Duration
The programme is available in two duration modes:
Fast track - 1 month
Standard mode - 2 months
Course fee
The fee for the programme is as follows:
Fast track - 1 month: £140
Standard mode - 2 months: £90
Assessment mode
Assignments or Quiz
Tutor support available
International Students can apply
Students from over 90 countries
Flexible study
Study anytime, from anywhere
Overview
Dimensionality Reduction for Feature Engineering Certification:
Master advanced techniques in dimensionality reduction for feature engineering with our comprehensive certificate program. Ideal for data scientists, machine learning engineers, and AI professionals looking to enhance model performance and interpretability. Dive deep into PCA, t-SNE, and autoencoders to extract meaningful insights from high-dimensional data. Gain hands-on experience with real-world datasets and industry-relevant projects. Stay ahead in the competitive field of data science with this specialized certification. Take the next step in your career and enroll today!
Dimensionality Reduction for Feature Engineering is a crucial aspect of data science training that can greatly impact the accuracy and efficiency of machine learning models. Our Advanced Certificate program offers hands-on projects, real-world examples, and practical skills to help you master this essential technique. Dive deep into the world of data analysis and learn how to extract valuable insights from complex datasets. With our self-paced learning format, you can study at your own convenience while receiving expert guidance from industry professionals. Elevate your data analysis skills and boost your career prospects with this comprehensive course.Get free information
Course structure
• Introduction to Dimensionality Reduction in Feature Engineering • Principal Component Analysis (PCA) for Dimensionality Reduction • Linear Discriminant Analysis (LDA) for Feature Engineering • t-Distributed Stochastic Neighbor Embedding (t-SNE) for Dimensionality Reduction • Autoencoders for Feature Engineering in Machine Learning • Feature Selection Techniques for Dimensionality Reduction • Non-negative Matrix Factorization (NMF) for Feature Engineering • Singular Value Decomposition (SVD) for Dimensionality Reduction • Deep Learning Approaches for Feature Engineering • Applications of Dimensionality Reduction in Real-world Data AnalysisOur Advanced Certificate in Dimensionality Reduction for Feature Engineering is designed to equip you with the necessary skills to excel in data science and machine learning. Through this program, you will master advanced techniques in dimensionality reduction, enabling you to effectively preprocess data for model building and analysis. You will learn how to apply various algorithms such as PCA, t-SNE, and LDA to reduce the number of features while preserving essential information.
The duration of this certificate program is 10 weeks, with a self-paced learning format that allows you to study at your convenience. Whether you are a beginner looking to enhance your data science skills or an experienced professional seeking to stay updated with the latest trends, this program is tailored to meet your needs. By the end of the course, you will have a solid understanding of dimensionality reduction techniques and their applications in real-world scenarios.
This certificate is highly relevant to current trends in the data science field, as feature engineering plays a crucial role in model performance and interpretability. Employers are increasingly seeking professionals with expertise in dimensionality reduction as part of their data science teams. By enrolling in this program, you will be aligning yourself with modern tech practices and gaining a competitive edge in the job market.
Advanced Certificate in Dimensionality Reduction for Feature Engineering
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| 2018 | 67% |
| 2019 | 72% |
| 2020 | 87% |