AI and ML are two words that we hear all around us. From ChatGPT and Google Bard to Midjourney, we have seen immense growth in AI and ML tools. But do you know exactly what machine learning and artificial intelligence are? Do you believe both terms are synonymous? If you still think that both ML and AI are the same, then this blog is for you.
In this blog, we will discuss what ML is, its processes and types, and how it differs from artificial intelligence. Are you prepared to delve deeply into the world of AI? If yes, let’s jump in.
Also if you are planning to learn machine learning, what’s a better option than a machine learning course?
Machine learning is a subset of artificial intelligence that helps computers learn from data sets, find patterns, and make predictions. Machine learning (ML) allows computers to learn without being explicitly programmed. How does this happen? How does a machine learn to identify objects?
Let me simply explain. Imagine you are telling a child what a cat is. For the child to quickly identify any animal as a cat, you will show him/her many pictures of the cat. In this way, the child will be able to remember the unique features that a cat has, and readily identify a cat among other animals.
Machine learning works similarly. Data is at its core. Data sets are created that become the base for ML algorithms to train on. For example, an ML algorithm might be trained on a data set containing thousands of images of flowers that are labelled for their different names. When that algorithm is presented with a new image, it can identify it based on the data it was trained on.
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Data collection: The first step is to collect a large amount of data that's relevant to the task you want the machine to learn. This could be anything from text and images to sensor data and audio recordings.
Data preparation: Once you have your data, you need to clean it and prepare it for training. This may involve removing errors, formatting the data correctly, and splitting it into training and testing sets.
Model selection: There are many different machine learning algorithms, each with its own strengths and weaknesses. You need to choose the right algorithm for your task and data.
Model training: This is where the magic happens! The algorithm is trained on the training data, learning to identify patterns and relationships.
Model evaluation: Once the model is trained, you need to evaluate its performance on the testing data. This will help you assess how well it's likely to generalise to new data.
Model deployment: If the model performs well, you can deploy it into production. This means using it to make predictions or decisions based on real-world data.
There are three main types of machine learning. I have explained them in the most simple terms below:
Supervised learning is like learning from an expert. You provide the machine with a lot of data that has already been labelled, and the machine learns to map the inputs to the outputs. Once the machine is trained, it can then make predictions on new, unseen data.
Some common supervised learning tasks include:
Unsupervised learning is like figuring things out on your own. You give the machine a lot of data, but it has to figure out what the data means on its own. This can be a more challenging task than supervised learning, but it can also be more rewarding.
Some common unsupervised learning tasks include:
Reinforcement learning is similar to trial and error. The machine is placed in an environment and given a goal. It then has to explore the environment and take actions that will help it achieve its goal. The machine learns from its successes and failures, and over time it gets better at achieving its goal.
Some common reinforcement learning tasks include:
In addition to these three main types, there are also several other types of machine learning, such as semi-supervised learning and deep learning.
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| Feature | Artificial Intelligence (AI) | Machine Learning (ML) |
|---|---|---|
| Definition | A field in computer science focused on creating intelligent machines capable of performing tasks that typically require human intelligence | A subset AI involved in developing algorithms that can learn from data without being explicitly programmed |
| Goal | To replicate or surpass human intelligence | To allow computers to learn from data and make predictions |
| Data Dependence | Not necessarily data-driven | Heavily reliant on large amounts of data for training |
| Examples | Self-driving cars, game-playing AI, medical diagnosis systems, chatbot assistants | Image recognition, spam filtering, recommendation engines, fraud detection |
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We hope that this blog has provided you with useful insights about machine learning. We have all heard this term but never pondered about what it means. And more than often we have confused it with AI. machine learning is concerned with training a computer to mimic human learning, whereas AI focuses on creating machines that are capable of performing tasks that require human intelligence. Explore more about ML with our machine learning programs and make a career that’s not only rewarding but futuristic.
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