Haruskah saya menggunakan Deep Learning?

Patricia bekerja di sebuah bank dan ingin membantu perusahaannya memberikan pinjaman yang lebih cerdas. Ia ingin memprediksi probabilitas seorang peminjam gagal bayar berdasarkan informasi pada aplikasi mereka, seperti riwayat kredit, tingkat pendidikan, pendapatan, dan aset. Patricia kemudian akan menggunakan nilai ini untuk menentukan suku bunga pinjaman.

Patricia memiliki ratusan ribu contoh pinjaman dari sepuluh tahun terakhir.

Patricia menyampaikan idenya kepada departemen hukum, dan mereka mengingatkannya bahwa ada undang-undang tentang cara penentuan suku bunga yang melindungi peminjam dari diskriminasi berdasarkan usia, jenis kelamin, ras, dan faktor lainnya.

Berdasarkan informasi ini, apakah Patricia sebaiknya menggunakan Deep Learning atau Explainable AI untuk masalah ini?

Latihan ini adalah bagian dari kursus

Data Science untuk Bisnis

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Mulai berolahraga

Latihan ini adalah bagian dari kursus

Data Science untuk Bisnis

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4.8+

Mulai Kursus Gratis

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: Kapan menggunakan Supervised Learning Exercise 3: Fitur dan label Exercise 4: Evaluasi model Exercise 5: Clustering Exercise 6: Supervised vs. Unsupervised Exercise 7: Pemilihan jumlah klaster Exercise 8: Topik khusus dalam Machine Learning Exercise 9: Mengklasifikasikan tugas machine learning Exercise 10: Analisis Sentimen Exercise 11: Deep Learning dan Explainable AI Exercise 12: Menemukan solusi yang tepat Exercise 13: Haruskah saya menggunakan Deep Learning?

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