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How do you spot mistakes in your Machine Learning data?

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Mistakes in your Machine Learning data can lead to poor performance, misleading results, and wasted time and resources. How can you spot and avoid them? In this article, you will learn some practical tips and techniques to check and improve the quality of your data before you feed it to your Machine Learning models.

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1 Data Cleaning

The first step to spot mistakes in your Machine Learning data is to clean it. Data cleaning involves removing or correcting missing, inaccurate, duplicate, or irrelevant values in your data. You can use various tools and methods to perform data cleaning, such as pandas, scikit-learn, or TensorFlow Data Validation. Data cleaning can help you identify and fix errors, outliers, and inconsistencies in your data.

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Priya Ranjani Mohan

Manager, Cybersecurity & Tech Risk @ KPMG || Author of Linkedin Learning AI Courses || Forbes Technology Council Member || Speaker || AI Strategist
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Imagine your Machine Learning data is like a box of colorful blocks, and some blocks are broken or don't belong there. Cleaning the data means fixing those blocks. 1. Missing blocks: If some data is missing, like forgetting to write how many apples you ate, data cleaning fills in the gaps. 2. Duplicate blocks: If you accidentally put the same toy car twice, data cleaning finds and removes those duplicates. 3. Incorrect blocks: If someone put a toy dinosaur in the box instead of a toy tiger, data cleaning helps fix that mistake. Using tools like pandas, scikit-learn, or TensorFlow Data Validation makes cleaning data easier. By doing this, you can make sure your data is accurate and ready for your Machine Learning adventures! ????

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2 Data Exploration

The next step to spot mistakes in your Machine Learning data is to explore it. Data exploration involves analyzing and visualizing your data to understand its characteristics, distribution, and relationships. You can use various tools and methods to perform data exploration, such as matplotlib, seaborn, or TensorFlow Data Analysis. Data exploration can help you discover and understand patterns, trends, and anomalies in your data.

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3 Data Preprocessing

The third step to spot mistakes in your Machine Learning data is to preprocess it. Data preprocessing involves transforming and scaling your data to make it suitable for your Machine Learning models. You can use various tools and methods to perform data preprocessing, such as scikit-learn, TensorFlow Transform, or Keras. Data preprocessing can help you avoid and reduce errors, biases, and noise in your data.

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Priya Ranjani Mohan

Manager, Cybersecurity & Tech Risk @ KPMG || Author of Linkedin Learning AI Courses || Forbes Technology Council Member || Speaker || AI Strategist
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FInding mistakes in Machine Learning data is called preprocessing. It means getting your data ready for the ML models. This is done by changing and scaling the data so that the models can understand it better. To do this, you can use tools like scikit-learn, TensorFlow Transform, or Keras. Preprocessing helps to avoid errors, biases, and noise in your data, making your ML models work more accurately. It's like preparing your ingredients before cooking - when you clean and cut them, the recipe turns out much better!

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4 Data Validation

The fourth step to spot mistakes in your Machine Learning data is to validate it. Data validation involves checking and testing your data to ensure that it meets your expectations and requirements. You can use various tools and methods to perform data validation, such as TensorFlow Data Validation, PySpark, or Apache Beam. Data validation can help you monitor and verify the quality, consistency, and compatibility of your data.

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5 Data Augmentation

The fifth step to spot mistakes in your Machine Learning data is to augment it. Data augmentation involves creating and adding new data to your existing data to enhance its diversity and quantity. You can use various tools and methods to perform data augmentation, such as TensorFlow Data Augmentation, Keras, or PyTorch. Data augmentation can help you improve and expand your data, especially if you have limited or imbalanced data.

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6 Data Feedback

The final step to spot mistakes in your Machine Learning data is to get feedback on it. Data feedback involves collecting and incorporating feedback from your Machine Learning models, users, or experts to improve your data. You can use various tools and methods to get data feedback, such as TensorFlow Model Analysis, TensorFlow Serving, or TensorFlow Lite. Data feedback can help you evaluate and optimize your data, as well as identify and address any issues or gaps in your data.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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How do you spot mistakes in your Machine Learning data?

1

2

3

4

5

6

7

1 Data Cleaning

2 Data Exploration

3 Data Preprocessing

4 Data Validation

5 Data Augmentation

6 Data Feedback

7 Here’s what else to consider

Machine Learning

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How do you spot mistakes in your Machine Learning data?

1

2

3

4

5

6

7

1 Data Cleaning

2 Data Exploration

3 Data Preprocessing

4 Data Validation

5 Data Augmentation

6 Data Feedback

7 Here’s what else to consider

Machine Learning

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