Learning AI from scratch– category –

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...

Recap and Today's Theme Hello! In the previous episode, we explored the basics of NumPy, a library for high-speed numerical computation in Python. NumPy enables efficient operations like array manipulation, matrix calculations, and stati...

Recap and Today's Theme Hello! In the previous episode, we learned the basics of the Pandas library for data manipulation. We explored how Pandas makes it easy to work with data frames for tasks like filtering, aggregation, and modificat...

Recap and Today's Theme Hello! In the previous episode, we covered the setup of Python development environments using Anaconda, making it easy to create virtual environments and manage Python and libraries efficiently. Today, we will lea...

Recap and Today's Theme Hello! In the previous episode, we explored the basic operations of Jupyter Notebook. By using Jupyter Notebook, you can run Python code interactively and immediately check the results, making data analysis and AI...

Recap and Today's Theme Hello! In the previous episode, we explored the basics of Python, covering essential knowledge for AI development such as Python syntax, data types, lists, conditional statements, loops, and functions. Today, we w...