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Some common mistakes that beginner machine learning (ML) students often make are: 

1. Skipping Fundamentals: 

   Some beginners might be eager to jump into complex algorithms without fully understanding the fundamentals of ML. Skipping concepts like linear algebra, statistics, and probability can hinder their ability to grasp advanced topics. 

2. Jumping straight to neural networks: 

   Some beginners jumps straight to neural networks. Beginners might be attracted to the complexity and popularity of neural networks without first gaining a solid understanding of the fundamentals of machine learning. 

3. Ignoring Data Quality: 

   Beginners may underestimate the importance of clean and high-quality data. Ignoring issues like missing values, outliers, and data imbalances can lead to inaccurate models and unreliable results. 

4. Overfitting: 

   Beginners often struggle with overfitting, where a model performs well on training data but fails to generalize to new, unseen data. Understanding techniques to prevent overfitting, such as regularization, is crucial. 

5. Lack of Model Evaluation: 

   Some beginners focus only on building models without paying enough attention to proper evaluation. It's important to use metrics like accuracy, precision, recall, and F1 score to assess model performance. 

6. Copying and Pasting Code: 

   Instead of understanding the code they find online, beginners may resort to copying and pasting without grasping the underlying concepts. This hinders their ability to troubleshoot and modify code for different scenarios. 

7. Not Exploring Different Algorithms: 

   Beginners may stick to a single algorithm without exploring a variety of models. It's essential to understand the strengths and weaknesses of different algorithms and choose the one that suits the problem at hand. 

8. Underestimating Domain Knowledge: 

   Machine learning is not only about algorithms; domain knowledge is equally crucial. Beginners sometimes overlook the importance of understanding the specific domain they are working in, which can impact feature selection and model interpretation.

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