Machine Learning for Intermediate Level
Course Description
This course covers advanced techniques in machine learning for those looking to deepen their knowledge. You’ll gain hands-on experience with model tuning, evaluation metrics, and feature engineering, essential for building effective ML models in real-world applications.
Benefits of Attending
1. Gain a foundational understanding of ML
2. Hands-on practice with real-world data
3. Build confidence in working with ML tools
4. Understand basic algorithms and their applications
5. Obtain a certificate for completing the course
Who should attend ?
Data analysts, junior ML engineers, IT professionals
Prerequisites
Basic ML concepts, Python programming
Course Info
- Duration: 3 Days
- Modules: 7
- Prerequisites: No
Curriculums
Modules
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1. Feature Engineering
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2. Model Selection & Evaluation Metrics
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3. Advanced Supervised Learning
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4. Intro to Ensemble Methods
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5. Dimensionality Reduction Techniques
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6. Model Tuning and Hyperparameter Optimization
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7. Real-world Case Studies