Machine Learning Fails: Are You Making These Mistakes? Feeling lost in your machine learning project? This video reveals sneaky issues that can sabotage your AI dreams. Learn about these issues and get your project back on track! Want the full scoop on avoiding these ML pitfalls? Head over to the full FODAI panel discussion on YouTube for a deep dive with the experts! ▶️ https://hubs.la/Q02yhDFJ0 Join our Discord Community and stay up-to date with Future AI and Data trends▶️ https://hubs.la/Q02y

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