Text Classification Basics Part 1: A Glimpse of Machine Learning 🤖
What is Machine Learning?
Machine learning is a technique that uses algorithms to teach computers to learn from data and make predictions or decisions, without needing specific instructions.
What is machine learning used for?
Machine learning can be used for a wide range of tasks, including fraud detection, web search results, recommendation engines, customer segmentation, sentiment analysis, pattern & image recognition, email spam filtering, and many more.
What is supervised learning?
Supervised learning is a machine learning method that uses labeled examples to learn a task. This helps it learn to make predictions on new data. A supervised learning algorithm learns by processing input data and comparing their results to known correct answers. This process helps them refine their ability to make accurate predictions. For instance, it can be used to classify emails as spam or not spam or to categorize reviews as positive or negative.
Machine Learning Pipelines
The process begins with data acquisition. It’s a process of gathering all the data that you need to be fed to the algorithm. You can collect the data by yourself or you just can use open datasets that are widely available on the internet.
After collecting data, you need to clean it up. You need to have clean and well-formatted data to be fed into the machine-learning algorithm. Make sure your data has no missing values and convert the raw text into numerical vectors so the machine learning models can understand.
Once you have clean data, you need to split the data into training sets and test sets, I normally split the data with a 7:3 or 8:2 ratio. After that, you can train the training sets with the machine learning model of your desire. Now it’s time to evaluate the model’s performance by using the test sets.
Let’s evaluate the model’s performance using test data. By using different metrics, we can assess its accuracy. To improve performance, you can adjust the model’s parameters. After fine-tuning the parameters, retrain and test the model until you’re satisfied with the results. Finally, you can deploy the model for real-world use.