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Our AI Developer Advanced Course (Track 2)
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.
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iT’S FREE
✅ Duration: 16–24 weeks
✅ Format: Hands-on, project-based learning
✅ Prerequisite: Completion of Track 1 or equivalent foundational knowledge
Your Pathway to Success
💼 AI Career Paths After This Course
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🎓 Free Certifications Included
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📜 Google AI for Beginners – Covers fundamental AI principles.
📜 AWS AI Cloud Practitioner – Teaches AI model deployment on cloud platforms.
📜 IBM AI Ethics & Bias Prevention – Focuses on responsible AI development.
📜 DeepLearning.AI Machine Learning Fundamentals – Covers core ML techniques.
💼 AI Career Paths After This Course
🔹 Machine Learning Engineer – Develop AI models for automation, recommendation systems, and predictive analytics.
🔹 AI Software Developer – Build AI-powered applications and integrate ML models into software solutions.
🔹 Data Scientist – Analyze large datasets and optimize AI models for business insights.
🔹 AI Research Assistant – Assist in AI model development and contribute to open-source AI projects.
🔹 AI Ethics & Compliance Specialist – Ensure responsible AI development and mitigate bias in models.
🎓 Track 2: AI Developer Advanced Course Syllabus
Goal: Transition from foundational knowledge to building and deploying real-world AI systems using deep learning, NLP, cloud tools, and MLOps.
📘 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 4.1: Prompt Engineering for Developers
Lesson 4.1.1. Advanced Prompt Structures:
- Few-Shot
- Chain-of-Thought, and Zero-Shot Reasoning
Lesson 4.1.2. System Messages and Constraining LLM Behavior (JSON, XML Output)
- Constraining LLM Behavior (JSON
- XML Output)
Lesson 4.1.3. Introduction to Cost and Latency Optimization (Token Management).
- Optimization
- Token Management
Chapter 4.2: Vector Databases and Embeddings
Lesson 4.2.1. Deep Dive into Vector Embeddings and Similarity Metrics (Cosine, Dot Product
- Embeddings and Similarity Metrics (Cosine
- Cosine, Dot Product)
Lesson 4.2.2. Practical: Setting up a Vector Store (e.g., ChromaDB, Pinecone Free Tier).
- ChromaDB
- Pinecone Free Tier)
Lesson 4.2.3. Document Chunking Strategies and Metadata Management for Retrieval.
- Chunking Strategies
- Metadata Management for Retrieval
Chapter 4.3: Retrieval-Augmented Generation (RAG)
Lesson 4.3.1. Introduction to the RAG Pipeline: Components and Flow.
- RAG Pipeline: Components
- Flow
Lesson 4.3.2. Project: Building a Custom QA Chatbot over Documents using LangChain.
- Building a Custom QA Chatbot
- over Documents using LangChain.
Lesson 4.3.3. LLM Orchestration Frameworks: LangChain/LlamaIndex Agents and Tools
- LangChain/
- LlamaIndex Agents and Tools.
Chapter 5.1: Production-Ready Code & Data Practices
Lesson 5.1.1. Python Best Practices: Type Hinting, Virtual Environments, Dependency Management.
- Type Hinting, Virtual Environments
- Dependency Management
Lesson 5.1.2. Unit Testing and Integration Testing for ML (Model I/O, Feature Functions)
- Testing for ML (Model I/O
- Feature Functions
Lesson 5.1.3. Data Management: Introduction to SQL and Database Integration (e.g., PostgreSQL)
- Introduction to SQL and Database
- Integration (e.g., PostgreSQL)
Chapter 5.2: AI Service Deployment & API Design
Lesson 5.2.1: Building Robust RESTful APIs with FastAPI/Flask.
- Building Robust RESTful APIs
- Using FastAPI/Flask
Lesson 5.2.2: Project: Containerizing an API with Docker & Deployment to a Serverless Endpoint.
- Containerizing an API
- Deployment to a Serverless Endpoint using Docer
Lesson 5.2.3: API Design: Authentication, Authorization, and Rate Limiting for AI Services.
- API Design: Authentication
- Authorization, and Rate Limiting for AI Services
Chapter 5.3: Full-Stack Integration Project (Capstone Extension)
Lesson 5.3.1: Introduction to UI Frameworks for AI: Streamlit or Gradio.
- Streamlit
- Gradio
Lesson 5.3.2: Project: Creating a Frontend for the RAG Chatbot (using Streamlit).
- Creating a Frontend for the RAG Chatbot
- using Streamlit
Lesson 5.3.3: AI System Monitoring: Logging, Error Tracking, and Model Drift Alerts.
- Logging
- Error Tracking, and Model Drift Alerts.
Chapter 6: Capstone Project: Real-Time AI System
Build & deploy a real-time fraud detection system
Lesson 6.1 – Capstone Project: Real-Time AI 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
Lesson 6.2 Capstone Project Submission Guidelines
- Project Report – Document outlining the system design, model architecture, deployment setup, and optimization strategies
- Code Repository (GitHub/Bitbucket) – Fully functional implementation with README for setup instructions
Lesson 6.3: Course Wrap-Up & Next Steps
- Building APIs with FastAPI/Flask
- Containerizing & deploying with Docker + serverless platforms
- Designing secure APIs with authentication, authorization, and rate limiting
- Creating frontends with Streamlit/Gradio
Chapter 7: Build & deploy a real-time fraud detection system
Project Overview: Real-Time Fraud Detection System
- Deep Learning Model – LSTM or CNN for fraud detection
- Dockerized API – FastAPI for real-time inference
- Cloud Deployment – AWS Lambda, Azure ML, or GCP Vertex AI
- Monitoring & Drift Detection – Prometheus, Grafana, Evidently AI
Final Checklist for Submission
- Portfolio Enhancement – Feature this project as a highlight for job applications.
- Certification & Recognition – Earn the AI Developer Career Accelerator completion certificate
- Internship & Job Referrals – Eligible for career opportunities in AI and ML engineering roles.
- Community Engagement – Join AI developer networks to continue learning and collaborating.
🧑🤝🧑 Community & Career Support
- Peer code reviews & study groups
- Mentor-led project feedback
- Resume & portfolio workshops
- Internship & open-source contribution guidance