My Most Read Medium Stories

And my posts categorized according to various topics

Azores, Portugal. Photo by Ferdinand Stöhr on Unsplash

If you appreciated my articles and would like to focus on one or more specific topics, this post will be useful for you. I am going to provide comprehensive tutorials on a range of Data Science topics. Each category has a list of articles, that will help you to save time on your learning journey.

A summary of topics covered until now:

• Programming
• Data Engineering
• Data Visualization
• Statistics
• Web Applications
• NLP
• Web Scraping
• Anomaly Detection
• Pytorch

Programming:

• Recursive Functions with Python
• Explaining Python Classes in a simple way
• Master Class Inheritance in Python
• Master the basics of R programming

Data Engineering:

• How To Deal With Time Series Using Pandas and Plotly Express
• 3 Pandas Functions To Group and Aggregate Data
• Convert Strings to DateTime format in Python
• Mito: A JupyterLab Extension for easy data manipulation with no code
• Feature Engineering for Machine Learning in R

Data Visualization:

• Plotly Express: Interprete data through interactive visualization
• How to transition from Excel to Power BI
• Tableau Tutorial for Beginners

Learn more about Statistics:

• An intuitive introduction to Boxplots
• An introduction to Probability Sampling Methods
• An introduction to non-Probability Sampling Methods

Photo by Jessica Ruscello on Unsplash

Web Applications:

• Web App to Transcribe and Summarize audio with Python
• Web Application to extract topics from audio with Python
• Web Application to Convert a Video to a Gif Using Streamlit

Basics of NLP:

• Text pre-processing in Python
• Bag-of-Words with an Example in Python
• TF-IDF with an Example in Python
• Word Clouds with TF-IDF

Web Scraping:

• Understanding the HTML Basics for Web Scraping
• A Simple Introduction to Web Scraping with Beautiful Soup
• An Intuitive Guide to Web Scraping using Selenium
• Scraping job offers from Glassdoor using Octoparse

Anomaly Detection:

• Anomaly Detection With Isolation Forest
• Interpretation of Isolation Forest with SHAP
• Paper Review: Reconstruction by inpainting for visual anomaly detection

Photo by Dan Meyers on Unsplash

Manage ML Experiments:

• An Intuitive Guide to Track Your ML Experiments with MLflow
• A quick guide for tracking Pytorch Experiments using Neptune.ai
• Three ways to parse arguments in your Python code
• How To Update Your GitHub Repository in Visual Studio Code

Regularization:

• A Comprehensive Guide of Regularization Techniques in Deep Learning
• K-Fold Cross Validation for Machine Learning Models
• A Comprehensive Guide to Image Augmentation using Pytorch

Get started with Pytorch:

1. Pytorch Tutorial for Beginners
2. Manipulating Pytorch Datasets
3. Understand Tensor Dimensions in DL models
4. CNN & Feature visualizations
5. Hyperparameter tuning with Optuna
6. K Fold Cross Validation
7. Convolutional Autoencoder
8. Denoising Autoencoder
9. Variational Autoencoder

My Most Read Articles on Medium

1. Plotly Express: Interprete data through interactive visualization
2. K Fold Cross Validation with Pytorch and sklearn
3. How to create a strong Data Science Portfolio for free
4. Convolutional Autoencoder in Pytorch on MNIST dataset
5. Master Class Inheritance in Python

About Me

Research fellow at the University of Padova and Freelance writer, focusing on data science, machine learning, artificial intelligence and programming.

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https://eugeniaring.github.io/