video & text course
April 14, 2026
7 chapters · 28 lessons
machine learning engineering how tos
AI-generated course with 7 chapters and 28 lessons.
Includes theory, images, videos, an interactive quiz and a downloadable certificate.
Course Content
1
Technical Details
4 lessons
- Understanding Model Architectures (e.g., CNNs, RNNs, Transformers)
- Feature Engineering Techniques and Best Practices
- Hyperparameter Tuning and Optimization Algorithms
- Model Evaluation Metrics (e.g., Precision, Recall, F1-score, AUC)
2
Real Life Samples
4 lessons
- Predictive Maintenance in Manufacturing
- Fraud Detection in Financial Services
- Personalized Recommendations in Streaming Services
- Medical Diagnosis Assistance
3
Usage in Ecommerce
4 lessons
- Product Recommendation Systems
- Dynamic Pricing Strategies
- Customer Churn Prediction
- Optimizing Search and Discovery
4
How to Build Charts and KPIs from Machine Learning
4 lessons
- Visualizing Model Performance Metrics over Time
- Creating Dashboards for A/B Test Results of ML Models
- Tracking Key Business Metrics Influenced by ML (e.g., conversion rate, revenue)
- Interpreting Feature Importance Visualizations
5
Deployment Strategies
4 lessons
- Containerization with Docker for ML Models
- Orchestration with Kubernetes for Scalable Deployments
- Serverless Deployment of ML APIs (e.g., AWS Lambda, Google Cloud Functions)
- A/B Testing ML Model Versions in Production
6
Monitoring and Maintenance
4 lessons
- Detecting Model Drift and Data Drift
- Setting up Alerting for Performance Degradation
- Retraining Strategies and Automation
- Ensuring Model Explainability in Production
7
Data Preprocessing for ML
4 lessons
- Handling Missing Data Techniques
- Feature Scaling and Normalization
- Encoding Categorical Variables
- Data Augmentation for Image and Text Data
