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General questions

Neural networks are computational models inspired by the human brain. They consist of layers of interconnected nodes (neurons) that process data. Neural networks are essential for tasks like image recognition, natural language processing, and predictive analytics, forming the backbone of modern AI.
Machine learning is a broader field of AI that focuses on algorithms allowing computers to learn from data. Deep learning is a subset of machine learning that uses neural networks with many layers to analyze complex patterns in large datasets, such as recognizing faces or generating text.
Begin by understanding the basics of AI and machine learning. Start with beginner-friendly courses on platforms like Coursera or edX, and explore tutorials on Python libraries like Scikit-learn, TensorFlow, or PyTorch. Reading foundational books like "Artificial Intelligence: A Modern Approach" is also a good starting point.
Python is the most popular language in AI due to its simplicity and extensive libraries, such as TensorFlow, PyTorch, and Keras. Other languages include R for statistical modeling, Java for scalability, and Julia for high-performance computations.
Yes, there are many free resources! Platforms like Kaggle, Google AI, and YouTube offer free tutorials, datasets, and practical projects. Websites like TensorFlow.org also provide free guides for building AI models.
The time it takes depends on your goals and prior experience. For basic concepts, 3-6 months of consistent study can be enough. To become proficient in building and deploying AI models, it may take 1-2 years, especially if you’re diving into advanced topics like deep learning.

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