20th August 2023
Why to Join This Course
Join Our Advanced Machine Learning Engineering Today!
Ready to embark on a transformative learning journey? Don’t miss this opportunity to gain valuable skills and elevate your career. Enroll in our comprehensive course today and join a community of learners dedicated to excellence. Take the first step towards unlocking your full potential!
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Curriculum for Advanced Machine Learning Engineering
Module 1: Supervised and Unsupervised Learning Algorithms (10 hours)
- Introduction to supervised and unsupervised learning
- Decision trees and ensemble methods
- Support Vector Machines (SVM)
- Clustering algorithms (e.g., K-means, DBSCAN)
- Dimensionality reduction techniques (e.g., PCA, t-SNE)
Module 2: Model Evaluation and Selection (10 hours)
- Evaluation metrics for classification and regression models
- Cross-validation and model validation techniques
- Bias-Variance trade-off and overfitting
- Hyperparameter tuning and model selection
- Ensembling and stacking methods
Module 3: Feature Engineering and Selection (10 hours)
- Feature extraction and transformation techniques
- Handling missing values and outliers
- Feature scaling and normalization
- Dimensionality reduction methods
- Feature selection algorithms (e.g., Lasso, Recursive Feature Elimination)
Module 4: Model Deployment and Scalability (10 hours)
- Model deployment strategies and considerations
- Containerization and cloud deployment platforms
- Scalable model architectures (e.g., distributed computing, GPU acceleration)
- Model monitoring and performance optimization
- Continuous integration and deployment (CI/CD) pipeline
For admission to this Professional Certificate course in Advanced Machine Learning Engineering Course, candidates should have:
- Candidates must possess at least a bachelor’s degree from a recognized institution.
- Basic programming skills are preferred, though not mandatory.
- A foundational understanding of mathematics will be advantageous.
- Access to a computer with an internet connection and required software tools is essential.
- Prior work experience is not required for enrollment in this course.
Upon completing the course, students will:
- Demonstrate proficiency in supervised and unsupervised learning algorithms.
- Apply various evaluation metrics and techniques for model selection effectively.
- Employ feature engineering and selection methods to improve model performance.
- Understand model deployment strategies and scalability considerations.
- Gain practical experience in ensembling and stacking for better predictive accuracy.