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Welcome to Dat’s Notebook
FUNDAMENTAL & THEORY
1. Mathematics
1.1. Linear Algebra
1.2. Calculus and Optimization
1.3. Distribution and Statistic
1.4. Information Theory
1.5. Graph Theory
2. Data Processing
2.1. Explore Data Analysis
2.1.1. Data Analyst Process
2.1.2. Read Datasets
2.1.3. Explore Data
2.1.4. Description Statistic
2.1.5. Inferential Statistic
2.1.6. Hypothesis Testing
2.1.7. ANOVA
2.2. Feature Engineering
2.2.1. Customize Transformer
2.2.2. Feature Extraction
2.2.3. Variable Overview
2.2.4. Missing Imputation
2.2.5. Categorical Encoding
2.2.6. Feature Transformation
2.2.7. Discretisation
2.2.8. Outliers Handling
2.2.9. Feature Scaling
2.2.10. Feature Selection
2.3. Imbalance Data Handling
2.3.1. Data-Level Approaches
2.3.2. Cost Sensitive Approaches
2.3.3. Ensemble Approaches
2.4. Feature Selection & Decomposition
2.4.1. Filter Methods
2.4.2. Wrapper Methods
2.4.3. Embedded Methods
2.4.4. Hybrid Methods
2.4.5. Feature Decomposition
2.5. Evaluation Metrics
2.5.1. List Metrics & Customize Metrics
2.5.2. Classification Metrics
2.5.3. Regression Metrics
2.5.4. Clustering Metrics
2.5.5. Pairwise Metrics
2.5.6. Recommendation Metrics
2.5.6.1. Overview
2.5.6.2. Similarity Metrics
2.5.6.3. Candidate Generation Metrics
2.5.6.4. Rating Metrics
2.5.6.5. Ranking Metrics
2.6. Model Monitoring
2.6.1. Overview
2.6.2. Functional level monitoring
2.6.3. Operation level monitoring
3. AI/ML Algorithms
3.1. ML Supervised Learning
3.2. ML Unsupervised Learning
3.3. DL Fundamental
3.3.1. Fundamentals
3.3.4. Data Augmentation
3.3.5. Transfer Learning
3.3.6. Multi-Modal
3.4. Computer Vision
3.4.1. CV Overview & CNN
3.5. NLP
3.5.1. NLP Foundation
3.5.1.1. Introduction
3.5.1.2. Text Cleaning
3.5.1.4. Embedding
3.5.1.5. Linguistic Features
3.5.1.6. Language Models
3.5.2. NLP Applications
3.5.2.1. Info Extraction
3.5.2.2. Text Classification
3.5.2.3. Text Generation
3.5.2.4. Sentiment Analysis
3.5.2.5. Topic Modelling
3.5.2.6. Generative AI
3.6. LLM
3.6.1. Transformer
3.6.2. LLM
3.6.3. Prompting
3.6.4. RAG
3.6.4.8. (Lab) Agentic RAG with CrewAI
3.6.4.9. (Lab) RAG from Scratch
3.6.4.10. (Lab) RAG with LangChain
3.6.4.11. (Lab) RAG with Webpage Data
3.6.4.12. (Lab) RAG with Youtube Video Data
3.6.5. LangChain
3.6.6. LlamaIndex
3.6.7. Agent
3.6.8. Fine-tune LLMs
3.6.9. Deployment LLMs
3.6.10. Function Calling
4. Topics
4.1. Recommendation System
TOOLS
1. System
1.1. AI/ML System (Linux)
1.2. Ubuntu MLOps Server Setup
2. Programming
2.1. Python
2.1.1. Python Basics
2.1.2. Concurrency & Parallelism
2.1.3. Python Environment
2.2. NoSQL
2.3. SQL
2.4. Bash
2.5. Linux Command
3. Frameworks
3.1. Pydantic
3.2. Pytest
3.3. SpaCy
3.4. TensorFlow (Updating)
3.5. PyTorch (Updating)
4. Engineering
4.1. DBT
4.2. Polars
4.3. PySpark
4.4. (GCP) Dataform
5. MLOps
5.1. Airflow
5.1.1. Overview
5.1.2. Workflow
5.1.3. DAGs & Tasks
5.1.4. Operator
5.1.5. Deployment
5.1.6. Monitor
5.2. Docker
5.3. Makefile
5.4. Git
5.5. Jenkins
5.6. Kubernetes
5.7. Terraform
5.8. Grafana
5.9. Prometheus
6. WebAPI
6.1. Streamlit
6.2. FastAPI
6.2.18. Small App Example
6.2.19. Big App Example
7. Storage
8. Cloud
8.1. AWS
8.2. GCP
8.2.1. Vertex AI
BLOGS
1. AI/ML
1.1. AI Agent
2. MLOps
2.1. ML Model Lifecycle Strategy
3. Data Engineering
3.1. Best Practice Data Engineering
3.2. Pull & Push API
4. Mindset
4.1. Code with AI
4.2. ML Project Structure
4.3. VSCode Dev Containers
4.4. Documents as Code
LABS
1. MLOps Labs
1.1. E2E MLOps System
.md
.pdf
Data Engineering Blogs
3.
Data Engineering Blogs
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