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Python Development
Building a strong base in Python, OOP, file handling, APIs, automation, and clean project structure.
Aspiring AI/ML Engineer
Building Intelligent Systems with Python.
I'm MARK ZOSUF, an AI & ML student learning how to turn data into useful applications. I build Python projects, experiment with machine-learning workflows, and create AI-powered automation such as GitHub AI Jarvis.
PS C:\AI-Lab> python jarvis.py
● Loading AI pipeline...
✓ GitHub Models connected
✓ Voice assistant ready
JARVIS: AI workspace is ready, Mark.
YOU: Analyze this dataset
AI/ML Learning Stack
From Python fundamentals to intelligent applications.
My current focus is building strong foundations in programming, data handling, machine learning, and practical AI integrations.
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Machine Learning
Learning supervised and unsupervised ML with NumPy, Pandas, Matplotlib, and scikit-learn.
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Data Analysis
Cleaning datasets, finding patterns, visualizing results, and preparing data for model training.
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Generative AI
Connecting AI models through APIs and building useful assistants with prompts, memory, and tools.
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Model Evaluation
Exploring train/test splits, accuracy, precision, recall, and ways to improve model performance.
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Linux & Deployment
Using Git, GitHub, virtual environments, Linux basics, and lightweight web APIs for projects.
Featured Projects
Add your own build photos here
AI ASSISTANT
GitHub AI JarvisPython assistant with AI responses, voice commands, memory, and desktop automation.
NEXT ML BUILD
ML Project Coming SoonAdd a prediction, classification, or computer-vision project here.
AI/ML Workflow
How I build and improve a model.
I follow a simple learning workflow: understand the problem, prepare the data, build a baseline, evaluate it, and improve one step at a time.
✓ Define the problem
✓ Clean and explore the data
✓ Train and evaluate a baseline
✓ Deploy, test, and improve
01
Create the AI/ML environment
py -3.12 -m venv .venv
.venv\Scripts\activate
pip install numpy pandas matplotlib scikit-learn jupyter
02
Train a baseline model
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model.fit(X_train, y_train)
03
Publish and keep learning
git add .
git commit -m 'Add first ML model'
git push origin main
✦ Never commit API keys, tokens, private datasets, or your
.env file.