Machine Learning for Engineers

Course on GitHub

• Overview
• Syllabus
• Schedule
• 4-Day Course
• Course Project
• Resources
• Install Python
• Install Packages
• AI Ethics

Exams

• Midterm Exam

Data Engineering

• Overview
• 1️⃣ Gather Data
• 2️⃣ Statistics
• 3️⃣ Visualize
• 4️⃣ Cleanse
• 5️⃣ Features
• 6️⃣ Balance
• 7️⃣ Scale
• 8️⃣ Split
• 9️⃣ Deploy
• 🔟 Apps

Classification

• Overview
• Hyperparameters
• Cybersecurity

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

• Gaussian Mixture Model
• K-Means Clustering
• Spectral Clustering

Regression

• Overview
• Linear Regression
• k-Nearest Neighbors
• Support Vector Regressor
• Gaussian Processes
• Neural Network Regressor
• XGBoost Regressor

Time-Series

• ARX Model
• LSTM Network

Computer Vision

• Introduction
• Cascade Classifier
• Deep Learning

Applications

TCLab Project

• 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

Related Courses

• 🎓 Begin Python
• 🎓 Begin Matlab
• 🎓 Begin Java
• 🎓 Engineering Computing
• 🎓 Data Science
• 🎓 Data-Driven Engineering
• 🎓 Machine Learning
• 🎓 Control (MATLAB)
• 🎓 Control (Python)
• 🎓 Optimization
• 🎓 Dynamic Optimization

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