Graduate Certificate in Dimensionality Reduction in Feature Engineering
-- viewing nowThe Graduate Certificate in Dimensionality Reduction in Feature Engineering is a comprehensive course that addresses the critical need for data professionals to master feature engineering and dimensionality reduction techniques. In an era of big data and AI, these skills are in high demand across industries, with companies seeking experts who can turn raw data into actionable insights.
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Course Details
• Introduction to Dimensionality Reduction in Feature Engineering
• Linear Dimensionality Reduction Techniques: Principal Component Analysis (PCA)
• Advanced Linear Dimensionality Reduction: Linear Discriminant Analysis (LDA)
• Non-Linear Dimensionality Reduction: t-Distributed Stochastic Neighbor Embedding (t-SNE)
• Non-Linear Dimensionality Reduction: Autoencoders in Feature Engineering
• Feature Selection vs. Dimensionality Reduction
• Dimensionality Reduction for Text Data: Latent Dirichlet Allocation (LDA)
• Evaluation Metrics for Dimensionality Reduction Techniques
• Real-World Applications of Dimensionality Reduction in Feature Engineering
Career Path
Entry Requirements
- Basic understanding of the subject matter
- Proficiency in English language
- Computer and internet access
- Basic computer skills
- Dedication to complete the course
No prior formal qualifications required. Course designed for accessibility.
Course Status
This course provides practical knowledge and skills for professional development. It is:
- Not accredited by a recognized body
- Not regulated by an authorized institution
- Complementary to formal qualifications
You'll receive a certificate of completion upon successfully finishing the course.
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