Dimensionality Reduction for Machine Learning
AdvancedLevel
1252+Students Enrolled
30 MinsDuration
4.9Average Rating
About this Course
- Learn key dimensionality reduction techniques like PCA and Factor Analysis to simplify complex data and improve machine learning model accuracy.
- Gain hands-on experience with practical examples, exploring how to visualize high-dimensional data and choose the right reduction method.
- Understand the trade-offs of dimensionality reduction, balancing information loss with model performance for real-world data science applications.
Learning Outcomes
Master PCA
Understand and apply Principal Component Analysis.
Factor Analysis
Discover techniques to visualize data patterns for ML models.
Model Optimization
Enhance model accuracy through data simplification.
Data Visualization
Visualize high-dimensional data with clarity and insight.
Who Should Enroll
- Aspiring Data Scientists: Learn dimensionality reduction to manage and analyze complex datasets.
- AI/ML Practitioners: Master techniques to boost model accuracy and reduce computational costs.
- Data Enthusiasts: Gain skills to simplify data, uncover patterns, and make analysis more effective.
Course Curriculum
Master PCA, Factor Analysis, and t-SNE. Learn to preprocess data, visualize patterns, and improve ML models with real-world examples and hands-on practice.
1. Introduction
2. AI&ML Blackbelt Plus Program
1. What is Dimensionality Reduction?
2. Why is Dimensionality Reduction required?
3. Common Dimensionality Reduction Techniques
1. Missing Value Ratio
2. Missing Value Ratio Implementation
3. Low Variance Filter
4. Low Variance Filter Implementation
5. High Correlation Filter
6. Backward Feature Elimination
7. Backward Feature Elimination Implementation
8. Forward Feature Selection
9. Forward Feature Selection Implementation
10. Random Forest
1. Introduction to the Module
2. Factor Analysis
3. Principal Component Analysis
4. Independent Component Analysis
1. Understanding Projection
2. Understanding ISOMAP
3. t- Distributed Stochastic Neighbor Embedding (t-SNE)
4. Undserstanding UMAP
Get this Course Now
With this course you’ll get
- 30 Mins
Duration
- Kunal Jain
Instructor
- Advanced
Level
Certificate of completion
Earn a professional certificate upon course completion
- Globally recognized certificate
- Verifiable online credential
- Enhances professional credibility
Frequently Asked Questions
Looking for answers to other questions?
This course is designed for anyone who wants to learn about the different dimensionality reduction techniques, such as PCA and Factor Analysis. So if you’re a newcomer to machine learning and want to understand how to work with a dataset containing multiple features, this course is for you!
Absolutely! We have designed the course in a way that will cater to newcomers and beginners in machine learning. Having basic knowledge about machine learning algorithms will be hugely beneficial for your learning.
This course is free of cost!
Once you register, you will have 6 months to complete the course. If you visit the course 6 months after your initial registration, you will need to enroll in the course again. Your past progress will be lost.
You can complete the “Dimensionality Reduction for Machine Learning” course in a few hours.
We regularly update the “Dimensionality Reduction for Machine Learning” course and hence do not allow videos to be downloaded. You can visit the free course anytime to refer to these videos.
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