🎯 Your Complete AI Engineering Journey
This comprehensive 16-week program transforms you from a complete beginner into a production-ready AI Engineer. You'll master the complete AI engineering stack through 4 progressive phases covering programming, machine learning algorithms, deep neural networks, and MLOps practices used by top tech companies.
📋 What Makes This Different?
- Structured Learning Path: Follow a proven sequence from basics to advanced topics
- 4 Real-World Projects: Build portfolio projects that demonstrate production skills
- Hands-On Practice: 31+ interactive tutorials with code examples you can run
- Professional Certificate: Earn a verified credential to showcase on LinkedIn & resume
- Self-Paced & Free: Learn at your own speed with lifetime access
Career Outcomes
What you can achieve after completing this path
📚 Your 16-Week Learning Journey
Phase 1: Python Fundamentals
Weeks 1-3 • Foundation
Master Python programming from scratch. Learn syntax, data structures, OOP, and libraries essential for AI development.
Phase 2: Machine Learning Mastery
Weeks 4-8 • Core Skills
Build your ML foundation with supervised and unsupervised learning algorithms, from linear regression to gradient boosting.
- 1. Introduction to Machine Learning
- 2. Linear Regression
- 3. Logistic Regression
- 4. Decision Trees
- 5. Random Forests
- 6. Support Vector Machines
- 7. Gradient Boosting (XGBoost, LightGBM)
Phase 3: Deep Learning
Weeks 9-13 • Advanced
Master neural networks, CNNs, RNNs, Transformers, and generative models. Build state-of-the-art AI systems.
- 1. Neural Networks Fundamentals
- 2. Training Neural Networks
- 3. Convolutional Neural Networks (CNNs)
- 4. Recurrent Neural Networks (RNNs)
- 5. Attention Mechanisms & Transformers
- 6. Generative Models & GANs
- 7. Transfer Learning & Fine-Tuning
Phase 4: MLOps & Production
Weeks 14-16 • Production Ready
Deploy models to production with Docker, Kubernetes, CI/CD pipelines, monitoring, and automated retraining.
- 1. What is MLOps & Why It Matters
- 2. ML Development Workflow
- 3. Model Packaging & Serialization
- 4. API Development for ML Models
- 5. Containerization with Docker
- 6. Cloud Deployment (AWS, GCP, Azure)
- 7. Model Serving at Scale
- 8. Orchestration & ML Pipelines
- 9. Kubernetes for ML
- 10. Infrastructure as Code (Terraform)
- 11. Model Monitoring
- 12. Model Retraining & Continuous Learning
- 13. Production Best Practices
- Project 1: End-to-End ML Pipeline
- Project 2: Real-Time Prediction System
- Project 3: Model Monitoring & Drift Detection
- Project 4: Multi-Model Serving Platform
💪 Skills You'll Master
Python Programming
NumPy, Pandas, Matplotlib, OOP, data structures, algorithms
Machine Learning
Scikit-learn, regression, classification, clustering, ensemble methods
Deep Learning
TensorFlow, PyTorch, CNNs, RNNs, Transformers, GANs
Mathematics
Linear algebra, calculus, probability, statistics, optimization
DevOps & Docker
Containerization, Docker Compose, image optimization, registries
Kubernetes
Pods, Deployments, Services, HPA, persistent storage, Helm
Cloud Platforms
AWS SageMaker, GCP Vertex AI, Azure ML, serverless deployment
ML Pipelines
Airflow, Kubeflow, MLflow, experiment tracking, versioning
Monitoring
Prometheus, Grafana, Evidently AI, drift detection, alerting
FastAPI & REST
API development, Pydantic validation, async programming, testing
Infrastructure
Terraform, IaC, resource management, cloud automation
Best Practices
Model governance, compliance, cost optimization, documentation
Get Your AI Engineer Certificate
Completed all 4 phases? Claim your professional certificate!
📜 Your certificate includes:
- ✅ Official AI Engineer Complete Path completion
- ✅ Unique certificate ID for verification
- ✅ Shareable on LinkedIn, Twitter, and resume
- ✅ Public verification page
- ✅ Professional PDF download
❓ Frequently Asked Questions
Do I need prior programming experience?
No! Phase 1 starts with Python basics. If you're already comfortable with Python, you can skip to Phase 2.
Is this really 100% free?
Yes! All tutorials, projects, and the certificate are completely free. No hidden costs, no credit card required.
How long does it take to complete?
At 10-15 hours per week, you can complete the path in 16 weeks. However, it's self-paced - take as long as you need!
Will I be job-ready after completing this?
Yes! You'll have hands-on experience with production ML systems, 4 portfolio projects, and the skills companies look for in AI Engineers.
Can I get individual course certificates?
Yes! You can earn separate certificates for Machine Learning, Deep Learning, and MLOps Engineer courses as you complete each phase.