What we do first: This is like playing a game where you have to figure out what’s going on by looking at clues. For example, if you’re playing a detective game, you get better at solving mysteries by practicing with cases that already have answers (that’s supervised learning). Sometimes, you just explore the game world to find hidden treasures without a map (that’s unsupervised learning). And when you team up with friends to play better together, that’s like ensemble methods!

Then: Imagine you have a high-tech drone. This drone has a brain called deep learning, which is made up of neural networks that help it understand what it sees, like plants and animals. Some drones are excellent at spotting details (like CNNs that can find pests in crops), while others are great at following patterns (like RNNs that remember which parts of the farm need more water). Together, they make sure your farm is healthy and happy, just like a game headset makes your gaming experience awesome!

But it's not done yet: is like a game where you learn the best moves through trial and error. It’s similar to riding a bike; falling is part of learning. Each attempt improves your skill. RL teaches robots to make decisions through rewards for success, like safely driving a car. They try different actions, learn from outcomes, and gradually get better, just like you do when mastering a new skill.

And final RAVAM assemble a team of top gamers, each an expert in a different genre. Together, they tackle the toughest levels, combining their unique skills to conquer challenges. Similarly, EL combines diverse AI methods to solve complex problems, enhancing a computer’s ability to learn and perform tasks independently, making it smarter and more capable.