A Glimpse into Machine Learning
ahead and behind the scenes...
LinkedIn currently lists 452,732 jobs worldwide that require Machine Learning skills, showcasing the growing demand for expertise in this field.
While we witness remarkable AI advancements every day, many enthusiasts remain unfamiliar with the underlying magic.
AI vs. ML: Understanding the Difference
To grasp the concept of machine learning, it's important to first differentiate it from artificial intelligence (AI). AI encompasses the broader scope of developing intelligent machines capable of emulating human thinking and behavior. Machine learning, on the other hand, is a specific field within AI that enables devices to acquire knowledge from data without explicit programming. In simpler terms, machine learning is a subset of AI that focuses on building intelligent systems capable of adapting to human behavior. However, it's crucial to note that machine learning itself is dependent on data for its functioning.
Let's Explore Machine Learning, Shall We? 😎
Machine learning primarily revolves around data acquisition, training, and inference. However, data is not a one-size-fits-all concept. Different data types and use cases require distinct approaches for effective analysis. Just as you can't understand an image by examining each pixel individually, you can't comprehend text by merely glancing at it. The same principle applies to machine learning.
Now that we've scratched the surface of machine learning, let's delve a little deeper into its capabilities.
The Three Types of ML Training 😲
Machine learning models are typically trained using three different methods:
Think of this as a scenario where I show you a picture and inform you that it's a dog 🐶. You observe the characteristics, such as wide ears, big round eyes, and a curvy tail. Later, when I show you a picture of a human, you can differentiate it from a dog based on the learned features. Supervised learning involves providing the model with questions and answers, enabling it to learn the relationship between them.
In contrast to supervised learning, unsupervised learning doesn't involve providing explicit answers. Let's say I give you a set of pictures containing both dogs and cats, but without any labels. Your task is to classify them into separate groups. This method discovers hidden patterns and organizes data without relying on pre-existing knowledge or labels.
Remember how you were rewarded with candy in your childhood for passing exams? Reinforcement learning works similarly. The model is rewarded for every positive action it takes, encouraging it to make more beneficial moves. A classic example of reinforcement learning is training bots to play chess. These bots learn the game through trial and error, with positive moves leading to rewards and improved gameplay.
Additionally, you may encounter a fourth type: Semi-Supervised Learning, which combines elements of supervised and unsupervised learning approaches.
By understanding these three main types of learning, you're equipped with a glimpse into machine learning, ready to embark on your own ML journey.
Until we meet again, Sree Teja Dusi.
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