|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/data-science-sentiment-analysis-nlp-model-building-deployment/
课程评论：没有评论
本课程《Data Science: NLP: Sentiment Analysis - Model Building》旨在教授学员如何利用自然语言处理（NLP）技术和机器学习模型构建一个情感分析模型，用于将推文分类为积极或消极。 **课程内容概要：** 本课程为实践性项目，将引导学员完成机器学习模型的完整生命周期： * **数据探索与准备：** 包括安装所需软件包、导入库、加载数据、理解数据、数据预处理（如词性标注、词形还原、创建词云、识别高频词）以及数据训练集和测试集的划分。 * **特征工程：** 重点讲解 TF-IDF 向量化技术，并实践应用。 * **模型构建与评估：** 探索多种机器学习算法，比较模型表现，并学习使用混淆矩阵、分类报告和 AUC-ROC 等指标进行模型评估。最终选出性能最佳的模型。 * **模型部署：** 教授如何创建用户界面（使用 Streamlit）与模型进行交互，并将模型部署到云平台（如 Heroku），以便客户访问。 **课程结构：** 课程被细分为 27 个任务，覆盖从基础环境搭建到最终项目部署的每一个环节。 **学习收获：** * 牢固掌握 NLP 技术、机器学习和模型部署的知识。 * 获得 AutomationGig 的结业证书。 * 提供课程中使用的所有数据集、Jupyter Notebook 和项目文件。 本课程适合希望提升数据分析、NLP 和机器学习技能的学习者。
In this course I will cover, how to develop a Sentiment Analysis model to categorize a tweet as Positive or Negative using NLP techniques and 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 pre-processing, data preparation, model building, evaluation and deployment techniques. We will explore NLP concepts and then use multiple ML algorithms to create our model and finally focus 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 Packages.Task 2 : Importing Libraries.Task 3 : Loading the data from source.Task 4 : Understanding the dataTask 5 : Preparing the data for pre-processingTask 6 : Pre-processing steps overviewTask 7 : Custom Pre-processing functionsTask 8 : About POS tagging and Lemmatization Task 9 : POS tagging and lemmatization in action.Task 10: Creating a word cloud of positive and negative tweets.Task 11: Identifying the most frequent set of words in the dataset for positive and negative cases.Task 12: Train Test SplitTask 13: About TF-IDF VectorizerTask 14: TF-IDF Vectorizer in actionTask 15: About Confusion MatrixTask 16: About Classification ReportTask 17: About AUC-ROCTask 18: Creating a common Model Evaluation functionTask 19: Checking for model performance across a wide range of modelsTask 20: Final Inference and saving the modelsTask 21: Testing the model on unknown datasetsTask 22: Testing the model on unknown datasets - Excel optionTask 23: What is Streamlit and Installation steps.Task 24: Creating an user interface to interact with our created model.Task 25: Running your notebook on Streamlit Server in your local machine.Task 26: Pushing your project to GitHub repository.Task 27: Project Deployment on Heroku Platform for free.Data Analysis, NLP techniques, 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 NLP techniques, 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]
