Career in AI
Memahami lanskap peluang karir di era AI. Pelajari berbagai keahlian AI Engineer, Data Scientist dan ML engineer. Perluas wawasan mengenai kompetensi wajib, rentang gaji dan cara membangun jenjang karir yang tepat di industri AI.
Materi Yang Dipelajari:
- AI job market overview di Indonesia
- Role dan responsibilities: AI Engineer vs Data Scientist
- Skill requirements untuk berbagai posisi AI
- Salary expectations dan career progression
- Building portfolio dan personal branding
- Interview preparation untuk AI roles
Python Basics
Foundation programming dengan Python untuk AI development. Memahami konsep dasar hingga implementasi praktis.
Materi Yang Dipelajari:
- Variables dan Data Types dalam Python
- User Input handling dan validasi
- Conditional Statements (if/else/elif)
- Looping dengan for dan while
- Data structures: Lists, Tuples, Dictionaries
- Functions: definition, parameters, return values
- Error handling dan debugging
- Introduction to Streamlit untuk UI
Tools & Technologies:
AI-Assisted Programming
Belajar coding dengan cara yang relevan dan menyenangkan. Mengintegrasikan AI tools untuk meningkatkan produktivitas dan kreativitas dalam development.
Materi Yang Dipelajari:
- Modern coding practices dan workflow
- Integration dengan AI coding assistants
- Creative problem solving techniques
- Building engaging projects
- Code collaboration dan best practices
- Making programming enjoyable dan sustainable
- GitHub Copilot dan AI pair programming
Tools & Technologies:
Workflow Automation dengan N8N
Menggunakan n8n untuk membuat workflow automation yang mengintegrasikan berbagai layanan AI tanpa perlu coding yang kompleks.
Materi Yang Dipelajari:
- Pengenalan platform N8N dan setup
- Creating automated workflows
- API integration tanpa coding
- AI service integration (OpenAI, Claude, dll)
- Real-world use cases dan applications
- Data transformation dan processing automation
- Scheduling, triggers, dan monitoring
- Building complex automation pipelines
Tools & Technologies:
Machine Learning
Foundation untuk memahami machine learning dari supervised hingga unsupervised learning dengan implementasi praktis.
Materi Yang Dipelajari:
- Introduction to Machine Learning concepts
- Supervised learning algorithms dan use cases
- Unsupervised learning: clustering, dimensionality reduction
- Model evaluation dan validation techniques
- Feature engineering dan data preprocessing
- Cross-validation dan model selection
- Overfitting prevention strategies
Tools & Technologies:
Deep Learning
Memahami neural networks dan deep learning architecture untuk complex pattern recognition dan AI applications.
Materi Yang Dipelajari:
- Neural network fundamentals dan architecture
- Backpropagation dan gradient descent
- Deep learning optimization techniques
- Regularization methods (dropout, batch normalization)
- Hyperparameter tuning strategies
- Deep learning frameworks introduction
- Model training dan validation best practices
Tools & Technologies:
PyTorch
Deep dive into PyTorch framework untuk deep learning development dengan hands-on implementation.
Materi Yang Dipelajari:
- PyTorch tensors dan automatic differentiation
- Building custom neural network architectures
- Training loops dan model checkpoints
- Data loading dan preprocessing dengan PyTorch
- GPU acceleration dan distributed training
- Model deployment dan optimization
- Transfer learning dengan pre-trained models
Tools & Technologies:
Visual Models
Computer vision dan generative AI models untuk image processing, object detection, dan image generation.
Materi Yang Dipelajari:
- Convolutional Neural Networks (CNN) architecture
- Image processing dan computer vision basics
- Object detection dan image classification
- Generative models untuk image creation
- Stable Diffusion dan image generation techniques
- Transfer learning untuk computer vision
- Real-world computer vision applications
Tools & Technologies:
Prompt Engineering & RAG
Advanced techniques untuk prompt engineering dan implementasi Retrieval Augmented Generation systems.
Materi Yang Dipelajari:
- Advanced prompt engineering techniques
- Few-shot dan zero-shot learning strategies
- Chain-of-thought prompting
- RAG architecture dan implementation
- Vector databases dan embeddings
- Document retrieval dan ranking systems
- Context injection strategies
- RAG evaluation dan optimization
Tools & Technologies:
Natural Language Processing (NLP)
Comprehensive NLP journey dari text processing dasar hingga advanced architectures seperti Transformers dan Speech Models.
Materi Yang Dipelajari:
- Text preprocessing dan tokenization
- Feature extraction techniques
- Word2Vec dan word embeddings
- RNN dan LSTM architectures
- Sequence-to-sequence models
- Attention mechanisms dan Transformer architecture
- BERT, GPT, dan modern LLMs
- Speech recognition dan processing
- Sentiment analysis dan text classification
Tools & Technologies:
Agentic AI
Building intelligent AI agents untuk automated workflows dan complex task execution.
Materi Yang Dipelajari:
- LLM application development fundamentals
- Agent architecture dan design patterns
- Task planning dan decomposition strategies
- Tool usage dan function calling
- Multi-agent systems dan collaboration
- Workflow automation dengan AI agents
- Safety mechanisms dan control systems
- Real-world agentic AI applications
Tools & Technologies:
Final Project
Sesi konsultasi 1-on-1 untuk review final project dan feedback teknis.
Yang Akan Didapat:
- 1-on-1 consultation session
- Personal project review dan feedback
- Technical guidance untuk final project
- Code review dan best practices suggestions
- Troubleshooting untuk technical issues
- Quick feedback untuk project improvement
Format:
Soft Skills
Career preparation dan professional skills untuk sukses transisi ke AI Engineer role di industri teknologi.
Materi Yang Dipelajari:
- CV Best Practices: Tailoring CVs to job descriptions, strategic keyword usage, quantifying achievements
- Portfolio Optimization: Showcasing practical skills, end-to-end project functionality, measurable results
- Coding Interview Strategies: Effective communication during coding challenges, problem breakdown techniques
- Technical Project Delivery: Presenting projects during interviews, structuring narrative around problem-solution-impact
- Communication Skills: Techniques for explaining complex technical concepts to diverse audiences
- Non-Technical Stakeholder Presentation: Translating technical details into clear business value
- Professional networking dalam industri AI
