Samuel P.
Senior Python and Machine Learning Engineer
Senior Python and machine learning engineer with 8+ years in production Python. Handles most ML and data-science briefs at GeeksProgramming.
Credentials and experience
- 8+ years writing production Python
- Works with PyTorch, TensorFlow, scikit-learn, pandas, FastAPI, and Hugging Face
- Handles ML and data-science briefs end to end
About Samuel P.
Samuel P. is a Senior Python and ML Engineer at GeeksProgramming. He reviews and handles most machine-learning and data-science assignments that come through the service, pairing tested, working code with clear walkthroughs that explain the design decisions, not just the output.
Background and specialty
Samuel has spent eight years writing production Python, the kind that runs in pipelines, serves predictions, and gets maintained. His daily tools include PyTorch, TensorFlow, scikit-learn, pandas, FastAPI, and Hugging Face, covering both classical machine learning and deep learning workflows. Data-science and ML briefs are his primary territory.
What Samuel handles
Samuel takes on ML and data-science assignments end to end: supervised and unsupervised learning models, deep learning and NLP pipelines, data wrangling with pandas, API work with FastAPI, and framework-specific coursework built around PyTorch, TensorFlow, or scikit-learn. He matches methods to what the course has covered so the solution is defensible in a viva.
Experience behind the work
Eight years of production machine-learning work means Samuel has debugged the failure modes students encounter in coursework: models that converge differently across environments, pipelines that pass locally but fail on graders, and assignments where the evaluation metric matters as much as the code. His deliveries include model-comparison analysis and inline comments explaining each architectural choice, so students can walk an examiner through the logic.
Articles by Samuel P.
- Building a Sentiment Analysis Model With Audio Data
· Build and evaluate an audio sentiment model in Python using RAVDESS, librosa features, actor-grouped validation, and honest emotion labels.
- Building an AI-Based Inventory Demand Forecasting Model
· Build an inventory demand forecast in Python with leakage-safe lag features, rolling validation, seasonal baselines, practical metrics, and monitoring.
- Statistics for Data Science: Complete Guide with Examples
· Learn the statistics behind data science with Python examples on distributions, sampling, confidence intervals, hypothesis tests, regression, and leakage.
- World Happiness Report Analysis in Python: 9 Visualizations
· Analyze the World Happiness Report 2019 in Python with pandas, seaborn, and Folium. 9 charts across 156 countries, and the two columns everyone misreads.
- Build a Movie Recommendation System in Python
· Build a movie recommender in Python with content-based filtering, collaborative filtering, and a hybrid model, then evaluate it and ship it with Flask.
- Machine Learning with Python: A Guide
· A practical walkthrough of supervised learning, unsupervised learning, and deep learning in Python using scikit-learn, CNNs, and RNNs.
- Matplotlib in Python: Plots and Charts
· Learn how to use Matplotlib in Python to create line plots, scatter plots, bar charts, pie charts, and customized graphs with markers, colors, and grids.
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