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Recap and Today's Theme Hello! In the previous episode, we explored text data preprocessing, covering techniques such as tokenization, stopword removal, part-of-speech tagging, and n-gram creation. These methods are fundamental in transf...

Recap and Today's Theme Hello! In the previous episode, we explored version control with Git, covering the basics of code management and team collaboration. Using Git allows developers to manage project history, develop with multiple bra...

Recap and Today's Theme Hello! In the previous episode, we concluded Chapter 8 by reviewing the foundational steps in AI development, including building, evaluating, and tuning models. Today, we begin a new theme: Natural Language Proces...

Recap and Today's Theme Hello! In the previous episode, we discussed running models in cloud environments like AWS and GCP, which allowed us to build scalable and reliable systems that can handle large traffic volumes using cloud service...

Recap and Today's Theme Hello! In the previous episode, we explored environment setup with Docker, learning how to ensure reproducibility using container technology. By using Docker, we minimized differences in environments and enabled s...

Recap and Today's Theme Hello! In the previous episode, we explored creating and publishing APIs, using Flask to turn a trained model into an API and deploying it with Heroku. This enabled us to make prediction functions accessible from ...

Recap and Today's Theme Hello! In the previous episode, we learned how to deploy models using Flask by building a simple Web API. We saw how an API can provide access to trained models, allowing real-time predictions as a service. This t...

Recap and Today's Theme Hello! In the previous episode, we learned how to use Flask to integrate a trained model into a web application, allowing users to input data via a form and view prediction results in real-time. Today, we will foc...

Recap and Today's Theme Hello! In the previous episode, we explored data augmentation, demonstrating how to expand image datasets using Keras. We confirmed that data augmentation not only helps models learn from more diverse data but als...

Recap and Today's Theme Hello! In the previous episode, we explored transfer learning, learning how to adapt pre-trained models to new tasks. We saw how transfer learning allows for efficient construction of high-accuracy models even wit...

Recap and Today's Theme Hello! In the previous episode, we covered saving and loading models using Keras, learning how to save trained models for future use. This helps streamline both development and deployment processes. Today, we will...

Recap and Today's Theme Hello! In the previous episode, we explored building and evaluating basic machine learning models using Python’s Scikit-learn library. By learning the steps of data splitting, model training, and evaluation, you s...

Recap and Today's Theme Hello! In the previous episode, we explored the basics of data visualization using Python's Matplotlib library. We learned how to create various graphs, such as line plots, bar charts, and scatter plots, to visual...

Recap and Today's Theme Hello! In the previous episode, we discussed how to create beautiful and advanced graphs using the data visualization library Seaborn. By leveraging Seaborn, it’s easier to visually grasp data distributions and co...

Recap and Today's Theme Hello! In the previous episode, we explained how to read and save various data formats using Pandas. By learning efficient data input and output methods, you are now better equipped for data analysis and AI develo...