Project Structure Here’s the project structure for my application. Adjust to your needs. I used the Express application generator to scaffold the program. . ├── docker-compose.yml ├── Dockerfile ├── healthcheck.js ├── LICENSE ├── node_app │ ├── app.js │ ├── bin │ │ └── www │ ├── db │ │ ├── Dockerfile │ │ ├── knex.js │ │ ├── migrations │ │ └── seeds │ ├── knexfile.js │ ├── node_modules │ ├── package.

Streamlit allows you to write Markdown within a Python file (.py): import streamlit as st st.title("Otto Group Product Classification Challenge 🛍") st.markdown("## 1. Problem Statement") st.markdown( "Given a dataset with 93 features, create a predictive model which is able to distinguish between the main product categories." ) st.markdown("### 1.2 Evaluation") st.markdown( "The evaluation for the competition is multi-class logarithm loss. See Kaggle: Evaluation." ) I like that I can write Markdown, but the syntax is cumbersome.

Today I learned that you can pass a date argument to find. I wanted to delete all files that were older than 10 days using the command line. We can use find which ships with every Linux distribution. find . -type f -mtime +20 Find all files that are older than 20 days in the current directory. Now let’s delete them: find . -type f -mtime +20 -exec rm -f {} ; I use fd as an alternative to find:

Create a Docker container that runs your machine learning models as a web application This article will explain the advantages of Streamlit and how to build a Streamlit application with Docker. Why Streamlit? You’ve explored your data and developed a machine learning model. It’s now time to release it to the world so that others can see what you’ve built. Now what? Deploying machine learning models is not trivial.

I finished the Complete Machine Learning and Data Science: Zero to Mastery this weekend (and wrote about it). The course has given me the foundations of working with data in Python. Practice makes perfect. My goal is to sharpen my skills by exploring a Kaggle dataset, building a model and deploying it with Streamlit using Docker and Heroku. The project will be on GitHub where I will post all the links, my thoughts and observations.

I’m going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery and writing down my observations/lecture notes. This is the eigth part of the blog post series. part 1 part 2 part 3 part 4 part 5 part 6 part 7 TL;DR (A Review of The Complete Course) The program is a praise-worthy introduction to data science and machine learning with Python. The instructors focus on practical skills and convey an enormous topic in a captivating and friendly way.

I’m going through the Udemy course Complete Machine Learning and Data Science: Zero to Mastery and writing down my observations/lecture notes. This is the seventh part of the blog post series. part 1 part 2 part 3 part 4 part 5 part 6 part 8 13. Data Engineering These lectures cover what kind of data we have (structured data, unstructured data, etc.). How can we make the raw data consumable for machine learning libraries?