CORE
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 turning data into useful applications. I build Python projects, explore 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
About Me
Student today. AI builder in progress.
I'm pursuing B.Tech in Artificial Intelligence & Machine Learning. My approach is simple: understand the concept, write the code, break it, fix it, and turn it into a project that solves something useful.
I'm currently strengthening Python and C++ while building toward practical ML, generative AI, and automation systems.
See my learning roadmap ↓MZ / 01
MARK ZOSUF
AI & ML Student · Aligarh, India Learning & building
01Flagship AI build
10+Tools in learning stack
∞Problems left to solve
PythonC++GitGitHubVS CodeLinuxFlaskNumPyPandasscikit-learnPythonC++GitGitHubVS CodeLinuxFlaskNumPyPandasscikit-learn
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.
MODELS
Machine Learning
Learning supervised and unsupervised ML with NumPy, Pandas, Matplotlib, and scikit-learn.
DATA
Data Analysis
Cleaning datasets, finding patterns, visualizing results, and preparing data for model training.
GEN AI
Generative AI
Connecting AI models through APIs and building useful assistants with prompts, memory, and tools.
METRICS
Model Evaluation
Exploring train/test splits, accuracy, precision, recall, and ways to improve model performance.
TOOLS
Linux & Deployment
Using Git, GitHub, virtual environments, Linux basics, and lightweight web APIs for projects.
Selected Work
Projects built to learn by doing.
Featured build01
GitHub AI Jarvis
A Python desktop assistant with AI responses, voice commands, memory, system tools, and practical automation.
PythonVoiceAI APIAutomation
View project ↗Next experiment02
ML Prediction Lab
A space for the next classification, prediction, or computer-vision project.
scikit-learnPandas
Learning build03
AI API Studio
A Flask-based API playground for connecting Python models to web applications.
FlaskREST APIPython
Learning Roadmap
A clear path from code to production AI.
This roadmap tracks where I've been, what I'm learning now, and the systems I want to build next.
✓
Foundation
Python & problem solving
Syntax, loops, functions, OOP, file handling, and practice projects.02
Now learning
Data & machine learning
NumPy, Pandas, visualization, model training, and evaluation.03
Up next
Deep learning & computer vision
Neural networks, image pipelines, and real-world AI applications.04
Goal
Production AI systems
APIs, deployment, monitoring, and useful end-to-end products.Current focus
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
01py -3.12 -m venv .venv02.venv\Scripts\activate03pip install numpy pandas matplotlib scikit-learn jupyter
02
Train a baseline model
01from sklearn.model_selection import train_test_split02X_train, X_test, y_train, y_test = train_test_split(03 X, y, test_size=0.204)05model.fit(X_train, y_train)
03
Publish and keep learning
01git add .02git commit -m 'Add first ML model'03git push origin main
✦ Never commit API keys, tokens, private datasets, or your .env file.
Let's build
Learning in public, one project at a time.
Follow my journey through Python, AI, machine learning, and automation.