Artificial Intelligence & Machine Learning

At a basic level, Machine Learning (ML) is a method used to create systems that can learn from data and improve at tasks over time. It’s one of the many methods used within the larger field of Artificial Intelligence to build smart systems.

Think of AI as a toolbox filled with different techniques — and Machine Learning is one of those key tools used to make systems more intelligent.

What is Machine Learning?

Machine Learning (ML) is a part of Artificial Intelligence (AI) that helps computers learn from data and make decisions or predictions—without needing to be told exactly what to do every time.

Unlike traditional computer programs, which require exact instructions from a user to perform a task, ML systems study data, learn patterns, and improve how they work over time.

For example, in a normal program to make coffee, you’d need to tell it how much coffee, milk, and sugar to use. But with ML, the system learns from your past choices—so it can decide the best coffee to make for you based on your habits, like whether you prefer an espresso, latte, or cappuccino, and how much sugar you usually take.

ML systems need a large amount of data. Once that data is given to the algorithm, it looks for patterns, relationships, and trends. Using that information, it builds a model that can make decisions or give predictions—even without needing new instructions.

So eventually, the system learns you prefer an espresso with less sugar and prepares it automatically, based on your behaviour—not your input.

How Machines Learn: Key ML Approaches

Supervised Learning

The system learns from labelled data, where the correct answer is already known. For example, it can predict fuel or engine oil levels based on how often and how far you drive.

1. Unsupervised Learning The system finds hidden patterns in data without being told what to look for. For instance, if you often visit the same fuel station, it learns this pattern and might suggest it automatically.
2. Semi-Supervised Learning This method is a mix of both supervised and unsupervised learning. It works with a small set of labelled data and a large set of unlabelled data to improve learning.
3. Reinforcement Learning Here, a system learns by trial and error, getting rewards for good actions and penalties for bad ones. For example, if a self-driving car speeds, it could "learn" not to do so again after a penalty is applied.
4. Transfer Learning This involves using what a model has learned in one area to solve a problem in a different but related area. For instance, a system that has learned your cafe visits could apply that knowledge to suggest routes in a new city.
5. Deep Learning This uses layered networks (like how our brains work) to understand more complex patterns. It's especially useful for tasks like image recognition and voice processing.
6. Ensemble Learning Multiple models work together to make better predictions. For example, if you want to sell your car, an ensemble model could compare several platforms and give you the best deal suggestion.

Machine Learning powers many real-world tools—like voice assistants, fraud alerts, recommendation engines, and self-driving systems. It helps machines get smarter, make better decisions, and handle complex tasks with little human input.

How Machine Learning Fits Into AI

A simple explanation of how ML works as part of Artificial Intelligence

As we can see from the above diagram, ML is a subset of AI, and ML itself contains other subsets like Neural Networks which in turn contains the subset of Deep Learning. This is basically how you would integrate ML into your AI system.

1. Build an AI system using ML and other techniques

2. Define the problem you aim to solve, and determine if it really needs ML

3. Gather the data needed for your problem domain, and ensure it's properly labelled and structured

4. Review the raw dataset, to uncover patterns for further analysis

5. Do a proper data cleanup by handling missing values, outliers

6. Choose the appropriate ML algorithm based on problem type and data

7. Divide data into training and validation sets, feed to the chosen algorithm/model, and iteratively adjust model parameters to improve performance

8. Assess the performance of model, using appropriate evaluation metrics on validation set

9. Fine-tune model by adjusting parameters like learning rate, regularization using techniques like grid or random search

10. Validate the model with a separate test dataset to get a reliable estimate of performance

11. Deploy the model into a production environment — could be an API or cloud platform

12. Continuously monitor the model's performance after deployment

13. Undergo iterations for user feedback, collecting new data to periodically re-evaluate and refine your model

Again, the above steps need not necessarily be in order, nor all of them need to be performed, but this is a very broad overview of how we can integrate ML with AI.

While ML and AI are often used interchangeably, they are both distinct concepts. While they have some similarities, there are key differences:

• AI exhibits human-like intelligence; ML does not — it needs the proper input
• ML model complexity can vary; AI models are typically more complex

Why Do We Use Machine Learning with Artificial Intelligence?

Since Machine Learning (ML) is a part of Artificial Intelligence (AI), all the benefits of ML also help make AI more powerful.

1. ML helps automate tasks, saving time and reducing human effort.

2. It can analyze data to make smart predictions and better decisions.

3. ML tools personalize experiences based on what users like or do.

4. ML works in real-time, giving quick insights for faster actions.

5. It learns and improves over time with new data.

6. ML is great at spotting patterns and finding meaning in complex data.

It works best with structured and labelled data — the kind that's already sorted and organized.

Where Are AI and ML Used in Real Life?

• Predictions & Forecasts: Like sales forecasting, stock market trends, or demand planning using past data
• Fraud Detection: Spotting fake or unusual transactions
• Financial Services: Credit scoring, trading, risk analysis, and financial planning
• Natural Language Processing (NLP): Used in chatbots, virtual assistants, and language translation
• Recommendations: Apps like Amazon, Netflix, or Spotify use ML to suggest books, movies, or songs
• Image & Video Recognition: Used in face recognition, video analysis, and smart cameras
• Healthcare: For diagnosing diseases, analyzing medical images, drug research, and patient monitoring
• Self-Driving Tech: In autonomous cars, drones, and robots