AI Vs Machine Learning | What is the Difference between AI and Machine Learning |Simplilearn

In this video on AI Vs Machine Learning, we explore the intriguing world of AI vs. Machine Learning and break down the key differences and similarities between these two cutting-edge technologies. Machine Learning (ML) is like a genius student, mastering data to predict the future. It’s all about finding patterns and making smart predictions. As an ML engineer, my role focuses on creating algorithms and models that learn from data. I handle the data collection, cleaning, and training processes to ensure the models perform accurately and make reliable predictions.

But AI takes it to the next level, making computers think like humans. Picture Siri answering your questions; AI aims to simulate human intelligence and decision-making. Without Machine Learning, AI wouldn’t be as powerful. ML provides the foundational techniques that enable AI to function effectively. These techniques allow AI systems to learn from data, adapt to new information, and improve their performance over time.

As an AI engineer, I integrate these models into larger systems. I design the architecture that allows machines to process information, make decisions, and interact with the world. This involves creating systems that can understand natural language, recognize images, and even drive cars.

✅What is the main difference between AI and Machine Learning?
A: AI is a broader concept that aims to create intelligent systems capable of performing tasks that require human intelligence, while Machine Learning is a subset of AI focused on developing algorithms that allow systems to learn from data and make predictions.

✅How does Machine Learning support AI?
A: Machine Learning provides the foundational techniques and models that enable AI systems to learn from data, adapt to new information, and improve their performance, making AI applications more powerful and effective.

✅ What roles do ML and AI engineers play?
A: ML engineers focus on creating and training models that learn from data, while AI engineers integrate these models into larger systems, designing the architecture that allows machines to process information and make intelligent decisions.

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