Learn All About the Power of Dimensionality Reduction
Have you worked on a dataset with more than a thousand features? How about 40,000 features? We are generating data at an unprecedented pace right now and working with massive datasets in machine learning projects is becoming mainstream.
This is where the power of dimensionality reduction techniques comes to the fore. Dimensionality reduction is actually one of the most crucial aspects in machine learning projects.
You can use dimensionality reduction techniques to reduce the number of features in your dataset without having to lose much information and keep (or improve) the model’s performance. It’s a really powerful way to deal with huge datasets, as you’ll see in this course!
Every data scientist, aspiring established, should be aware of the different dimensionality reduction techniques, such as Principal Component Analysis (PCA), Factor Analysis, tSNE, High Correlation Filter, Missing Value Ratio, among others.
So in this beginnerfriendly course, you will learn the basics of dimensionality reduction and why you should know dimensionality reduction in machine learning. We will also cover 12 dimensionality reduction techniques! This course is as comprehensive an introduction as you can get!
You should know the answer to the below questions on dimensionality reduction:
 What is dimensionality reduction?
 What are the different dimensionality reduction techniques?
 Why should I learn dimensionality reduction?
 I already know what Principal Component Analysis (PCA) is. Do I really need to learn more dimensionality reduction techniques?
 Do I need to have huge computational power to apply dimensionality reduction?
 What are the different applications of dimensionality reduction?
 What kind of machine learning projects can I apply dimensionality reduction on?
 Will learning about dimensionality reduction and techniques like PCA help me in clearing machine learning interviews?
 Is dimensionality reduction a supervised or unsupervised machine learning technique?
 What are the challenges of applying dimensionality reduction techniques?
Who is the Dimensionality Reduction for Machine Learning Course for?
This dimensionality reduction course is designed for machine learning folks who:
 Want to understand how to work with high dimensional data
 Are struggling to build machine learning models on a dataset with hundreds and thousands of features
 Want to explore the various dimensionality reduction techniques out there
 Are preparing for their machine learning journey
 Want to understand where dimensionality reduction fits in
Course curriculum

1
Introduction to the Course
 Introduction

2
Introduction to Dimensionality Reduction
 What is Dimensionality Reduction?
 Why is Dimensionality Reduction required?
 Common Dimensionality Reduction Techniques

3
Feature Selection Techniques
 Missing Value Ratio
 Missing Value Ratio Implementation
 Low Variance Filter
 Low Variance Filter Implementation
 High Correlation Filter
 Backward Feature Elimination
 Backward Feature Elimination Implementation
 Forward Feature Selection
 Forward Feature Selection Implementation
 Random Forest

4
Factor Based Feature Extraction Techniques
 Introduction to the Module
 Factor Analysis
 Principal Component Analysis
 Independent Component Analysis

5
Projection Based Feature Extraction Techniques
 Understanding Projection
 ISOMAP
 t Distributed Stochastic Neighbor Embedding (tSNE)
 UMAP
Common Questions Beginners Ask About Dimensionality Reduction for Machine Learning
What is dimensionality reduction?
Dimension Reduction refers to the process of converting a set of data having vast dimensions into data with lesser dimensions ensuring that it conveys similar information concisely. These dimensionality reduction techniques are typically used while solving machine learning problems to obtain better features for a classification or regression task.
What are the different dimensionality reduction techniques?
There are multiple dimensionality reduction techniques for machine learning. We will cover 12 such techniques in this course:
 Missing Value Ratio
 Low Variance Filter
 High Correlation Filter
 Random Forest
 Backward Feature Elimination
 Forward Feature Selection
 Factor Analysis
 Principal Component Analysis (PCA)
 Independent Component Analysis
 Methods Based on Projections
 tDistributed Stochastic Neighbor Embedding (tSNE)
 UMAP
Why should I learn dimensionality reduction?
That’s a fair question! Here are a few key reasons why every machine learning professional should know dimensionality reduction:
 Space required to store the data is reduced as the number of dimensions comes down
 Less dimensions lead to less computation/training time
 Some algorithms do not perform well when we have large dimensions. So reducing these dimensions needs to happen for the algorithm to be useful
 Dimensionality reduction also takes care of multicollinearity by removing redundant features.
 We can visualize high dimensional data thanks to the various dimensionality reduction techniques such as tSNE
We could go on, but you get the point! Dimensionality reduction is a crucial cog in the machine learning project lifecycle.
I already know what Principal Component Analysis (PCA) is. Do I really need to learn more dimensionality reduction techniques?
Absolutely. Principal Component Analysis (PCA) is a powerful dimensionality reduction technique but it does have its challenges. You should consider this course as a Siwss Army Knife in your dimensionality reduction skill set!
Learning the various dimensionality reduction techniques will help you become a better machine learning practitioner and expand your horizons when you’re working on different machine learning problems.
Do I need to have huge computational power to apply dimensionality reduction?
The question on computational power varies from the volume of data we have. For a dataset containing millions of records and thousands of dimensions one needs to have huge computational power to reduce the dimensions and make the data suitable for building models.
Where as if the dataset contains only less records and dimensions the computational power required will be very less. It all depends on the volume of data and how quickly you want the result.
What are the different applications of dimensionality reduction?
There are a plethora of applications of dimensionality reduction. Here’s just 3 of them:
 If the dataset has too many missing values, we use dimensionality reduction techniques to reduce the number of variables
 We can find the importance of each feature and keep the top most features, resulting in dimensionality reduction
 We can use dimensionality reduction to find highly correlated features and drop them accordingly
There are a whole lot more as you’ll see inside the course.
What kind of machine learning projects can I apply dimensionality reduction on?
You can apply dimensionality reduction techniques on any dataset with a ton of variables. That does not mean you should do it without considering the challenges (more on that later)!
We suggest heading over to the DataHack platform and picking up a project to apply these dimensionality reduction techniques on. After all, practice makes perfect!
Will learning about dimensionality reduction and techniques like PCA help me in clearing machine learning interviews?
Of course! As a newcomer or fresher in machine learning, you’ll be asked about how you would deal with massive datasets. It’s a common interview question and your knowledge about dimensionality reduction techniques like PCA and Factor Analysis will hold you in good stead.
Is dimensionality reduction a supervised or unsupervised machine learning technique?
Dimensionality reduction can be both supervised and unsupervised. The unsupervised techniques of Dimensionality Reduction is used when the dataset you have is humungous and is done prior to supervised dimensionality reduction technique. They include
 Principal Component Analysis
 Random Projections
 Feature Agglomeration
Supervised Dimensionality reduction techniques include methods like
 Linear Discriminant Analysis
What are the challenges of applying dimensionality reduction techniques?
The major challenges of applying dimensionality reduction includes
 Choosing the right variables from all the predictors is one of the most challenging tasks of dimensionality reduction. Wrong choice will lead to building a poor performing model.
 The second challenge is the choice of dimensionality reduction techniques. Out of all the techniques that exist, dimensions of a dataset can be reduced using multiple dimensionality reduction technique and choosing the right one is really important to get the perfect analysis of data.
 Ensuring possession of the right kind of system with appropriate computational power.
FAQ
Common questions related to the Dimensionality Reduction for Machine Learning course

Who should take the Dimensionality Reduction for Machine Learning course?
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!

I have decent programming experience but no background in machine learning. Is this course right for me?
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.

What is the fee for the course?
This course is free of cost!

How long would I have access to the “Dimensionality Reduction for Machine Learning” course?
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.

How much effort do I need to put in for this course?
You can complete the “Dimensionality Reduction for Machine Learning” course in a few hours.

I’ve completed this course and have a good grasp on the various dimensionality reduction techniques. What should I learn next?
The next step in your journey is to build on what you’ve learned so far. We recommend taking the popular “Applied Machine Learning” course to understand the endtoend machine learning pipeline, and how to use dimensionality reduction when working with massive datasets.

Can I download the videos in this course?
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.