I like to solve problems using structured and unstructured data to provide the best solutions that will impact the business I’m involved in. Using different types of analysis () allows for better problem’s identification and extract relevant information that helps to solve the problems.
The main technologies I use in my classes and professional projects are:
- Python =)
- Pandas, pandas-profile and NumPy for exploratory data analysis
- Matplotlib, Seaborn and Bokeh for data visualization
- scikit-learn usage for the main supervised and unsupervised learning algorithms
- Tensorflow for image classification and object identification in imagens
- Image processing and treatment with Pillow and OpenCV
- Use of PySpark (DataFrame and SQL) for parallel and distributed Big Data processing
- Use of Kafka and PySpark Streaming for near real-time Big Data processing
- Use of the main Cloud Computing services from AWS, GCP and Azure.
- ML/DL models deployment using REST API with FastAPI library
- Git for version control
The tutorials was written to all kind of users. In case you have some doubts keep in touch. All the tutorials are available at Tutorials →
List of some terms from the areas of Artificial Intelligence, Machine Learning, Deep Learning and Big Data. Glossary →