← Computer Science & IT
Machine Learning
Master regression, classification, and ensemble techniques, then take models from feature engineering through evaluation and deployment.
What You'll Learn
- Linear/Logistic Regression
- Data Pipelines
- Evaluation Metrics
- Neural Network Basics
- Portfolio polish
Curriculum
5 modules · 20 topics covered
1
Module 1: ML Foundations
- Linear/Logistic Regression
- Decision Trees
- SVM
- Ensemble Methods
2
Module 2: Feature Engineering
- Data Pipelines
- Encoding Techniques
- Feature Scaling
- Feature Selection
3
Module 3: Model Ops
- Evaluation Metrics
- Model Explainability
- Hyperparameter Tuning
- Model Deployment
4
Module 4: Deep Learning & Advanced Topics
- Neural Network Basics
- Intro to CNNs & RNNs
- Transfer Learning
- Model Monitoring in Production
5
Module 5: Career & Portfolio
- Portfolio polish
- LinkedIn & branding
- ATS-ready resume
- Mock interviews & feedback
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Quick Facts
- Department
- Computer Science & IT
- Modules
- 5
- Topics
- 20
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