|
所在平台: Udemy |
课程主页: https://www.udemy.com/course/pycaret-for-marketing-analytics/
课程评论：没有评论
课程名称：营销分析的自动化机器学习（AutoML） 课程概述：在寻找新职位（无论是作为初学者还是资深数据分析师）或推销自由职业服务时，您的营销分析作品集的实力取决于多个因素：(1) 项目的多样性 - 营销分析项目通常聚焦于聚类、回归和分类问题，此外还要展示新产品开发的主题建模能力。 (2) 项目的背景信息 - 这是展示您商业洞察力和对行业知识的理解的机会，无论是银行、通信还是电子商务行业，您都能与不同类型的数据工作，并将洞察融入上下文中。 (3) 展示您在机构内利用公民数据洞察的能力，您可以成为自动化机器学习和专业电子商务营销Python包的首席资源人。 课程内容包括： - 低代码解决方案，分析数百万客户互动并发现隐藏洞察。 - 准确预测客户流失并创建针对性的保留活动，仅需少量代码。 - 借助先进的聚类算法革命性地进行客户细分，理解买家画像以增加销售。 - 通过先进的主题建模深入理解客户情感，转变市场策略。 - 利用关联规则挖掘提升销售并优化交叉销售和追加销售策略，从而增加客户终身价值。 如果您是初学者，不必担心，我们将使用一个Auto Machine Learning包，您可以下载代码手册，改变数据集，并在我们一起进行的练习中自行运行不同步骤（但建议完全初学者在工作项目中实验自己的数据集时，让数据科学家审核其贡献 - AutoML提供了一个简单的起点，消除“挫折点”，但精确且可用的解决方案仍需专家）。此外，我们主要使用内置数据集，旨在消除学习旅程初期的挫折，为初学者创造胜利感。 PyCaret是由Moez Ali开发的一个 AutoML 库，应用广泛。这对于现有的自由数据科学分析提供者，能够通过PyCaret扩展服务。PyCaret生成的可视化效果可用于与利益相关者有效沟通关键洞察。 PyCaret的异常检测模块可用于需求激增检测、社交媒体反应的异常检测等；其关联规则挖掘课程可帮助识别电子商务数据集中交易数据的模式；主题建模模块则可以从大量非结构化文本中识别主题，非常适合新产品开发和筛选产品评论中需要采纳的新特性。 本课程适合希望提升营销分析技能并获取竞争优势的营销分析师、数据科学家和商业领袖。无论是初学者还是经验丰富的专业人士，本课程将帮助您获取新的洞察力和技能，提升您的营销策略。 课程收益： - 应用RFM分析、客户流失预测、情感分析、主题建模和关联规则挖掘。 - 快速进行数据预处理、特征工程、模型选择和评估。 - 利用引人注目的可视化结果与利益相关者沟通洞察和结果。 - 获得真实数据和用例的实践经验。 您将学习如何使用Python中的机器学习和自然语言处理创建预测模型，进行可视化和沟通结果，并将概念应用于现实世界的营销挑战。我们将在本课程中使用Google Colab，一起开始吧！
While assembling your portfolio both when you're looking for a new role (either as a beginner or as an experienced data analyst) or if you're pitching your services on a freelance basis, the strength of your marketing analytics portfolio depends on:(1) the diversity of the projects undertaken - marketing analytics projects will frequently showcase clustering, regression, and classification problems. Go beyond to showcase Topic Modelling for new product development. (2) how well contextualized the projects are - this is your chance to shine and demonstrate your business acumen and your insight into the constraints and domain knowledge the sector grapples with - be it banking, telecommunication, or e-commerce, you'll find you can not only work with different types of data, but you can stack the insights into the context (3) Showcase your ability to leverage citizen data insights within the institution - you can position yourself as the go-to resource person on auto-machine learning and specialized e-commerce marketing Python packages.The course will cover:The low-code solution to analyzing millions of customer interactions and unlocking hidden insightsHow to accurately predict customer churn and create targeted retention campaigns in just a few lines of codeRevolutionize customer segmentation with state-of-the-art clustering algorithms and increase sales by understanding buyer personasTransform your marketing strategy by gaining a deeper understanding of customer sentiment with cutting-edge topic modelingLeverage association rule mining to increase sales and enhance customer lifetime value through optimized cross-selling and up-selling campaigns.If you're a beginner, worry not, we are working with an Auto Machine Learning Package where you can download the codebook, change the dataset, and run through the different steps to glean similar insights as the exercises we walk through together by yourself when you use your own datasets (however, if one is a complete beginner experimenting with their own datasets for a project at work, it's best to have contributions reviewed by a Data Scientist - AutoML provides an easy starting point, and eliminates "points of frustration", yet precise and usable solutions need experts). Plus, we are primarily working with inbuilt datasets which means you don't have to trip yourself up in downloading the datasets and loading them again into your notebook and your environment (the objective here is to eliminate frustrations at the beginning of a learning journey, and to instead stack wins - this insight, derived from habit formation research, is especially useful as a beginner where working professionals may not find the time and energy to invest in learning a skill ).PyCaret, developed by Moez Ali is an AutoML library with a wide range of applications:If you're an existing freelance data science analytics provider, you can double the services you provide in analytics by using PyCaret. Leverage the visuals that PyCaret generates to communicate critical insights to your stakeholders.PyCaret Anomaly Detection module is useful to detect spikes in demand for inventory management, detect anomalous reactions to Social Media posts, etc.PyCaret's Association Rule Mining course helps you identify patterns within transaction datasets for e-commerce datasets, or if you plan to service Hypermarkets or Supermarket chains. PyCaret's Topic Modeling for new product development or for identifying themes from large amounts of unstructured text. Whether you are combining through 1000s of product reviews to identify new features that need to be adopted, you no longer need to read these documents when you can instead leverage unsupervised learning to glean the themes in the document collection. This course is designed for marketing analysts, data scientists, and business leaders who want to improve their skills in marketing analytics and gain a competitive advantage. Whether a beginner or an experienced professional, this course will help you gain new insights and skills to enhance your marketing strategies.Here are some of the benefits of taking this course:Apply RFM analysis, customer churn prediction, sentiment analysis, topic modeling, and association rule miningQuickly undertake data preprocessing, feature engineering, model selection, and evaluation using Auto Machine Learning Communicate insights and results to stakeholders with compelling visuals that enhance explainability and effectively aid decision-makingGain hands-on experience with real-world data and use casesYou will learn how to use machine learning and NLP in Python to create predictive models, visualize and communicate results, and apply the concepts to real-world marketing challenges.We will be using Google Colab in this course, so let us get started.
