Blog

Blog

What is Machine Learning

January 31, 2025

Things that were once shown in science fiction are now the reality of the world we live in. We have mobile applications that can predict our daily needs and autonomous cars like Tesla that can drive themselves. All this is possible due to Machine Learning . Machine learning (ML)  is the backbone of today’s technology and innovation. It helps computers to learn from the data, get information and make intelligent decisions without human assistance.

Machine learning is transforming the way we live, work, and engage with the world. With WeCloudData  Let’s explore the world of Machine learning Models and understand how Netflix  recommends your next binge-worthy show on the basis of previous watched shows.

What Does ML Mean?

Machine Learning is the subset of Artificial Intelligence (AI) (add link to what is AI blog on Artificial Intelligence (AI) )that focuses on teaching machines to acquire information from data without being explicitly programmed. ML allows machines to continuously adjust and enhance themselves as they get more experiences. Tom Mitchell defines Machine learning as the study of algorithms that improve their performance by doing some task and gaining experience along the way.

Here is an example to understand the concept of ML. The task is to teach a robot to bake a cookie.

  • A traditional programming method is giving detailed step-by-step instructions to the robot on how to bake a cookie. However ML is training a robot on 10,000 cookies recipes and methods, teaching it how it works and letting the robot figure out by itself how to bake the best cookies.

How Machine Learning Fits into Artificial Intelligence

Machine Learning and Artificial Intelligence are the terms often used interchangeably, but they’re not the same. Artificial Intelligence is the umbrella term for machines that mimic human intelligence, and ML  is one of the key technologies to achieve that goal.

What’s the difference between AI and ML?

  • Artificial Intelligence: Creating computer systems that copy human intelligence to perform tasks like problem-solving, and reasoning.
  • Machine Learning: A subset of AI that teaches machines to absorb from data and improve their performance without human involvement.

Curious about Artificial Intelligence? Read our What is Artificial Intelligence blog to see how Machine Learning fits into the bigger picture.

What is learning machine learning models, with AI

AI vs. ML: What’s the Difference?

Importance of Machine Learning

The trend in data generation is increasing at an alarming rate. World is creating more data every day than it ever has in its history. Without ML, analyzing and using all that data would be practically impossible. ML is all about innovation such as drones, virtual reality, robotics and autonomous cars. ML is transformative technology solving real-world problems like fraud detection, security threat identification, personalization and recommendations, chatbot-assisted automated customer support, data analysis, and more.

Types of Machine Learning

Machine Learning (ML) is broadly categorized into three sub categories;

Supervised Machine Learning

Supervised machine learning or supervised learning is the type of ML which uses label data to train algorithms to predict outcome accurately. Labeled data means the input and corresponding output are known in the data. ML algorithms that use supervised learning methods include neural networks, logistic regression, and  naĂ¯ve bayes.

Example: Predicting the price of a house based on features like size, location, condition and age.

Unsupervised Machine Learning

Unsupervised machine learning or unsupervised learning  use unlabeled data for prediction. Unlabeled data means  the input and corresponding output are unknown in the data. These ML algorithms try to uncover hidden patterns in data by data grouping or clustering. Some unsupervised learning algorithms are k-means clustering, and probabilistic clustering methods.

Example: Customer segmentation for targeted marketing.

Reinforcement Machine Learning

In Reinforcement machine learning, the model is not trained on sample data but it learns  through trial and error by receiving rewards for good decisions and penalties for bad decisions.

Example: Training AI to play chess or navigate a robot in an environment. An excellent example is the IBM Watson system that won the Jeopardy! Competition in 2011.

Types of Machine Learning

Machine Learning in Daily Life

We are using ML based systems in our daily life. Here are some examples;

  • Personalized Recommendations: Spotify and Netflix use Machine Learning to recommend content based on users preferences.
  • Spam Email Detection: Gmail uses ML to detect and filter spam messages.
  • Fraud Detection: Banks use ML models to detect suspicious transactions and prevent fraud.
  • Customer Support: Chatbots like ChatGPT using ML models provide instant support and enhance customer satisfaction.
  • Virtual Assistants: Apple Siri and Amazon Alexa are ML based virtual systems that understand voice commands and respond effectively.
  • Healthcare: Smartwatches use ML to monitor users health metrics and detect irregularities.
  • Predictive Analytics: ML is rapidly used in business to forecast different trends, such as stock market movements or demand in supply chains.
  • Generative AI : ****Tools like Gemini and ChatGPT also use ML models to generate human-like responses.

WeCloudData: Your Partner in Learning Machine Learning

WeCloudData offers a practical and industry-relevant Machine Learning Course designed to help you build a solid foundation in ML. Whether you’re a student, professional, or enthusiast, our self-paced courses and live training sessions provide the perfect start to your ML journey.

Tip: Pair this course with our AI training online course for a comprehensive understanding of AI and ML concepts.

What We Offer More!

If you are looking to build your Machine Learning portfolio, do check our Machine Learning Engineering Course and start your career as a Machine Learning Engineer.

WeCloudData not only offers short courses but also provides a comprehensive range of resources to support your learning journey. These include self-paced courses tailored to your schedule, live public training sessions led by industry experts, career workshops to prepare you for the job market, dedicated career services, and portfolio support to help showcase your skills to potential employers.

Join WeCloudData to kickstart your learning journey and unlock new career opportunities in this exciting field.

SPEAK TO OUR ADVISOR
Join our programs and advance your career in AI EngineeringMachine Learning Engineering

"*" indicates required fields

Name*
This field is for validation purposes and should be left unchanged.
Other blogs you might like
Career Guide, Student Blog
The blog is posted by WeCloudData’s full-time data science diploma program student Yining Zhuang. In this blog, I would…
by Student WeCloudData
November 27, 2020
Blog
Prompt engineering has become a key discipline for optimizing AI systems. Among the various prompt engineering techniques, role-playing prompting…
by WeCloudData
January 22, 2025
Blog
In the age of AI, Fine Tuning Large Language Models (LLMs) like have revolutionized how businesses operate. These LLMs…
by WeCloudData
January 29, 2025

Kick start your career transformation

This site is registered on wpml.org as a development site. Switch to a production site key to remove this banner.