Dimensionality Reduction for Machine Learning

AdvancedLevel
6038+Students Enrolled
30 MinsDuration
4.9Average Rating

About
Learning Outcomes
Who Should Enroll
Course Curriculum
Instructors

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

Meet the instructor

Our instructor and mentors carry years of experience in data industry

Kunal Jain

Founder & CEO, Analytics Vidhya

Kunal has 15+ years of experience in the field of Data Science and is the founder and CEO of Analytics Vidhya- the world's 2nd largest Data Science community.

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

Industry-Recognized Credential
Career Advancement Credential
Shareable Achievement

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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