Tutorial Playlist

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 45 Machine Learning Interview Questions and Answers for 2022

Lesson - 31

Explaining the Concepts of Quantum Computing

Lesson - 32
Top 10 Machine Learning Applications in 2020

Machine learning is the latest buzzword sweeping across the global business landscape. It has captured the popular imagination, conjuring up visions of futuristic self-learning AI and robots. In different industries, machine learning has paved the way for technological accomplishments and tools that would have been impossible a few years ago. From prediction engines to online TV live streaming, it powers the breakthrough innovations that support our modern lifestyles.

Now, before we get into the various machine learning applications, let us first understand what machine learning is.

What is Machine Learning?

Machine learning is an umbrella term for a set of techniques and tools that help computers learn and adapt on their own. Machine learning algorithms help AI learn without being explicitly programmed to perform the desired action. By learning a pattern from sample inputs, the machine learning algorithm predicts and performs tasks solely based on the learned pattern and not a predefined program instruction. Machine learning is a life savior in several cases where applying strict algorithms is not possible. It will learn the new process from previous patterns and execute the knowledge.

One of the machine learning applications we are familiar with is the way our email providers help us deal with spam. Spam filters use an algorithm to identify and move incoming junk email to your spam folder. Several e-commerce companies also use machine learning algorithms in conjunction with other IT security tools to prevent fraud and improve their recommendation engine performance.

Let’s explore other real-world machine learning applications that are sweeping the world.

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1. Social Media Features

Social media platforms use machine learning algorithms and approaches to create some attractive and excellent features. For instance, Facebook notices and records your activities, chats, likes, and comments, and the time you spend on specific kinds of posts. Machine learning learns from your own experience and makes friends and page suggestions for your profile.

Social Media Features

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2. Product Recommendations

Product recommendation is one of the most popular and known applications of machine learning. Product recommendation is one of the stark features of almost every e-commerce website today, which is an advanced application of machine learning techniques. Using machine learning and AI, websites track your behavior based on your previous purchases, searching patterns, and cart history, and then make product recommendations.

Product Recommendations

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3. Image Recognition

Image recognition, which is an approach for cataloging and detecting a feature or an object in the digital image, is one of the most significant and notable machine learning and AI techniques. This technique is being adopted for further analysis, such as pattern recognition, face detection, and face recognition.

Facial Recognition

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4. Sentiment Analysis

Sentiment analysis is one of the most necessary applications of machine learning. Sentiment analysis is a real-time machine learning application that determines the emotion or opinion of the speaker or the writer. For instance, if someone has written a review or email (or any form of a document), a sentiment analyzer will instantly find out the actual thought and tone of the text. This sentiment analysis application can be used to analyze a review based website, decision-making applications, etc.

Sentiment Analysis

5. Automating Employee Access Control

Organizations are actively implementing machine learning algorithms to determine the level of access employees would need in various areas, depending on their job profiles. This is one of the coolest applications of machine learning.

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6. Marine Wildlife Preservation

Machine learning algorithms are used to develop behavior models for endangered cetaceans and other marine species, helping scientists regulate and monitor their populations.  

7. Regulating Healthcare Efficiency and Medical Services

Significant healthcare sectors are actively looking at using machine learning algorithms to manage better. They predict the waiting times of patients in the emergency waiting rooms across various departments of hospitals. The models use vital factors that help define the algorithm, details of staff at various times of day, records of patients, and complete logs of department chats and the layout of emergency rooms. Machine learning algorithms also come to play when detecting a disease, therapy planning, and prediction of the disease situation. This is one of the most necessary machine learning applications.

8. Predict Potential Heart Failure

An algorithm designed to scan a doctor’s free-form e-notes and identify patterns in a patient’s cardiovascular history is making waves in medicine. Instead of a physician digging through multiple health records to arrive at a sound diagnosis, redundancy is now reduced with computers making an analysis based on available information.

9. Banking Domain

Banks are now using the latest advanced technology machine learning has to offer to help prevent fraud and protect accounts from hackers. The algorithms determine what factors to consider to create a filter to keep harm at bay. Various sites that are unauthentic will be automatically filtered out and restricted from initiating transactions.

10. Language Translation

One of the most common machine learning applications is language translation. Machine learning plays a significant role in the translation of one language to another. We are amazed at how websites can translate from one language to another effortlessly and give contextual meaning as well. The technology behind the translation tool is called ‘machine translation.’ It has enabled people to interact with others from all around the world; without it, life would not be as easy as it is now. It has provided confidence to travelers and business associates to safely venture into foreign lands with the conviction that language will no longer be a barrier.

Credits: Adam Geitgey

Your model will need to be taught what you want it to learn. Feeding relevant back data will help the machine draw patterns and act accordingly. It is imperative to provide relevant data and feed files to help the machine learn what is expected. In this case, with machine learning, the results you strive for depend on the contents of the files that are being recorded.

Application of Unsupervised Learning Examples

Unsupervised learning enables systems to identify patterns within datasets with AI algorithms that are otherwise unlabeled or unclassified. There are numerous application of unsupervised learning examples, with some common examples including recommendation systems, products segmentation, data set labeling, customer segmentation, and similarity detection.

Application of Reinforcement Learning Examples

Reinforcement learning is also frequently used in different types of machine learning applications. Some common application of reinforcement learning examples include industry automation, self-driving car technology, applications that use Natural Language Processing, robotics manipulation, and more. Reinforcement learning is used in AI in a wide range of industries, including finance, healthcare, engineering, and gaming.

Machine Learning for Algorithmic Trading

Algorithms are a significant part of machine learning, and this technology relies on data patterns and rules in order to achieve specific goals or accomplish certain tasks. When it comes to machine learning for algorithmic trading, important data is extracted in order to automate or support imperative investment activities. Examples can include successfully managing a portfolio, making decisions when it comes to buying and selling stock, and so on.

Conclusion

Popular machine learning applications and technology are evolving at a rapid pace, and we are excited about the possibilities that our Machine Learning course has to offer in the days to come. As the demand for AI and machine learning has increased, organizations require professionals with in-and-out knowledge of these growing technologies and hands-on experience. Keeping the innate need in mind, Simplilearn has launched AI and Machine Learning certification courses with Purdue University in collaboration with IBM that will help you gain expertise in various industry skills and technologies from Python, NLP, speech recognition, to advanced deep learning. This Post Graduate program will help you stand out in the crowd and grow your career in thriving fields like AI, machine learning, and deep learning.

About the Author

Nikita DuggalNikita Duggal

Nikita Duggal is a passionate digital marketer with a major in English language and literature, a word connoisseur who loves writing about raging technologies, digital marketing, and career conundrums.

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