In today's world, our tasks are increasingly accomplished with the assistance of machines and applications. We guide them, and they respond accordingly. Ever noticed how machines seem to understand us better than ever? That's AI, or Artificial Intelligence, working its magic. From voice assistants to self-driving cars, AI is becoming a big part of our lives. But for these machines to work just right, they need the perfect training.

AI is like giving brains to machines.  

A young child is initially taught to count on their fingers; eventually, they transition to counting without fingers, yielding commendable results or think about self-driving cars. They've learned how to drive safely by studying countless real-life driving situations. Or think of software that can summarize long articles for you. It knows what's important in a sea of words. But all this is possible only when AI is trained well.  

                        AI also achieves higher levels of proficiency through continuous practice and learning.

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Here are steps in training AI :-

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1. Data Preparation:

• Imagine teaching a robot to recognize animals. It needs to see all kinds of animals, not just cats or dogs. Similarly, AI needs diverse data to learn from.  

• After the data has been gathered, the next step is to annotate it. This involves labelling the data to make it machine-readable.

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2. Model selection :

• Choosing the appropriate model architecture and algorithms to best solve the problem.  

• There are various types of models, such as decision trees, random forests, neural networks, deep learning etc.

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3. Initial training  

• Inputting the prepared data into the model to identify any errors that might surface.

• Just like we learn from others' experiences, AI can learn from what's already been taught for similar tasks. It's like building on what's already known.

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4. Training validation

• Corroborate your assumptions about the performance of the machine learning model with a new dataset called the validation dataset.

• Results obtained from the new dataset should be carefully analyzed to identify any shortcomings.  

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5. Testing the model

•  Where Things Could Go Wrong and Lessons from Mistakes

•  Sometimes, if we train AI with the wrong stuff, it can mess up.

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While writing this article, I did indeed seek assistance from ChatGPT, but solely to help structure sentences appropriately for the examples I had in mind.

Training AI doesn't stop at initial data preparation. It involves iterative learning, model selection, and validation to ensure the AI adapts to a variety of situations. AI, like us, can make mistakes, especially if trained with the wrong data or assumptions. These mistakes teach us valuable lessons about the importance of precision in training.

When AI is trained well, it becomes a super-smart helper, making our lives easier and more fun. So, whether it's teaching a car to navigate or a chatbot to understand, remember, getting the training right is like giving AI superpowers!

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