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Course Outline
- Pengenalan ke Machine Learning
- Mesin pembelajaran sebagai bagian dari Kecerdasan Buatan
- Jenis-jenis ML
- Algoritma ML
- Tantangan dan potensi penggunaan ML
- Overfitting dan trade-off bias-variance dalam ML
- Teknik Mesin Pembelajaran
- Workflow Machine Learning
- Pembelajaran terawasi – Klasifikasi, Regresi
- Pembelajaran tidak terawasi – Pengelompokan, Deteksi anomali
- Pembelajaran semi-terawasi dan Reinforcement Learning
- Perhatian dalam Machine Learning
- Pra-pemrosesan Data
- Persiapan data dan transformasi
- Feature engineering
- Penyekalaan fitur
- Reduksi dimensi dan pemilihan variabel
- Visualisasi data
- Analisis eksploratif
- Kasus Studi
- Feature engineering lanjutan dan dampaknya terhadap hasil dalam regresi linier untuk prediksi
- Analisis deret waktu dan Forecasting volume penjualan bulanan - metode dasar, penyesuaian musiman, regresi, smoothing eksponensial, ARIMA, jaringan saraf
- Analisis keranjang pasar dan penambangan aturan asosiasi
- Analisis segmentasi menggunakan pengelompokan dan peta otomatis self-organising
- Klasifikasi pelanggan yang mungkin gagal bayar menggunakan regresi logistik, pohon keputusan, xgboost, svm
Requirements
Knowledge and awareness of Machine Learning fundmentals
14 Hours
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - 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.
