Machine Learning Muhammad Fikry Rizal Curriculum Developer Machine Learning Unsupervised Learning: Hidden Clustering and Pattern Discovery 

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 

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 

Unsupervised Learning Concept Machine Learning 

Unsupervised Learning Concept Machine Learning 

Machine Learning Types of Clustering Methods Hierarchical Clustering Non-hierarchical Clustering NHC 

Machine Learning KMeans Clustering: The Core of Unsupervised Learning 

Machine Learning KMeans Clustering: The Core of Unsupervised Learning 

Hands-on Coding: Let's Build It Machine Learning 

Quiz #1 DevCoach 186 Machine Learning Format jawaban: #quiz1-username-jawaban 

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 

Machine Learning Feature Engineering & Hyperparameter Tuning 

Machine Learning Feature Engineering: Enhancing Data for Better Models 

Machine Learning Feature Engineering: Feature Selection 

Machine Learning Feature Engineering: Feature Selection 

Machine Learning Feature Engineering: Feature Extraction 

Machine Learning Feature Engineering: Feature Extraction 

Machine Learning Feature Engineering: Feature Extraction 

Machine Learning Hyperparameter Tuning: Grid Search vs Random Search 

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. 

Quiz #2 DevCoach 186 Machine Learning Format jawaban: #quiz2-username-jawaban 

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? 

Machine Learning Underfitting to Overfitting: Finding the Sweet Spot in Model Training 

Machine Learning Underfitting to Overfitting: Finding the Sweet Spot in Model Training 

Machine Learning Methods to Identify Overfitting and Underfitting in Machine Learning 

Machine Learning Techniques to Overcome Overfitting: Regularization 

Machine Learning Techniques to Overcome Underfitting: Using More Complex Models 

Hands-on Coding: Let's Build It Machine Learning 

Quiz #3 DevCoach 186 Machine Learning Format jawaban: #quiz3-username-jawaban 

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. 

Thank You Machine Learning 

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