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Kerangka Materi
Algoritma Machine Learning dalam Julia
Konsep pendahuluan
- Pembelajaran bersupervisi dan tidak bersupervisi
- Validasi silang dan pemilihan model
- Kompromi bias/variansi
Regresi linear dan logistik
(NaiveBayes & GLM)
- Konsep pendahuluan
- Menyesuaikan model regresi linear
- Diagnostik model
- Naive Bayes
- Menyesuaikan model regresi logistik
- Diagnostik model
- Metode pemilihan model
Jarak
- Apa itu jarak?
- Euclidean
- Cityblock
- Cosine
- Correlation
- Mahalanobis
- Hamming
- MAD
- RMS
- Deviasi rata-rata kuadrat
Pengecilan dimensi
- Analisis Komponen Utama (PCA)
- PCA linear
- Kernel PCA
- PCA probabilistik
- CA independen
- Skalasi multidimensi
Metode regresi yang dimodifikasi
- Konsep dasar regularisasi
- Regresi Ridge
- Regresi Lasso
- Regresi Komponen Utama (PCR)
Klastering
- K-means
- K-medoids
- DBSCAN
- Klastering hierarkis
- Algoritma Klaster Markov
- Klastering Fuzzy C-means
Model machine learning standar
(Paket NearestNeighbors, DecisionTree, LightGBM, XGBoost, EvoTrees, LIBSVM)
- Konsep gradient boosting
- Tetangga terdekat K (KNN)
- Model pohon keputusan
- Model random forest
- XGboost
- EvoTrees
- Mesin vektor dukungan (SVM)
Jaringan saraf tiruan
(Paket Flux)
- Stochastic gradient descent & strategies
- Multilayer perceptrons forward feed & back propagation
- Regularization
- Recurrence neural networks (RNN)
- Convolutional neural networks (Convnets)
- Autoencoders
- Hyperparameters
Persyaratan
Kursus ini ditujukan bagi orang-orang yang sudah memiliki latar belakang dalam ilmu data dan statistik.
21 Jam
Testimoni (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Kursus - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
