What is Machine Learning?

Machine learning models or algorithms can learn from data and make predictions on their own. They don’t need humans to explicitly program every rule for how to learn and come up with an output. Hence, the ‘learning’ part. However, in traditional machine learning, humans do need to manually label every single data feature. For example, if you want it to know what a cat looks like, you need to state its distinct features, like ears, whiskers, tail, and paws. This is why traditional Machine learning is best suited for small-scale structured data sets.

Types of Machine Learning Models

The field of machine learning is quite vast and encompasses deep learning as a subset, but there are primarily three main types.

Use Cases of Machine Learning Models

Machine learning in Artificial Intelligence has become quite common lately, and the demand has increased in a lot of fields. Here are some everyday applications of these models:

Spam mail detection and email classification Recommendation engine and personalised ads Fraud detection and flagging suspicious activities Search engine ranking, like the one used by Google House price prediction

What is Deep Learning?

Deep learning is a subset of machine learning, as I have explained above. Unlike traditional machine learning, deep learning can automatically learn features from raw data without manual feature engineering. The data can be labeled or unlabeled, depending on the task. It does so by using multiple hidden layers of neural networks that automatically extract and learn increasingly complex patterns. Deep learning is being widely used today in AI chatbots, image generators, video and music creation, large language models, as well as language translations.

Types of Deep Learning Models

Similar to machine learning, deep learning models also have a variety of architectures based on different use cases. Here’s a breakdown of them.

Use Cases of Deep Learning Models

Deep learning is a branch of machine learning, but its use has evolved a lot, and it is being widely adopted in various industries. Here are a few examples.

Image and object recognition Large language models like ChatGPT, Gemini, and Copilot Image, video, and music generation Building personalized social media algorithms for apps like TikTok, YouTube, and Instagram Converting spoken language into text

Deep Learning vs Machine Learning: Key Differences

Now that we have taken a good look at both models, it is time we finally compare the two and find out the differences between machine learning and deep learning. Name Email ID

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