Machine learning is used in many different industries and fields. It can fundamentally improve the business if applied correctly. This chapter outlines machine learning use cases, job roles and how they fit in the data needs pyramid.

Machine learning and data pyramid

Terminology clarification

Order data pyramid needs

Match tasks in data pyramid

Machine learning principles

Modeling types

Find supervised and unsupervised cases

Job roles, tools and technologies

Job role responsibilities

Match data projects with job roles

Team structure types

Machine learning and data use cases

This chapter overviews different machine learning types. We will look into differences between causal and prediction models, explore supervised and unsupervised learning, and finally understand the sub-types of supervised learning: classification and regression. 

Dilema prediksi vs. inferensi

Perbedaan inference dan prediction

Options

Inference (Causal)

Prediction

Identifikasi use case inferensi vs. prediksi

Model inferensi (kausal)

Eksperimen dan model kausal

Identifikasi variabel yang tidak dapat ditindaklanjuti

Model prediksi (supervised learning)

Prinsip pemodelan supervised

Model machine learning

Classification

Regression

Identifikasi model classification dan regression

Model prediksi (pembelajaran tanpa pengawasan)

Contoh penerapan pemodelan tanpa supervisi

Use case Machine Learning

Klasifikasi

Regresi

Unsupervised learning

Klasifikasi, regresi, atau model unsupervised

Machine learning types

This chapter reviews key steps in scoping out business requirements, identifying and sizing machine learning opportunities, assessing the model performance, and identifying any performance risks in the process.

Business requirements

Identify situation, opportunity and action

Identify successful experiments

Model training

Model training process

Training, validation and test

Model performance measurement

Poor performance examples

Identify performance metrics

Machine learning risks

Fixing non performing models

Non-actionable models

Identify actionable recommendations

Business requirements and model design

This chapter will look into the best and worst practices of managing machine learning projects. We will identify most common machine learning mistakes, learn how to manage communication between the business and ML teams and finally address the challenges when deploying machine learning models to production.

Machine learning mistakes

Identify machine learning mistakes

Data needs pyramid

Match ML mistakes by their types

Communication management

Business communication focus

Market testing

Machine learning in production

Production systems

Production systems ML use cases

ML in production launch

Wrap-up

Managing machine learning projects

Kursus ini akan memperkenalkan elemen-elemen kunci Machine Learning kepada para pemimpin bisnis. Kita akan berfokus pada wawasan inti dan praktik dasar tentang cara menyusun pertanyaan bisnis sebagai proyek pemodelan bersama tim Machine Learning. Anda akan memahami berbagai jenis model, pertanyaan bisnis apa yang dapat mereka bantu jawab, serta peluang apa yang dapat mereka ungkap, dan juga belajar mengidentifikasi situasi ketika Machine Learning TIDAK boleh diterapkan, yang sama pentingnya. Anda akan memahami perbedaan antara inferensi dan prediksi, memprediksi probabilitas dan jumlah, serta bagaimana penggunaan unsupervised learning dapat membantu membangun strategi segmentasi pelanggan yang bermakna.

<h2> Pelajari Dasar-Dasar Machine Learning </h2>
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Kursus ini akan memperkenalkan unsur-unsur utama machine learning kepada para pemimpin bisnis. Kami akan fokus pada wawasan kunci dan praktik dasar dalam merumuskan pertanyaan bisnis sebagai proyek pemodelan bersama tim machine learning. 
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<h2> Telusuri Detail Model </h2>
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Anda akan memahami berbagai jenis model, jenis pertanyaan bisnis apa yang dapat mereka bantu jawab, atau jenis peluang apa yang dapat mereka temukan. Selain itu, Anda juga akan belajar mengidentifikasi situasi di mana machine learning TIDAK SEHARUSNYA diterapkan, yang sama pentingnya. Anda akan memahami perbedaan antara inferensi dan prediksi, memprediksi probabilitas dan jumlah, serta bagaimana penggunaan pembelajaran tanpa pengawasan dapat membantu membangun strategi segmentasi pelanggan yang bermakna.

Model prediksi (pembelajaran tanpa pengawasan)

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