Machine learning (ML) has proven to be one of the most disruptive technological advances of the last decade. In an increasingly competitive enterprise world, ML enables companies to accelerate their digital transformation and enter the era of automation. Some might even argue that AI/ML should remain relevant in some industries, such as B. Fraud detection in digital payments and banking or product recommendations. The eventual adoption and enterprise adoption of
machine learning algorithms are also well documented, with various enterprises adopting machine learning at scale across industries.
Every other app and software on the web today uses some form of machine learning. Machine learning is pervasive and has become the solution to many business problems.
This blog details machine learning, ML basics, types of machine learning algorithms, and real-world examples of machine learning. It also explores the differences between artificial intelligence and machine learning.
What is Machine Learning?
Machine learning (ML) is a component of artificial intelligence (AI) that allows computers to “learn by themselves” from training data and improve over time without being explicitly programmed. Machine Learning Algorithms can identify patterns in data and comprehend them to make their own predictions. In other words, machine learning algorithms and models learn through experience.
In traditional programming, computer engineers write a series of instructions that tell a computer to transform input data into the desired output. Statements are primarily based on the IF-THEN constructs. If certain conditions are met, the program will perform specific actions.
On the other hand, machine learning is an automated process that allows machines to solve problems with little or without human input and take action based on past observations.
Types of Machine Learning
Supervised Learning:-
In supervised learning, we are given a dataset, and we have the notion that there is a connection between the inputs and the outputs, so we already know the correct results.
Two Types of supervised learning:-
1. Regression – Estimation of continuous values (output in absolute values)
2. Classification – Identification of unique classes (discrete, Boolean, or categorical)
1.1 Regression:-
Regression is targeted. We model the predicted values based on the independent variables. It is mainly used to find relationships between variables and predictions. Regression can estimate/predict continuous values ?(real-valued output).
Example: An image of a person is given, and we need to predict their age based on that image.
1.2 Classification:
Classification means grouping the output into classes. If your dataset is discrete or categorical, this is a classification problem.
Example: Given data about house sizes in the real estate market, create an output about whether the house is above or below the asking price. H. Categorize housing into two separate categories.
Unsupervised Learning:-
You can tackle a problem with little or no idea what the outcome will be. Even if you don’t necessarily know the effects of variables, you can still derive structure from your data.
You can derive this structure by grouping the data based on the relationships between the variables in the data.
Clustering:-
Clustering is the mission of grouping a set of objects such that objects in the same sets(or cluster) are more similar (in a sense) to each other than objects in other groups (clusters).
Example: Take a collection of 1,000,000 different genes and find a way to automatically group them into groups that are somehow similar or related by some other variable, such as lifespan, location, or role.
Reinforcement Learning:-
Reinforcement learning is about taking appropriate actions in a given situation to maximize the reward. It is used by various software programs and machines to find the best possible behavior or path for a given situation.
Reinforcement learning is unlike supervised learning in that in supervised learning, the answer keys are included in the training data. That is, the model itself is trained with the correct answer. In contrast, reinforcement learning has no response. The reinforcement agent chooses what to do to complete a given task. Without a training dataset, you have to learn from experience.
There are three main types of machine learning problems that can be solved.
Regression: In this problem, the output is of continuous size. Let’s take an example, if you want to predict the speed of a car based on distance, that’s a regression problem. Regression problems can be solved using supervised learning algorithms such as linear regression.
Classification: In this, the output is a categorical value. Classifying emails into the two classes of spam and non-spam is a classification problem that can be solved using supervised learning classification algorithms such as Support Vector Machines, Naive Bayes, Logistic Regression, and K Nearest Neighbor.
Clustering: This problem involves assigning inputs to two or more clusters based on feature similarity. For example, you can utilize unsupervised learning algorithms such as K-Means clustering to group your audience into similar groups based on interest, age, geography, etc.
Machine Learning Applications
There are a variety of machine learning applications that can help your business in a myriad of ways. All you need is to define a strategy to help you decide how best to implement machine learning into your existing processes.
Social Media Monitoring
Uses machine learning to monitor mentions of your brand on social media and instantly see when your customers need immediate attention. You can automatically flag customer feedback for immediate action by detecting angry customer mentions in real time. You can also analyze customer support interactions on social media and measure customer satisfaction (CSAT) to see how your team performs.
Natural Language Processing allows machines to deconstruct spoken and written language in the same way humans process “natural” language, enabling machine learning to process text from virtually any source.
Customer Service and Satisfaction
Machine Learning lets you integrate powerful text analytics tools with your customer support tools so you can analyze emails, live chats, and all kinds of internal data on the go. Using machine learning, support tickets can be flagged and routed to the right team, and FAQs can be auto-replyed, so you never leave a customer behind.
Additionally, you can use machine learning to set up Voice of the Customer (VoC) programs and feedback loops to track the customer journey from start to finish and improve the customer experience (CX). , ultimately increasing your profits.
Image Recognition
Image Recognition helps businesses identify and classify images. For example, facial recognition technology is used as a form of identity verification, from unlocking phones to making payments.
Self-driving cars also use image recognition to perceive space and obstacles. For example, it can learn to recognize stop signs, identify intersections, and make decisions based on what it sees.
Virtual Assistant Her
virtual assistants like Siri, Alexa, and Google Now use machine learning to automatically process and respond to voice requests. Quickly scan for information, remember relevant recommendations, learn from past interactions, and send commands to other apps so they can collect data and provide the most effective responses.
‘s customer support team is already using virtual assistants to handle phone calls, automatically route support tickets to the correct entity, and expedite customer interactions with computer-generated responses.
Artificial Intelligence Vs Machine Learning
US Professor Douglas Hofstadter said: “AI is something that hasn’t been done yet.”, the latter being much more accessible and optimized for use. Following this logic, artificial intelligence refers to cognitive computing advances, and machine learning is a subset of AI.
Artificial Intelligenceis an umbrella term for technologies exhibiting human-like cognitive characteristics. As a rule of thumb, AI research is moving toward more general forms of intelligence that resemble how young children think and perceive the world around them. This shows that his AI has evolved from a program specifically designed for a single “narrow” task to a solution deployed for the “general” answers humans are expected to do. There is a possibility that.
On the other hand, machine learning is an exclusive subset of AI, exclusively for algorithms that can dynamically self-improve.
Unlike many AI programs, they are not statically programmed for a single task and can be extended after use. This makes it suitable for enterprise applications and provides new ways to solve problems in an ever-changing environment.
Machine learning also includes deep learning, an essential discipline for the future of AI. Deep learning features neural networks and algorithms based on the physical structure of the human brain. Neural networks are likely the most productive AI research method because they can emulate the human brain more closely than ever before.
Conclusion
After reading this blog, you have learned the basics of machine learning and its different types. They would have understood the training process and the problems they would solve. Finally, we have looked at various applications of machine learning.
Advanced Certification in Applied Data Science, Machine Learning & IoT By E&ICT Academy, IIT Guwahati
9 months
Certification in Data Science and Machine Learning With Python By The
IoT Academy Noida
180 Hrs
