Lesson 18 of 31By Avijeet Biswal

Last updated on Mar 9, 20218421#### Machine Learning Tutorial: A Step-by-Step Guide for Beginners

Overview#### An Introduction To Machine Learning

Lesson - 1#### What is Machine Learning and How Does It Work?

Lesson - 2#### The Complete Guide to Understanding Machine Learning Steps

Lesson - 3#### Top 10 Machine Learning Applications in 2020

Lesson - 4#### An Introduction to the Types Of Machine Learning

Lesson - 5#### Supervised and Unsupervised Learning in Machine Learning

Lesson - 6#### Everything You Need to Know About Feature Selection

Lesson - 7#### Linear Regression in Python

Lesson - 8#### Everything You Need to Know About Classification in Machine Learning

Lesson - 9#### An Introduction to Logistic Regression in Python

Lesson - 10#### Understanding the Difference Between Linear vs. Logistic Regression

Lesson - 11#### The Best Guide On How To Implement Decision Tree In Python

Lesson - 12#### Random Forest Algorithm

Lesson - 13#### Understanding Naive Bayes Classifier

Lesson - 14#### The Best Guide to Confusion Matrix

Lesson - 15#### How to Leverage KNN Algorithm in Machine Learning?

Lesson - 16#### K-Means Clustering Algorithm: Applications, Types, Demos and Use Cases

Lesson - 17#### PCA in Machine Learning - Your Complete Guide to Principal Component Analysis

Lesson - 18#### What is Cost Function in Machine Learning

Lesson - 19#### The Ultimate Guide to Cross-Validation in Machine Learning

Lesson - 20#### An Easy Guide to Stock Price Prediction Using Machine Learning

Lesson - 21#### What Is Reinforcement Learning? The Best Guide To Reinforcement Learning

Lesson - 22#### What Is Q-Learning? The Best Guide to Understand Q-Learning

Lesson - 23#### The Best Guide to Regularization in Machine Learning

Lesson - 24#### Everything You Need to Know About Bias and Variance

Lesson - 25#### The Complete Guide on Overfitting and Underfitting in Machine Learning

Lesson - 26#### Mathematics for Machine Learning - Important Skills You Must Possess

Lesson - 27#### A One-Stop Guide to Statistics for Machine Learning

Lesson - 28#### Embarking on a Machine Learning Career? Here’s All You Need to Know

Lesson - 29#### How to Become a Machine Learning Engineer?

Lesson - 30#### Top 34 Machine Learning Interview Questions and Answers in 2021

Lesson - 31

While working with high-dimensional data, machine learning models often seem to overfit, and this reduces the ability to generalize past the training set examples. Hence, it is important to perform dimensionality reduction techniques before creating a model. In this article, we’ll learn the PCA in Machine Learning with a use case demonstration in Python.

Below are the topics that we’ll be covering in this article:

- What is Principal Component Analysis?
- What is a Principal Component?
- Applications of PCA in Machine Learning
- How does PCA work?
- PCA Demonstration - Classify the Type of Wine

The Principal Component Analysis is a popular unsupervised learning technique for reducing the dimensionality of data. It increases interpretability yet, at the same time, it minimizes information loss. It helps to find the most significant features in a dataset and makes the data easy for plotting in 2D and 3D. PCA helps in finding a sequence of linear combinations of variables.

In the above figure, we have several points plotted on a 2-D plane. There are two principal components. PC1 is the primary principal component that explains the maximum variance in the data. PC2 is another principal component that is orthogonal to PC1.

The Principal Components are a straight line that captures most of the variance of the data. They have a direction and magnitude. Principal components are orthogonal projections (perpendicular) of data onto lower-dimensional space.

Now that you have understood the basics of PCA, let’s look at the next topic on PCA in Machine Learning.

- PCA is used to visualize multidimensional data.
- It is used to reduce the number of dimensions in healthcare data.
- PCA can help resize an image.
- It can be used in finance to analyze stock data and forecast returns.
- PCA helps to find patterns in the high-dimensional datasets.

Standardize the data before performing PCA. This will ensure that each feature has a mean = 0 and variance = 1.

Construct a square matrix to express the correlation between two or more features in a multidimensional dataset.

Calculate the eigenvectors/unit vectors and eigenvalues. Eigenvalues are scalars by which we multiply the eigenvector of the covariance matrix.

Now that you have understood How PCA in Machine Learning works, let’s perform a hands-on demo on PCA with Python.

From the above box plots, you can see that some features classify the wine labels clearly, such as Alkalinity, Total Phenols, or Flavonoids.

From the above graph, we’ll consider the first two principal components as they together explain nearly 56% of the variance.

By applying PCA to the wine dataset, you can transform the data so that most we can capture variations in the variables with a fewer number of principal components. It is easier to distinguish the wine classes by inspecting these principal components rather than looking at the raw data.

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The principal component analysis is a widely used unsupervised learning method to perform dimensionality reduction. We hope that this article helped you understand what PCA is and the applications of PCA. You looked at the applications of PCA and how it works.

Do you have any questions related to this article on PCA in Machine Learning? If yes, then please feel free to put them in the comments sections. Our team will be happy to solve your queries. Finally, we performed a hands-on demonstration on classifying wine type by using the first two principal components.

Click on the following video tutorial to learn more about PCA - Principal Component Analysis.

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Avijeet is a Senior Research Analyst at Simplilearn. Passionate about Data Analytics, Machine Learning, and Deep Learning, Avijeet is also interested in politics, cricket, and football.

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