Küme sayısı seçimi

Crystal, Houston, Texas'ta bir yemek teslimat servisinin operasyonlarını yönetiyor. Geçen aydaki yemek taleplerinin enlem ve boylam verilerine sahip. Talebin en yoğun olduğu yerlere birkaç yeni "teslimat merkezi" kurmak istiyor. Bunu yapmak için, siparişleri konumlarına göre gruplara ayırmak üzere bir Machine Learning bilimcisinden bir kümeleme algoritması kullanmasını istemiş.

Machine Learning bilimcisi kaç küme oluşturacağından emin değil. Sağda, farklı küme sayılarıyla algoritmayı çalıştırarak sonuçları gör.

En uygun küme sayısı nedir?

Bu egzersiz

İş Dünyası için Data Science

kursunun bir parçasıdır

Kursu Görüntüle

Uygulamalı interaktif egzersiz

İnteraktif egzersizlerimizden biriyle teoriyi pratiğe dökün

Egzersizi başlat

Bu egzersiz

İş Dünyası için Data Science

kursunun bir parçasıdır

InicianteNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

We'll start the course by defining what data science is. We'll cover the data science workflow, and how data science is applied to real-world business problems. We'll finish the chapter by learning about ways to structure your data team to meet your organization's needs.

Exercise 1: What is Data Science?Exercise 2: Customer Segmentation Workflow Exercise 3: Building a customer service chatbot Exercise 4: Improving OKRs Exercise 5: Applications of Data Science Exercise 6: Assigning data science projects Exercise 7: Investment research Exercise 8: Building a data science team Exercise 9: Interpreting a team sprint Exercise 10: Editing a job posting Exercise 11: Matching skills to jobs Exercise 12: Classifying data tasks

Now that we understand the data science workflow, we'll dive deeper into the first step: data collection. We'll learn about the different data sources your company can draw from, and how to store that data once it's collected.

Exercise 1: Data sources and risks Exercise 2: Classifying data for security Exercise 3: Creating web data events Exercise 4: Protecting PII Exercise 5: Solicited data Exercise 6: Identifying question purpose Exercise 7: Validating focus group feedback Exercise 8: Net Promoter Score Exercise 9: Collecting additional data Exercise 10: Sorting data sources Exercise 11: Asthma frequency Exercise 12: Data storage and retrieval Exercise 13: Cloud platforms Exercise 14: Querying a database Exercise 15: Which type of database?

In this chapter, we'll discuss ways to explore and visualize data through dashboards. We'll discuss the elements of a dashboard and how to make a directed request for a dashboard. This chapter will also cover making ad hoc data requests and A/B tests, which are a powerful analytics tool that de-risk decision-making.

Exercise 1: Dashboards Exercise 2: Classifying dashboard elements Exercise 3: Improving a dashboard Exercise 4: Choosing the right dashboard Exercise 5: Ad hoc analysis Exercise 6: Filling out an ad hoc request Exercise 7: Classifying requests Exercise 8: A/B Testing Exercise 9: Creating an A/B testing workflow Exercise 10: Sample size Exercise 11: Intermediate results

In this final chapter, we'll discuss the buzziest topic in data science: machine learning! We'll cover supervised and unsupervised machine learning, and clustering. Then, we'll move on to special topics in machine learning, including time series prediction, natural language processing, deep learning, and explainable AI!

Exercise 1: Supervised Machine Learning Exercise 2: Supervised Learning ne zaman kullanılır Exercise 3: Özellikler ve etiketler Exercise 4: Model değerlendirme Exercise 5: Kümeleme Exercise 6: Supervised vs. unsupervised Exercise 7: Küme sayısı seçimi

Geçerli Egzersiz

Exercise 8: Machine Learning'de özel konular Exercise 9: Machine Learning görevlerini sınıflandırma Exercise 10: Duygu Analizi Exercise 11: Deep Learning ve Açıklanabilir AI Exercise 12: Doğru çözümü bulma Exercise 13: Deep Learning kullanmalı mıyım?