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Course Outline
Konsep Lanjutan Machine Learning
Proyek Capstone
Pengantar ke Machine Learning dan Google Colab
Alur Kerja Proyek Machine Learning
Topik Khusus dalam Machine Learning
Ringkasan dan Langkah Berikutnya
Supervised Learning dengan Scikit-learn
Teknik Unsupervised Learning
- Algoritma klustering
- Pengurangan dimensi
- Pembelajaran aturan asosiasi
- Pra-pemrosesan data
- Seleksi model
- Implementasi model
- Menentukan pernyataan masalah
- Pengumpulan dan pembersihan data
- Pelatihan dan evaluasi model
- Feature engineering
- Pengaturan hyperparameter
- Interpretabilitas model
- Jaringan saraf dan deep learning
- Mesin vektor dukungan
- Metode ensemble
- Ulasan tentang pembelajaran mesin
- Menyiapkan Google Colab
- Penyegaran Python
- Model regresi
- Model klasifikasi
- Evaluasi dan optimisasi model
Requirements
Audience
- Memahami konsep pemrograman dasar
- Pengalaman dengan pemrograman Python
- Kenalan dengan konsep statistik dasar
- Ilmuwan data
- Pengembang perangkat lunak
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.
