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Main Challenges in Machine Learning: 1. Insufficient or Poor-Quality Data Lack of labeled data for supervised learning. Noisy, incomplete, or biased data can lead to poor models. 2. Overfitting and Underfitting Overfitting: Model performs well on training data but poorly on new data. Underfitting: Model is too simple to capture the underlying pattern. 3. High Computational Cost Training complex models (e.g., deep learning) requires powerful hardware and GPUs. 4. Scalability Models trained on sma
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AI • Machine Learning • Tech on Instagram: "Overfitting happens when a model learns the training data too closely, including its noise or random fluctuations, instead of capturing the general pattern. Imagine you’re fitting curves to a set of data points that roughly follow a parabolic trend. A linear model (a straight line) is too simple — it underfits because it can’t capture the curve, leading to both high training and test errors. A quadratic model (a simple parabola) matches the data’s true
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