Autoplay
Autocomplete
Previous Lesson
Complete and Continue
Machine Learning
MATERI 1 - PENGENALAN MACHINE LEARNING
Deskripsi Mata Kuliah - [VIDEO] (4:38)
Road Maps Pembelajaran (Materi, Tugas, Quiz dan Zoom) - [Text]
Road Maps Pembelajaran - [VIDEO] (5:52)
Pengenalan Machine Learning - [PDF]
Paradigma Sistem Komputer OLD vs. NOW - [VIDEO] (5:43)
Hubungan antara AI, Machine Learning, Deep Learning dan Neural Network - [VIDEO] (5:38)
Cara Kerja Machine Learning - [VIDEO] (6:06)
Metode-Metode Dalam Machine Learning - [VIDEO] (8:56)
Quiz #1
Link GMeet Materi 1
MATERI 2 - TIPE DATA DAN PREPROCESSING PADA MACHINE LEARNING
Tipe Data dan PreProcessing Pada Machine Learnin - [PDF]
Tahapan PreProcessing - [VIDEO] (2:04)
Tipe Data Numeric dan Categorical Pada Machine Learning - [VIDEO] (6:05)
Tipe Data Time Series dan Teks - [VIDEO] (3:30)
PreProcessing - Integration dan Tranformation - [VIDEO] (6:31)
PreProcessing - Cleaning dan Reduction - [VIDEO] (5:26)
Quiz #2
MATERI 3 - DATASET
Dataset - [PDF]
Pengertian Dataset dan Bagian - Bagiannya - [VIDEO] (5:03)
Bagian - Bagian Contact Lens Dataset - [VIDEO] (3:22)
Tipe - Tipe Data Dalam Dataset - [VIDEO] (3:33)
Contoh "Labelled" dan "UnLabeleld" Dataset - [VIDEO] (8:51)
Training Set - Validation Set - Test Set - [VIDEO] (5:09)
Quiz #3
MATERI 4 - REGRESION IN MACHINE LEARNING
Regresion In Machine Learning - [PDF]
Pengantar Regression Analysis Dalam Machine Learning - [VIDEO] (4:17)
Link GMeet Materi 4
MATERI 5 - SIMPE REGRESION LINEAR
Simpe Regresion Linear - [PDF]
Pengertian Simple Linear Regression - [VIDEO]
Studi Kasus #1 : Analisa Berat Badan - [VIDEO] (5:21)
Studi Kasus #2 : Implementasi Linear Regression "Salary & Experience" di Python - [VIDEO] (7:11)
Kode Program Studi Kasus #2 - [CODE]
Studi Kasus 3 - Multi Linear Regression - "Total Belanja Per Minggu" - [VIDEO] (6:41)
Quiz #4
Tugas #1 : Simple Linear Regression "Fish Market" Dataset - [PDF]
MATERI 6 - CLASSIFICATION IN MACHINE LEARNING
Classification In Machine Learning - [PDF]
Penjelasan Classification Pada Machine Learning - [VIDEO] (1:24)
Link GMeet Materi 6
MATERI 7 – ALGORTIMA NAIVE BAYES
Algortima Naive Bayes - [PDF]
Studi Kasus #1 : "Weather Dataset" - [VIDEO] (7:48)
Studi Kasus #2 : "Car Dataset" Bagian 1 - [VIDEO] (8:58)
Studi Kasus #2 : "Car Dataset" Bagian 2 - [VIDEO] (9:34)
Studi Kasus #3 : Implementasi "User Dataset" di Python - [VIDEO] (4:25)
Kode Program Studi Kasus #3 - [CODE]
UTS
Penjelasan Soal UTS - [VIDEO] (0:52)
Soal UTS - [PDF]
Link Pengumpulan UTS - [Link G-Drive]
MATERI 8 - KNN [K-NEARSET NEIGHBOR]
Algortima KNN [K-Nearset Neighbor] - PDF
Pengantar Algoritma KNN - [VIDEO] (3:31)
Cara Kerja KNN - [VIDEO] (6:08)
Cara memilik K dan Kelebihan Kekurangan KNN - [VIDEO] (5:01)
Studi Kasus #1 : "Bad Good" Dataset - [VIDEO] (7:58)
Studi Kasus #2 : "User Data" Dataset dam Implementasi di Python - [VIDEO] (2:18)
Kode Program Studi Kasus #2 - [CODE]
Tugas #2 : KNN - "Age Income" Dataset
MATERI 9 - PENGANTAR KONSEP DECESION TREE
Pengantar dan Konsep Decesion Tree - [PDF]
Decesion Tree Sebagai Model Dalam Machine Learning - [VIDEO] (3:22)
Simbol - Simbol Decesion Tree - [VIDEO] (3:42)
Spliting Way Atribut Kategortikal dan Numerik - [VIDEO] (16:30)
Membangun Decesion Tree Dengan Algortima "HUNT" - [VIDEO] (9:48)
Algortima "HUNT" vs "ID3" - [VIDEO] (1:59)
Link GMeet Materi 9
MATERI 10 - MEMBANGUN DECESION TREE DENGAN ID3
Membangun Decesion Tree Dengan ID3 - [PDF]
Algortima ID3 - Menghitung Information Gain - [VIDEO] (7:51)
Algortima ID3 - Menghitung Impurity dengan Entropy Bagian 1 - [VIDEO] (10:34)
Algortima ID3 - Menghitung Impurity dengan Entropy Bagian 2 - [VIDEO] (3:02)
Penggunaan Tools WEKA untuk ID3 - [VIDEO] (7:58)
Bagaimana Cara Penanganan Atribut Dengan Value Numerik - [VIDEO] (2:28)
Quiz #5
Tugas #3 : ID3 - "Customer" Dataset Atribut Ketegorikal - [PDF]
Link GMeet Materi 10 - Menghitung Information Gain Atribut Numerik
MATERI 11 - CLUSTERING IN MACHINE LEARNING
Clustering In Machine Learning - [PDF]
Penjelasan Clustering di Dalam Machine Learning - [VIDEO] (1:30)
Link GMeet Materi 11
MATERI 12- ALGORITMA K-MEANS CLUSTERING
Algoritma K-Means Clustering - [PDF]
Pengantar Algortima K-Means Clustering - [VIDEO] (4:00)
Cara Kerja K-Means Clustering - [VIDEO] (8:02)
Studi Kasus Algortima K-Means Clustering Dataset Mahasiswa Bagian 1 - [VIDEO] (7:53)
Studi Kasus Algortima K-Means Clustering Dataset Mahasiswa Bagian 2 - [VIDEO] (7:49)
Implementasi K-Means Clustering dengan Dataset Mall_Customer pada Python - [VIDEO] (5:52)
Kode Program K-Means Clustering Dalam Python - [CODE]
MATERI 13 - ASSOCIATION RULE IN MACHINE LEARNING
Association Rule In Machine Learning - [PDF]
Penjelasan Association Rule Dalam Machine Learning - [VIDEO] (0:58)
Link GMeet Materi 13
MATERI 14 - ALGORITMA APRIORI
Algortima Apriori - [PDF]
Pengantar Association Rule - [VIDEO] (5:05)
Istilah - istilah dalam Association Rule - [VIDEO] (5:47)
Studi Kasus Algoritma Apriori - [VIDEO] (8:16)
Implementasi Algortima Apriori Dalam Python - [VIDEO] (6:34)
Kode Program dalam Pyhton - [CODE]
UAS
Versi Lengkap Buku - Machine Learning Book #1 - [PDF]
Penjelasan Soal UAS - [VIDEO] (0:54)
Soal UAS - [PDF]
Link Pengumpulan UAS - [Link G-Drive]
REVIEW
REVIEW DOSEN
Algoritma K-Means Clustering - [PDF]
Lesson content locked
If you're already enrolled,
you'll need to login
.
Enroll in Course to Unlock