Kursus Skripsi Tesis Disertasi Machine Learning | Training Machine Learning Master Class Selama 3 Hari

Silabus 3 Hari Machine Learning Master Class

Durasi: 3 hari (09:00 – 16:00)
Istirahat: 12:00 – 13:00
Level: Master Class (Tingkat Lanjut)
Metode: Teori, Hands-on Coding, Studi Kasus, Diskusi

🗓 Hari 1: Fundamentals & Feature Engineering

Sesi 1: Introduction to Advanced Machine Learning (09:00 – 10:30)

• Tren terbaru dalam Machine Learning
• Perbedaan Traditional ML vs. Deep Learning
• Pipeline Machine Learning yang optimal
• Best practices dalam pengolahan dataset besar

Sesi 2: Feature Engineering & Data Preprocessing (10:30 – 12:00)

• Data preprocessing untuk model high-performance
• Teknik Feature Engineering: Scaling, Encoding, Transformasi
• Feature Selection & Dimensionality Reduction (PCA, LDA)

🔹 Istirahat (12:00 – 13:00)

Sesi 3: Model Selection & Hyperparameter Tuning (13:00 – 14:30)

• Pemilihan model optimal berdasarkan karakteristik data
• Grid Search vs. Random Search vs. Bayesian Optimization
• Tuning model untuk meningkatkan akurasi

Sesi 4: Model Evaluation & Interpretability (14:30 – 16:00)

• Evaluasi model dengan metrik yang tepat
• SHAP & LIME untuk interpretasi model
• Model deployment considerations

🗓 Hari 2: Deep Learning & Computer Vision

Sesi 1: Introduction to Deep Learning & Neural Networks (09:00 – 10:30)

• Arsitektur Neural Networks (CNN, RNN, Transformer)
• Optimasi model Deep Learning (Adam, RMSprop, SGD)
• Implementasi Feedforward Neural Network (Hands-on)

Sesi 2: Computer Vision with Convolutional Neural Networks (CNN) (10:30 – 12:00)

• Konsep CNN: Convolution, Pooling, Fully Connected Layer
• Implementasi CNN untuk klasifikasi gambar
• Transfer Learning dengan ResNet, EfficientNet

🔹 Istirahat (12:00 – 13:00)

Sesi 3: Advanced Deep Learning Techniques (13:00 – 14:30)

• Attention Mechanism & Transformer Model (Vision Transformer, ViT)
• Generative Adversarial Networks (GANs) untuk Synthetic Data
• Teknik Fine-Tuning untuk Custom Dataset

Sesi 4: Object Detection & Image Segmentation (14:30 – 16:00)

• Implementasi YOLO, SSD, dan Faster R-CNN
• Image Segmentation dengan U-Net dan Mask R-CNN
• Hands-on: Object Detection pada dataset real

🗓 Hari 3: NLP, Time-Series Forecasting & Deployment

Sesi 1: Natural Language Processing (NLP) & Transformers (09:00 – 10:30)

• Teknik NLP modern (Word Embeddings, Transformers, BERT, GPT)
• Sentiment Analysis & Text Classification
• Hands-on: Implementasi Transformer Model

Sesi 2: Time-Series Forecasting & Anomaly Detection (10:30 – 12:00)

• Teknik forecasting dengan LSTM dan ARIMA
• Anomaly detection menggunakan Autoencoders
• Hands-on: Prediksi data time-series

🔹 Istirahat (12:00 – 13:00)

Sesi 3: Model Deployment & MLOps (13:00 – 14:30)

• Model deployment dengan Flask & FastAPI
• Model monitoring dan CI/CD dalam MLOps
• Hands-on: Deployment model ke cloud

Sesi 4: Case Study & Capstone Project (14:30 – 16:00)

• Studi kasus real-world dari berbagai industri
• Implementasi end-to-end project
• Presentasi hasil & evaluasi

💡 Output dari Master Class:
✅ Pemahaman mendalam tentang Machine Learning & Deep Learning
✅ Hands-on proyek dengan dataset real
✅ Teknik optimalisasi model & deployment best practices
✅ Studi kasus industri untuk penerapan nyata