What I do

Design + Development

Coding: Using pertinent frameworks and best practices, write clear, effective, and maintainable Python code. Testing and debugging: To verify functionality and find and correct errors, unit tests, integration tests, and user acceptability testing should be implemented. Deployment: Ensuring appropriate configuration and security while packaging and launching the program in a production setting.

Machine Learning + AI

Investigating and picking the best machine learning algorithms: Selecting the best algorithms according to the nature of the problem, the properties of the data, and the intended result. Creating and designing machine learning systems: constructing the workflow and architecture needed for ML model deployment, management, and training. Training machine learning models: Which involves feeding pre-processed data into selected algorithms so they can learn and forecast. Monitoring model performance: that entails keeping track on, assessing, and pinpointing opportunities for improvement in the effectiveness of the deployed model.

Data Manipulation + Management

Data exploration and visualization: Tools like Pandas and Matplotlib enables exploration of data through descriptive statistics, data aggregation, and creating various visualizations (histograms, scatter plots, etc.) to identify patterns and trends. Extracting data from various sources: By use of Python I can connect to databases, APIs, and web scraping tools to retrieve data in different formats (CSV, JSON, Excel. Seamless integration: Python's versatility allows smooth integration with other data analysis and visualization tools like R, SQL, and Tableau, I can leverage the strengths of each platform for a comprehensive workflow.