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 dan piramida data

Klarifikasi terminologi

Piramida kebutuhan data

Urutkan kebutuhan piramida data

Options

Machine Learning in production

Prototyping & Testing ML

Analysis

Cocokkan tugas dalam piramida data

Prinsip Machine Learning

Jenis pemodelan

Kasus penggunaan Machine Learning

Supervised Machine Learning

Unsupervised Machine Learning

Temukan kasus supervised dan unsupervised

Peran pekerjaan, alat, dan teknologi

Tanggung jawab peran pekerjaan

Butir peta jalan analytics

Data Analyst

Data Scientist

Machine Learning Engineer

Cocokkan proyek data dengan peran pekerjaan

Struktur organisasi data

Terpusat

Terde-sentralisasi

Hibrida

Jenis struktur tim

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. 

Prediction vs. inference dilemma

Inference and prediction differences

Identify inference vs. prediction use cases

Inference (causal) models

Experiments and causal models

Identify non actionable variables

Prediction models (supervised learning)

Supervised modeling principles

Identify classification and regression models

Prediction models (unsupervised learning)

Unsupervised modeling use cases

Classification, regression or unsupervised models

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.

Klarifikasi terminologi

Machine Learning untuk Bisnis

Latihan interaktif praktis