At the beginning of the new year 2021, Kaggle created a new format of competitions aimed at beginners. On the 1st of each month, a month-long Playground competition is launched, where you can practice your ML skills on simple tabular datasets. Apart from competitive experience, the top 3 teams get to win some Kaggle merchandise!
My year in code - 2020 review
2020 has definitely been an objectively crappy year... But in terms of programming experience, this year has been by far my most productive and enriching! It was my third year of coding, and second year of doing for work. I've learned a lot on the job and through self-study, developed my Python skills and even learned two new languages. Here's how my coding year progressed:
Detecting emotions from speech with neural networks
Project completed in week 12 (14.12.-18.12.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
I did it! I graduated from the Data Science Bootcamp! On Friday I presented my final project, which was about detecting emotions from speech with neural networks. It was one of the most challenging project I've worked on, because I had to learn something new (how to process audio data and make live voice predictions) and prepare everything nicely in only 7 days. Here's how it went...
Best practices for software engineering
Project completed in week 11 (06.12.-11.12.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This was the last week of lectures and assigned projects, in which we learned general software engineering techniques and best practices for individual and team coding.
Creating a movie recommender system with Python and Flask
Project completed in week 10 (30.11.-04.12.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This was a really exciting week, because we had a team project which combined the power of Machine Learning algorithms with the beauty of Web Development!
Classifying clothes images with neural networks
Project completed in week 9 (23.11.-27.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This week we dived into Deep Learning and learned about different types neural networks (NN) and their applications in various domains. The main goal of this project was to learn and understand what each hyperparameter in a NN model does and how to tune it, so this week was more theoretical and math-heavy than usual.
Creating a Markov chain Monte Carlo simulation in Python
Project completed in week 8 (16.11.-20.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This week we learned to make a Markov Chain Monte Carlo (MCMC) simulation of new customers in a supermarket, based on data about customer paths from entrance to checkout through four aisles (fruit, drinks, dairy, and spices) recorded on five days from 7 am to 10 pm. This project was particularly challenging for two reasons: it involved object-oriented programming (OOP) and team work. In this post I'll give you an overview of our workflow.
Building a dockerized ETL pipeline for tweet streaming in Slack
Project completed in week 7 (09.11.-13.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This week's project was the most complex and difficult so far. The challenge was to create a database of tweets, along with their sentiment score, and post positive tweets in a Slack channel. This pipeline had to be orchestrated with Docker Compose.
Creating a Metabase dashboard on Postgres and AWS
Project completed in week 6 (02.11.-06.11.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
This was another week packed with new information and experiences! In only five days, I managed to set up a Postgres database, deploy it on AWS RDS, connect it to Metabase to create a dashboard and in turn connect it to AWS EC2 to make the dashboard run continuously.
Forecasting the weather in Berlin with ARIMA models
Project completed in week 5 (26.10.-30.10.20) of the Data Science Bootcamp at Spiced Academy in Berlin.
I found this week's project quite challenging, because I haven't worked with time series and forecasting before. But this means I had a lot of new things to learn!