Welcome To
Our FREE AI Developer Advanced Course-Hands-On, Project-Based Learning-(Track 2)
Note: You must complete the prerequisite course, Foundations for AI Developer Track1, before you can enroll in this course.
Structured for job readiness with industry-aligned projects, certifications, and community support. All resources are free or open-source. Create a free account or login if you already have one with us to get started.
Building on the Track 1: AI Developer Fundamentals – Prerequisite Course, here’s a structured and expanded curriculum for the AI Developer Advanced Course, modeled after the format and flow of Track 1. This version emphasizes hands-on, project-based learning with clear chapters and lessons grouped by theme.
🎓 Track 2: AI Developer Advanced Course
Goal: Transition from foundational knowledge to building and deploying real-world AI systems using deep learning, NLP, cloud tools, and MLOps.
✅ Duration: 16–24 weeks
✅ Format: Hands-on, project-based learning
✅ Prerequisite: Completion of Track 1 or equivalent foundational knowledge
📘 Chapter 1 – Deep Learning Foundations & Architectures
🔹 Lesson 1.1 – Neural Networks & Backpropagation
- Activation functions, forward/backward pass
- Loss functions & optimization
- Build a neural net from scratch (NumPy)
🔹 Lesson 1.2 – Convolutional Neural Networks (CNNs)
- Filters, pooling, padding
- Image classification project (e.g., CIFAR-10)
🔹 Lesson 1.3 – Recurrent Neural Networks (RNNs) & LSTMs
- Sequence modeling, vanishing gradients
- Time series forecasting project
🔹 Lesson 1.4 – Reinforcement Learning Basics
- Q-learning, policy gradients
- Gridworld or game-playing agent
📘 Chapter 2 – Natural Language Processing (NLP)
🔹 Lesson 2.1 – Text Preprocessing & Tokenization
- Stopwords, stemming, TF-IDF
- Build a spam classifier
🔹 Lesson 2.2 – Word Embeddings & Transformers
- Word2Vec, GloVe, attention mechanisms
- Visualize embeddings with PCA/t-SNE
🔹 Lesson 2.3 – Fine-Tuning Pretrained Models (BERT, GPT)
- Hugging Face Transformers
- Sentiment analysis or Q&A chatbot
🔹 Lesson 2.4 – Speech & Multimodal AI
- Intro to speech-to-text, image+text models
- Build a voice-commanded assistant
📘 Chapter 3 – AI Deployment & Cloud Integration
🔹 Lesson 3.1 – Model Packaging with Docker
- Dockerfiles, containers, reproducibility
- Containerize a trained model
🔹 Lesson 3.2 – Cloud Deployment (AWS, Azure, GCP)
- REST APIs with FastAPI or Flask
- Deploy to cloud with Streamlit or Lambda
🔹 Lesson 3.3 – Kubernetes & Serverless AI
- Pods, services, autoscaling
- Deploy a scalable inference service
🔹 Lesson 3.4 – Real-Time Inference & Monitoring
- Webhooks, latency, logging
- Build a real-time fraud detection API
📘 Chapter 4 – MLOps & AI DevOps
🔹 Lesson 4.1 – CI/CD for Machine Learning
- GitHub Actions, model versioning
- Automate model testing & deployment
🔹 Lesson 4.2 – Model Monitoring & Drift Detection
- Track metrics, detect concept drift
- Build a dashboard with Prometheus/Grafana
🔹 Lesson 4.3 – Automating Pipelines with Airflow
- DAGs, scheduling, data ingestion
- Build an end-to-end ML pipeline
🔹 Lesson 4.4 – Responsible AI in Production
- Bias audits, explainability (SHAP, LIME)
- Integrate fairness checks into CI/CD
🧪 Capstone Project: Real-Time AI System
Build & deploy a real-time fraud detection system
- Deep learning model (e.g., LSTM or CNN)
- Dockerized API with FastAPI
- Deployed to cloud with Kubernetes
- Monitored with Prometheus
- Audited for fairness using SHAP/Fairlearn
🧑🤝🧑 Community & Career Support
- Peer code reviews & study groups
- Mentor-led project feedback
- Resume & portfolio workshops
- Internship & open-source contribution guidance