Day 16: SVM Explained Simply | Hyperplane, Margin & Kernel Trick
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Day 16: SVM Explained Simply | Hyperplane, Margin & Kernel Trick
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【機器學習】監督式學習 | 支援向量機 | 核函數 | 不同Kernel的SVM鳶尾花分類探討，挖掘機器學習的奧秘 #svm #machinelearning #iris #dataanalysis
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【機器學習】監督式學習 | 支援向量機 | 核函數 | 不同Kernel的SVM鳶尾花分類探討，挖掘機器學習的奧秘 #svm #machinelearning #iris #dataanalysis
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YouTubeJoyous 工程師の師
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Types of Machine Learning (ML) Systems: 1. Based on Learning Style: a. Supervised Learning Learns from labeled data. Goal: Predict output for new inputs. Common tasks: Classification, Regression. Examples: Linear Regression, Decision Trees, SVM. b. Unsupervised Learning Learns from unlabeled data. Goal: Discover hidden patterns. Common tasks: Clustering, Dimensionality Reduction. Examples: K-Means, PCA, DBSCAN. c. Semi-Supervised Learning Uses a small amount of labeled data large unlabeled data.
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📊 Analisis Sentimen Komentar YouTube Kasus pengungkapan penyebar CCTV oleh Inara dianalisis menggunakan berbagai model machine learning. Hasilnya? SVM menjadi model paling optimal, dan mayoritas komentar publik cenderung bersentimen positif, meskipun sentimen negatif tetap menunjukkan pola konflik yang kuat. Analisis sentimen ini menunjukkan bahwa pemilihan model sangat berpengaruh terhadap tingkat akurasi klasifikasi. SVM dan Regresi Logistik terbukti lebih mampu menangkap pola bahasa komentar
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