Week 12/12 #DataScienceBootcamp

Week 12 (14.12.-18.12.) Topic: Final Project & GraduationProject: Speech Emotion RecognitionDataset: RAVDESSCode: GitHub 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... Continue Reading →

Week 11/12 #DataScienceBootcamp

Week 11 (06.12.-11.12.) Topic: Software EngineeringLessons: Software Engineering, Code Profiling, Automated Testing, Continuous Integration, Building Python Packages, Web DeploymentProject: Add tests to our projectsDataset: MovieLens (10k)Code: GitHub 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. Software engineering... Continue Reading →

Week 10/12 #DataScienceBootcamp

Week 10 (30.11.-04.12.) Topic: Recommender SystemsLessons: Unsupervised Learning, Matrix Factorization, Web Development with Flask, Collaborative Filtering, Git Collaboration, PCA, ClusteringProject: Create a web-based movie recommender systemDataset: MovieLens (100k)Code: GitHub 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!... Continue Reading →

Week 9/12 #DataScienceBootcamp

Week 9 (23.11.-27.11.) Topic: Deep LearningLessons: Artificial Neural Networks, Backpropagation, Keras, Convolutional Neural Networks, Pretrained Networks, Transfer Learning, Deep Learning PapersProject: Classify images of clothing items with neural network modelsDataset: Fashion MNISTCode: GitHub This week we dived into Deep Learning and learned about different types neural networks (NN) and their applications in various domains. The... Continue Reading →

Week 8/12 #DataScienceBootcamp

Week 8 (16.11.-20.11.) Topic: Markov SimulationLessons: Markov Chains, Vector & Matrix Multiplication, Python Classes, NumPy Arrays, Program Design, Composition & Inheritance, PEP8 Project: Simulate the paths of new customers in a supermarketDataset: internalCode: GitHub This week we learned to make a Markov Chain Monte Carlo (MCMC) simulation of new customers in a supermarket, based on... Continue Reading →

Week 7/12 #DataScienceBootcamp

Week 7 (09.11.-13.11.) Topic: Data Engineering PipelineLessons: APIs, Docker, MongoDB, ETL, Airflow, Logging, Sentiment Analysis, SlackbotProject: Build a pipeline that collects tweets, stores them in a Mongo database, applies sentiment analysis, loads the tweets into a Postgres database, then a Slackbot sends them in a slack channel.Dataset: Tweets collected with the Twitter APICode: GitHub This... Continue Reading →

Week 6/12 #DataScienceBootcamp

Week 6 (02.11.-06.11.) Topic: DatabasesLessons: Postgres (from Python), Data Modelling, (Advanced) SQL Queries, Joins & Foreign Keys, (Cloud) Databases, Cloud ComputingProject: Build a dashboard on top of a Postgres database that runs in the AWS cloudDataset: NorthwindCode: GitHub This was another week packed with new information and experiences! In only five days, I managed to... Continue Reading →

Week 5/12 #DataScienceBootcamp

Week 5 (26.10.-30.10.) Topic: Time Series AnalysisLessons: Decomposing Time Series, Naive Forecasts, Evaluating Forecasts, Linear Autoregression, Namespaces, Plotting on Maps, Generators, ARIMA Models, Python ModulesProject: Forecast the temperature in Berlin Mitte and plot climate data on a mapDataset: ecad.euCode: GitHub I found this week's project quite challenging, because I haven't worked with time series and... Continue Reading →

Week 4/12 #DataScienceBootcamp

Week 4 (19.10.-23.10.) Topic: Text ClassificationLessons: Web Scraping, Regular Expression, HTML Parsing, Language Models, Class Imbalance, Bag-of-Words, Naive Bayes, Python Functions, Command Line InterfaceDataset: Self web-scraped song lyricsProject: Predict the artist from song lyrics.Code: GitHub I was super excited about this week, because it was about language models and first steps into NLP, my favorite... Continue Reading →

Week 3/12 #DataScienceBootcamp

Week 3 (12.10.-16.10.) Topic: Machine Learning - RegressionLessons: Linear Regression, Feature Expansion, Gradient Descent, Regularization, Feature Selection, Hyperparameter Optimization, Gradient Boosting, Debugging.Dataset: Capital BikeshareProject: Predict demand for bicycle rentals at any given hour, based on time- and weather-related features (and submit the predictions in the kaggle competition).Code: GitHub I really enjoyed this week's project for... Continue Reading →

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