Start Your AI & Data Scientist Learning Journey

Welcome to the comprehensive AI & Data Scientist learning path! Let me guide you through a personalized learning journey.

Choose Your Path

1. Complete Beginner (0-3 months experience)

• Focus: Python fundamentals, statistics basics, first ML models
• Start with: Programming Foundations Agent + Statistical Analysis Skill
• Goal: Build first classification/regression model
• Time investment: 15-20 hours/week

2. Intermediate (3-12 months experience)

• Focus: Advanced ML, deep learning, real projects
• Start with: Machine Learning & AI Agent + Deep Learning Skill
• Goal: Complete portfolio project, deploy first model
• Time investment: 10-15 hours/week

3. Advanced (1+ years experience)

• Focus: Specialized domains (NLP, CV, MLOps), production systems
• Start with: MLOps & Deployment Agent + Domain-specific skills
• Goal: Production ML system, contributions to open source
• Time investment: 10+ hours/week

4. Career Transition

• Focus: Portfolio building, interview prep, networking
• Start with: Domain & Career Agent
• Goal: Land data scientist role
• Time investment: 20+ hours/week

Learning Roadmap (12-Month Plan)

Months 1-3: Foundations

Programming:

• Python (Kaggle, Google's Python Class)
• SQL (SQLTutorial.org)
• Git/GitHub basics

Mathematics:

• Statistics (Khan Academy, StatQuest)
• Linear Algebra (3Blue1Brown)
• Probability

Tools:

• Jupyter Notebooks
• Pandas, NumPy
• Matplotlib, Seaborn

Milestone: Complete Titanic or House Prices Kaggle competition

Months 4-6: Machine Learning

Core ML:

• Supervised learning (sklearn)
• Model evaluation & selection
• Cross-validation
• Hyperparameter tuning

Practice:

• 3-5 Kaggle competitions
• Build portfolio projects
• Document everything on GitHub

Milestone: Build end-to-end ML project (EDA → Model → Deployment)

Months 7-9: Deep Learning & Specialization

Deep Learning:

• Neural networks (PyTorch/TensorFlow)
• CNNs for computer vision
• RNNs/Transformers for NLP

Choose Specialization:

• NLP (text analysis, LLMs)
• Computer Vision (object detection)
• Time Series (forecasting)
• Recommender Systems

Milestone: Advanced project in specialization area

Months 10-12: Production & Career

MLOps:

• Docker, APIs (FastAPI)
• Model deployment
• Monitoring & maintenance
• Cloud platforms (AWS/GCP/Azure)

Career Prep:

• Polish portfolio (3-5 projects)
• Practice coding (LeetCode)
• Mock interviews
• Network on LinkedIn/Twitter

Milestone: Production ML system + Job applications

Recommended Learning Resources

Free Courses:

• Andrew Ng's ML Specialization (Coursera)
• Fast.ai Practical Deep Learning
• Kaggle Learn (micro-courses)
• FreeCodeCamp Data Analysis

Books:

• "Hands-On Machine Learning" by Aurélien Géron
• "Python for Data Analysis" by Wes McKinney
• "Introduction to Statistical Learning"

Practice:

• Kaggle competitions
• LeetCode (Python/SQL)
• GitHub open source contributions

Communities:

• r/MachineLearning, r/datascience (Reddit)
• Kaggle forums
• MLOps Community (Slack)

Weekly Study Schedule Template

Beginner (20 hours/week):

• Monday-Wednesday: Python/Math (2h/day)
• Thursday-Friday: ML theory (2h/day)
• Weekend: Projects (5h each day)

Intermediate (15 hours/week):

• Weekdays: Theory + coding (1.5h/day)
• Weekend: Project work (3-4h/day)

Advanced (10 hours/week):

• Deep dives into specialization
• Advanced projects
• Open source contributions

Getting Started Commands

# Browse all available agents
/browse-agent

# Take a knowledge assessment
/assess

# View complete roadmap
/roadmap

# Explore hands-on projects
/projects

Next Steps

1. Assess your current level with /assess
2. Choose learning path based on results
3. Start with relevant agent (e.g., Programming Foundations)
4. Build while learning - theory + practice
5. Track progress weekly
6. Adjust plan as needed

Remember: Consistency beats intensity. Better to study 1 hour daily than 7 hours on Sunday!

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Ready to start? Tell me your current level and goals, and I'll create a personalized learning plan!