解数据、数据清洗与插补、训练集/测试集划分、使用 StandardScaler 进行数据缩放。 * **模型构建与评估:** 了解混淆矩阵、分类报告 (Classification Report) 和 AUC-ROC 指标。评估多种模型的性能，并重点关注 Random Forest 模型。学习使用 RandomizedSearchCV 进行超参数调优，并训练最佳的 Random Forest 分类器模型。进行最终的模型评估和推理。 * **模型部署与用户界面:** 加载已保存的模型和缩放器对象，并在随机数据上进行测试。学习 Streamlit 并了解其安装步骤。创建用户界面以增强模型交互性。在本地机器上通过 Streamlit 服务器运行笔记本。将项目推送到 GitHub 仓库。最后，在 Heroku 平台上免费部署项目。 本课程强调数据分析、模型构建和部署在 21 世纪的重要性。完成课程后，学员将获得 AutomationGig 的结业证书，并能获得课程中使用的数据集、Jupyter Notebook 和其他项目文件。 这是一次学习热门技能、提升专业能力的绝佳机会。
This course is about predicting whether or not the person has diabetes using Machine Learning Models. This is a hands on project where I will teach you the step by step process in creating and evaluating a machine learning model and finally deploying the same on Cloud platforms to let your customers interact with your model via an user interface.This course will walk you through the initial data exploration and understanding, data analysis, data preparation, model building, evaluation and deployment techniques. We will explore multiple ML algorithms to create our model and finally zoom into one which performs the best on the given dataset.At the end we will learn to create an User Interface to interact with our created model and finally deploy the same on Cloud.I have splitted and segregated the entire course in Tasks below, for ease of understanding of what will be covered.Task 1 : Installing PackagesTask 2 : Importing Libraries.Task 3 : Loading the data from source.Task 4 : Pandas ProfilingTask 5 : Understanding the dataTask 6 : Data Cleaning and ImputationTask 7 : Train Test SplitTask 8 : Scaling using StandardScalerTask 9 : About Confusion MatrixTask 10: About Classification ReportTask 11: About AUC-ROCTask 12: Checking for model performance across a wide range of modelsTask 13: Creating Random Forest model with default parametersTask 14: Model Evaluation - Classification Report,Confusion Matrix,AUC-ROCTask 15: Hyperparameter Tuning using RandomizedSearchCVTask 16: Building RandomForestClassifier model with the selected hyperparametersTask 17: Final Model Evaluation - Classification Report,Confusion Matrix,AUC-ROCTask 18: Final InferenceTask 19: Loading the saved model and scaler objectsTask 20: Testing the model on random dataTask 21: What is Streamlit and Installation steps.Task 22: Creating an user interface to interact with our created model.Task 23: Running your notebook on Streamlit Server in your local machine.Task 24: Pushing your project to GitHub repository.Task 25: Project Deployment on Heroku Platform for free.Data Analysis, Model Building and Deployment is one of the most demanded skill of the 21st century. Take the course now, and have a much stronger grasp of data analysis, machine learning and deployment in just a few hours!You will receive:1. Certificate of completion from AutomationGig.2. All the datasets used in the course are in the resources section.3. The Jupyter notebook and other project files are provided at the end of the course in the resource section.So what are you waiting for?Grab a cup of coffee, click on the ENROLL NOW Button and start learning the most demanded skill of the 21st century. We'll see you inside the course!Happy Learning!![Please note that this course and its related contents are for educational purpose only]Music: bensound
