National Cyber Warfare Foundation (NCWF)

Welcome back, my aspiring AI practitioners! Often, to build intelligent systems, we need to tackle complex problems that traditional programming simply can’t handle efficiently. It might be recognizing objects in images, understanding human speech, or predicting future trends. The key, of course, is to let machines learn these patterns themselves, rather than trying to code […]

The post Machine Learning, Part 01: Getting Started with the Basics first appeared on Hackers Arise.

Welcome back, my aspiring AI practitioners!

Often, to build intelligent systems, we need to tackle complex problems that traditional programming simply can’t handle efficiently. It might be recognizing objects in images, understanding human speech, or predicting future trends. The key, of course, is to let machines learn these patterns themselves, rather than trying to code every possible scenario manually.

Machine Learning (ML), developed by great minds over decades, is a field designed to give computers the ability to learn without being explicitly programmed. Since ML tools and frameworks are now widely available, let’s learn about them.

What is Machine Learning?

Machine Learning is a field of study that gives computers the ability to learn without being explicitly programmed. Let me give you a real-world example that illustrates why this is so powerful.

Imagine you’re building a self-driving car system. Using traditional programming, you might start by writing code to detect a car by looking for 4 wheels. But what happens when you view the car from behind and can only see 2 wheels? You’d need additional code for this case. What if it’s a motorcycle with 2 wheels? Or a truck with many more wheels?

The exceptions become endless, and you’ll never be able to cover all possible situations that might appear on the road. Your code becomes a nightmare of if-statements and edge cases.

The ML approach is fundamentally different: instead of programming explicit rules, we teach the computer by showing it thousands of examples of different objects. The computer learns what defines a car, bicycle, or pedestrian on its own. But here’s the catch – you need to provide substantial datasets for effective learning.

Step 1: Understanding Why ML Matters

The first step is understanding why we need ML in the first place. Traditional programming works great when you can define clear rules, but ML shines in three key areas:

Building practical systems for real-world apps that would be impossible with traditional approaches

Creating general-purpose AI systems that can adapt and improve

Offering insights into human intelligence and how learning actually works

Step 2: Recognizing ML in Your Daily Life

Once you understand the basics, you’ll start noticing ML everywhere. Let me show you where it’s already working behind the scenes:

Search Engines: Every Google search uses ML algorithms to understand your query and rank billions of pages.

Personal Assistants: Google Assistant and Apple Siri rely on ML for speech recognition and question answering.

Spam/Fraud Detection: Banks use ML to flag suspicious transactions, and email providers filter spam automatically.

Self-Driving Cars: Autonomous vehicles use multiple ML systems simultaneously to navigate roads safely.

Step 3: The Three Methods of Machine Learning

There are three primary ML approaches, each suited for different scenarios:

Supervised Learning

In supervised learning, we collect a dataset of labeled training examples, then train a model to make accurate predictions. When the model sees new, similar data, it will also be accurate.

For example, let’s say we want to predict car prices. Our dataset includes:

Make and model

Year manufactured

Engine size

Mileage

Actual selling price (this is our label)

The algorithm learns the relationship between features and prices, enabling it to predict prices for new cars.

Unsupervised Learning

Here, we have a dataset without labels, and our goal is to learn something about the structure of the data. We’re looking for:

Clusters hidden in the dataset

Outliers: particularly unusual data points

Useful signals hidden in noise (like human speech over a noisy phone line)

Reinforcement Learning

In this approach, an agent interacts with the world over time. We teach it good behavior by providing rewards for positive actions and penalties for negative ones.

Step 4: Understanding AI, ML, and Deep Learning

It’s important to understand how these terms relate to each other, as they’re often confused:

AI (Artificial Intelligence) is about building machines that exhibit intelligence and can mimic human cognitive abilities.

ML (Machine Learning) enables machines to learn from experience – it’s a useful tool for achieving AI.

DL (Deep Learning) focuses on neural networks loosely inspired by the brain structure.

Summary

Now that you understand the fundamentals, you can start exploring specific ML algorithms and tools. Popular frameworks like TensorFlow, PyTorch, and scikit-learn provide ready-to-use implementations.

Remember: the key to successful ML isn’t just understanding the algorithms – it’s knowing which approach to use for your specific problem type.

Keep coming back, aspiring AI practitioners, as we continue to explore Machine Learning!