Machine learning, artificial intelligence and deep learning are closely related but have different characteristics:
- Artificial Intelligence encompasses a broader range of technologies and techniques aimed at creating intelligent systems capable of performing tasks that require intellectual abilities normally associated with humans. Machine Learning is one of the techniques used in Artificial Intelligence;
- Machine Learning is a field that includes algorithms and techniques that allow computer systems to learn from data and make predictions or decisions. Machine learning is a subset of artificial intelligence;
- Deep Learning is a subset of ML that uses artificial neural networks with a large number of layers to extract high-level features from data. It is usually applied to tasks such as pattern recognition, natural language processing, and automatic decision making.
Examples of machine learning applications
Machine learning has applications in everyday life as well as in various industries. Let’s look at a few examples.
For example, in everyday life, everyone knows the voice assistants Siri, Google Assistant and Alice, which use machine learning to recognize and understand voice commands. Also, many smartphones today have automatic facial recognition in photos, which allows you to organize and classify photos into people and create fun videos based on that data. Also one example of machine learning are recommendation systems in online platforms such as YouTube, Netflix, Spotify) that offer personalized recommendations on movies, music, books, etc.
An area where we encounter machine learning almost every day is finance. Analyzing financial data to predict market trends and make investment decisions, identifying fraudulent transactions based on anomalies in customer behavior and historical data, and credit scoring and assessing customers’ ability to pay based on their financial history and other factors.
Machine learning is a key component in the development of autonomous vehicles, allowing them to analyze their environment and make decisions based on sensor data.
Machine learning is also used in industry to optimize manufacturing processes, predict equipment failures, and improve product quality.
