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Course
- Overview
- Syllabus
- Schedule
- 4-Day Course
- Course Project
- Resources
- GitHub
- Install Python
- Install Packages
Exams
Data Engineering
- Overview
- 1️⃣ Gather Data
- 2️⃣ Statistics
- 3️⃣ Visualize
- 4️⃣ Cleanse
- 5️⃣ Features
- 6️⃣ Balance
- 7️⃣ Scale
- 8️⃣ Split
- 9️⃣ Deploy
Classification
Supervised Learning
- AdaBoost
- Decision Tree
- k-Nearest Neighbors
- Logistic Regression
- Naïve Bayes
- Neural Network Classifier
- Random Forest
- Stochastic Gradient Descent
- Support Vector Classifier
- XGBoost Classifier
Unsupervised Learning
Regression
- Overview
- Linear Regression
- k-Nearest Neighbors
- Support Vector Regressor
- Gaussian Processes
- Neural Network Regressor
- XGBoost Regressor
Time-Series
Computer Vision
Applications
Additive Manufacturing 📈📊 Automation with LSTM ⏱️ Automotive Monitoring 📈📊 Battery Life⏱️📈 Bit Classification 👁️📊 Concrete Strength 📈📊 Draw Classification 📊 Facial Recognition 👁️📊 Hand Tracking 👁️ IOT/OT Cybersecurity ⏱️📊 Lithium-ion Batteries 📊 Polymer Regression 📈 Road Detection 👁️📊 Safety Surveillance 👁️ Soil Classification 👁️📊 Sonar Detection 📊 Steel Plate Defects 📊 Texture Classification 👁️📊 Thermophysical Properties 📈 Wind Power ⏱️📈 📈=Regression 📊=Classification ⏱️=Time Series 👁️=Computer Vision 🎧=Audio
- Overview
- TCLab Help
- 1. Overview
- 2. Import Data
- 3. Statistics
- 4. Visualize
- 5. Prepare Data
- 6. Regression
- 7. Features
- 8. Classification
- 9. Interpolation
- 10. Solve Equations
- 11. Differential Equations
- 12. Time Series
- Final TCLab Project
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