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To better understand how the AI (Artificial Intelligence) that surrounds us works, it is necessary to know what Machine Learning (ML) and Deep Learning (DL) are. These two branches of AI represent the foundation of the current systems that we are learning to use daily such as ChatGPT by OpenAI, Gemini by Google, Perplexity and many others.
Before reading this article, I recommend you take a look at the post Simple Guide to Artificial Intelligence to get a general idea of what AI is and how it works.
What is Machine Learning?
Machine Learning (ML) is a branch of Artificial Intelligence (AI) that allows computers to learn from data and make predictions or decisions without being explicitly programmed. Instead of following rigid rules, ML models analyze large amounts of data to identify patterns and improve their performance over time.
Types of Machine Learning
Machine Learning is divided into three main categories:
Supervised learning
In this approach, the model is trained with a labeled dataset, i.e. with already classified examples.
Example: A spam email recognition system is trained with thousands of emails already classified as "spam" or "not spam".
The model learns to recognize patterns and, once trained, can classify new emails on its own.
Unsupervised learning
Here the model works with unlabeled data, autonomously searching for hidden relationships and patterns in the data.
Example: A clustering algorithm that analyzes user behavior on a website and groups them based on similarities, helping to personalize content.
This type of ML is often used to discover new information without explicit guidance.
Reinforcement learning
The model learns through a reward and penalty mechanism, just like a human would through trial and error.
Example: A chess-playing AI improves its game as it wins or loses games, updating its strategy based on previous experiences.
This method is widely used in video games, robotics, and advanced control systems.
How does training an ML model work?
Training a Machine Learning model follows several phases:
Data collection and preparation – The model needs quality data to learn properly.
Model selection – An algorithm suitable for the problem is chosen (for example, a neural network for image recognition or a regression for predicting numerical values).
Training – The model analyzes the data and changes its parameters (weights and biases) to improve its predictions.
Evaluation – The model is tested on new data to verify its accuracy.
Optimization and improvement – If the model is not accurate enough, you can adjust parameters or add more data to improve performance.
Practical Machine Learning Examples
Machine Learning is already present in our daily lives in many ways:
Chatbots and basic virtual assistants (like some automated customer support systems).
Recommendation systems (Netflix, YouTube, Amazon suggest content based on user preferences).
Simple facial recognition (unlock phones with your face, but without advanced depth analysis).
Market forecasting (systems that analyze financial data to predict market trends).
Medicine (automated diagnoses on simpler and predefined datasets).
What is Deep Learning?
Deep Learning (DL) is a subset of Machine Learning that uses deep artificial neural networks to analyze and interpret complex data. These networks are inspired by the functioning of the human brain and are composed of multiple layers of artificial neurons.
Deep Learning is particularly effective for problems involving large amounts of data and complex patterns, such as:
Image recognition (e.g. Google Photos which organizes photos based on recognized faces).
Natural language processing (like ChatGPT, which understands and generates text naturally).
Autonomous driving (self-driving cars use deep neural networks to interpret their surroundings).
Practical examples of Deep Learning
Deep Learning is used in many advanced applications:
Advanced Speech Recognition – Voice assistants like Siri and Google Assistant use neural networks to understand and respond to voice commands naturally.
Advanced Medical Diagnosis – Deep Learning Algorithms Analyze MRI and X-ray Images to Detect Diseases with High Accuracy.
Self-Driving – Self-driving cars use deep neural networks to analyze data from sensors and cameras in real time.
Deepfake and Image Generation – DL can create lifelike faces, edit videos, and generate highly sophisticated digital content.
Difference between Machine Learning and Deep Learning
Machine Learning uses simpler algorithms and can work with smaller datasets, while Deep Learning uses deep neural networks and requires large amounts of data and computing power.
Traditional ML: Algorithms like linear regression, decision trees, and SVM.
DL: Deep neural networks with multiple processing layers.
Applications: ML is suited for simpler or data-limited problems, while DL is ideal for processing images, audio, and natural language at scale.
- Artificial Intelligence (AI): Agents automating tasks usually performed manually by humans
- Machine Learning (ML): Systems that use algorithms and data to infer patterns
- Deep Learning (DL): ML technique that uses algorithms known as neural networks
- Generative AI (Gen-AI): Using deep learning algorithms to generate new content (text, images, etc.)
Conclusion
Machine Learning is one of the most revolutionary technologies of the modern era, allowing computers and intelligent systems to improve themselves autonomously. From personalization of content to autonomous driving, its applications are constantly expanding. Deep Learning represents its most advanced evolution, making even more sophisticated systems possible.
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