DevCoach 186: Machine Learning | Unsupervised Learning: Hidden Clustering & Pattern Discovery

Sesi ini akan membahas Unsupervised Learning dalam Machine Learning, dengan fokus pada penerapan clustering. Peserta akan diperkenalkan pada konsep dasar Unsupervised Learning, jenis-jenis clustering seperti K-Means, DBSCAN, dan Hierarchical Clustering, serta algoritma yang digunakan dalam membangun model. Selain itu, akan dijelaskan bagaimana evaluasi model clustering dilakukan menggunakan metode seperti Elbow Method dan Silhouette Score untuk menentukan jumlah cluster yang optimal. Sesi ini juga mencakup strategi optimasi model, termasuk pemilihan parameter yang tepat dan teknik peningkatan performa clustering. Melalui sesi ini, peserta akan memahami bagaimana Unsupervised Learning diterapkan dalam dunia nyata, terutama dalam analisis data yang lebih efektif. 🚀

Transcript

1. Machine Learning Muhammad Fikry Rizal Curriculum Developer Machine Learning Unsupervised Learning: Hidden Clustering and Pattern Discovery
2. Hi, I’m M. Fikry Rizal 👋 Latest Work Experiences: • AI/ML Curriculum Developer, Dicoding 2024 - present • Data Engineer Intern, Torche Education 2022 - 2023 Education: • UIN Syarif Hidayatullah Jakarta 2020 - 2024 Bachelor Degree, Physics • Bangkit Academy 2023 2023 Machine Learning About Me Muhammad Fikry Rizal https://github.com /mfikryrz Machine Learning
3. Machine Learning Machine Learning Overview 1. Hi, Machine Learning! 2. Machine Learning Workflow 3. Supervised Learning: Klasifikasi 4. Supervised Learning: Regresi 5. Unsupervised Learning - Clustering 6. Feature Engineering 7. Hyperparameter Tuning 8. Overfitting & Underfitting
4. Unsupervised Learning Concept Machine Learning
5. Unsupervised Learning Concept Machine Learning
6. Machine Learning Types of Clustering Methods Hierarchical Clustering Non-hierarchical Clustering NHC
7. Machine Learning KMeans Clustering: The Core of Unsupervised Learning
8. Machine Learning KMeans Clustering: The Core of Unsupervised Learning
9. Hands-on Coding: Let's Build It Machine Learning
10. Quiz #1 DevCoach 186 Machine Learning Format jawaban: #quiz1-username-jawaban
11. Quiz #1 DevCoach 186 Ketika bekerja dengan algoritma clustering, seperti KMeans, apa saja pendekatan yang dapat digunakan untuk menentukan jumlah cluster secara optimal? Pilih semua opsi yang benar.) Machine Learning a). Elbow Method b). Menggunakan Gap Statistic
12. Machine Learning Feature Engineering & Hyperparameter Tuning
13. Machine Learning Feature Engineering: Enhancing Data for Better Models
14. Machine Learning Feature Engineering: Feature Selection
15. Machine Learning Feature Engineering: Feature Selection
16. Machine Learning Feature Engineering: Feature Extraction
17. Machine Learning Feature Engineering: Feature Extraction
18. Machine Learning Feature Engineering: Feature Extraction
19. Machine Learning Hyperparameter Tuning: Grid Search vs Random Search
20. Machine Learning Tuning Is Done, Now Letʼs Validate the Model Like a Pro • Does hyperparameter tuning improve model performance? Compare performance metrics before and after tuning to ensure that the tuning process provides a significant improvement. • Is the model overfitting or underfitting? Ensure that the model performs well on test data and not just on training data. • Are there other metrics that should be considered? Sometimes, accuracy alone is not enough. Use additional metrics such as precision, recall, or F1-score to gain a more comprehensive understanding of the model's performance. • A thorough evaluation ensures that the model produced after tuning Not only performs well on the training dataset but can also generalize effectively to new data.
21. Quiz #2 DevCoach 186 Machine Learning Format jawaban: #quiz2-username-jawaban
22. Quiz #2 DevCoach 186 Machine Learning a). Melakukan standarisasi pada outlier. b). Menghapus data outlier. c). Mengganti nilai outlier dengan nilai Q1 dan Q3. Berikut ini pilihlah opsi yang BUKAN teknik digunakan untuk menangani outliers dalam feature engineering?
23. Machine Learning Underfitting to Overfitting: Finding the Sweet Spot in Model Training
24. Machine Learning Underfitting to Overfitting: Finding the Sweet Spot in Model Training
25. Machine Learning Methods to Identify Overfitting and Underfitting in Machine Learning
26. Machine Learning Techniques to Overcome Overfitting: Regularization
27. Machine Learning Techniques to Overcome Underfitting: Using More Complex Models
28. Hands-on Coding: Let's Build It Machine Learning
29. Quiz #3 DevCoach 186 Machine Learning Format jawaban: #quiz3-username-jawaban
30. Quiz #3 DevCoach 186 Anda menggunakan learning curves untuk menganalisis model machine learning Anda. Jika learning curves menunjukkan bahwa error training stabil rendah, tetapi error validation tinggi dan menurun, apa indikasi utama dari model Anda? Machine Learning a). Model mengalami underfitting. b). Model mengalami overfitting.
31. Thank You Machine Learning
32. Feedback! dicoding.id/devcoachfeedback Machine Learning