learning di industri digital pengenalan machine
TRANSCRIPT
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Pengenalan Machine Learning di Industri DigitalAlim Hanif
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Outline
1. Penjelasan tentang Machine Learninga. Overviewb. Pendahuluanc. Modelling
2. Penerapan dalam Industri Digital3. Tips & Triks Mendalami Machine Learning4. Q&A
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ML-Overview
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Gambaran Khusus dari Machine Learning
sumber gambar: Davinson
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Pengertian DasarAI: Sebuah teknik rekayasa komputer untuk meniru pekerjaan manusia
ML: Merupakan bagian dari AI dimana kita menggunakan statistik untuk meningkatkan performa AI itu sendiri
DL: Bagian dari ML, dimana sudah menggunakan statistik yang lebih dalam, yaitu neural network.
sumber: Xaltius [link]
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Tipe task/pekerjaan yang dapat dilakukan oleh Machine Learning
Sumber: Shankar
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ML- Pendahuluan
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Pendahuluan
Metode standard menurut CRISP-DM:
1. Business Understanding2. Data Understanding3. Data Preparation4. Modeling5. Evaluation6. Deployment
sumber : datasciencecentral.com
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Business Understanding
Transformasi dari business knowledge menjadi machine learning problem
Poin penting:
1. Identifikasi masalah (business)2. Menentukan machine learning problem (i.e. supervise/unsupervised)
a. Memilih metode yang cocok dengan masalah tersebut [research/reading research paper]b. Tentukan parameter keberhasilan (online metrics/offline metrics)c. Kebutuhan data
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Data Understanding
Menentukan dan memahami data yang diperlukan untuk menyelesaikan masalah
Poin penting:
1. Mengambil data dari sumbernya2. Mencari insight dari data (Exploratory Data Analysis)
a. Tipe datab. Central Tendency (mean, median, mode)c. Skewnessd. dll.
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Data Preparation
Semua aktivitas untuk mempersiapkan data sehingga data siap dikonsumsi oleh model
Poin penting:
1. Data Gathering (Mengambil data dari sumbernya)2. Data Cleansing (handle outlier dan null value)3. Feature Engineering (transform, encode, etc)4. Feature Selection (uji korelasi)5. [optional] Normalisasi data6. Split Train Test data (Prinsip Pareto: 80/20)
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ML-Modelling
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Unsupervised Learning: Clustering
Algoritma yang sering dipakai:
1. Hierarchical Clustering2. K-Means (selain itu bisa juga K-Modes dan K-Median)3. DBscan4. dll.
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Unsupervised Learning
Hierarchical Clustering
Bagian penting:
1. Dendogram2. Teknik:
a. Agglomerativeb. Divisive
Sumber gambar: University of Cincinnati
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Unsupervised Learning
K-Means
Bagian penting:
1. Centroid2. Jumlah K
Evaluasi: Silhouette score
Sumber gambar: Google Developer
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Supervised Learning: Clustering
Algoritma dalam regresi (estimasi nilai):
1. Regresi Linear2. Tree-based Model
Algoritma dalam klasifikasi:
1. Regresi Logistik2. Tree-based model
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Supervised Learning
Regresi Linier
Asumsi:
1. Linieritas2. Normalitas Residual3. Non Outlier4. Homoskedastisitas5. Non Multikolinearitas6. Non Autokorelasi
Sumber gambar: Tran, H
sumber: statistikian.com
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Supervised Learning
Regresi Logistic
Asumsi:
1. Target (variable dependen) harus dikotom (tinggi vs rendah, berat vs ringan, dst)
Sumber gambar: javapoint.com
sumber: statistikian.com
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Supervised Learning
Decision Tree (Tree-based model)
Sumber gambar: synergy37AI
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Supervised Learning
Random Forest (decision Tree)
Catatan:
- Urutan node decision berbeda untuk tiap `tree` nya
Sumber gambar: mygreatlearning.com
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Evaluasi Model
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Evaluasi
Evaluasi dalam masalah klasifikasi (supervised learning)
Sumber gambar: chemicalstatistician
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Evaluasi
Confusion Matrix
Sumber gambar: Nugroho, K.S
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Evaluasi
Rumus Perhitungan Metrix
Sumber gambar: Shrivastav, N.
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Penerapan ML dalam industri digital
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Beberapa contoh task dalam industri digital
1. Fraud Detection: Mendeteksi kecurangan. [5 Top Startup yang menyediakan jasa ini]
2. Chatbot: klasifikasi masalah pada user. kata.ai menyediakan service ini3. Cluster Lokasi Driver Go-Jek: [sumber]
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Tips & Tricks belajar MLHarus belajar dari mana ya? kemana? dan dengan siapa?
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Skill yang perlu dipelajari
1. Pemahaman statistik dasara. Metode kuantitatif (regresi linear, logistic)b. Clustering (K-means, DBscan), Classification (K-NN)c. [Better to learn] Tree Algorithm: decision tree, random forest dst
2. Programming Skill a. Python atau Rb. Jupyter Notebook [Better to learn]
3. Problem Solving
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Belajar dimana?
1. Pemahaman statistik dasar -> perkuliahan, platform online (misal udemy, coursera, web/artikel, dll)
2. Programming Skill (Python atau R) -> perkuliahan, platform online (misalnya udemy, coursera, web/artikel, code-academy, dll)
3. Problem Solving -> perkuliahan (skripsi/ penelitian lain), platform online (misalnya Kaggle). contoh: Klasifikasi Pendapatan [Kaggle]