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Advanced AI Engineer Program (6-Month Course | 24 Weeks)

A complete 6-month journey to become a professional AI Engineer, mastering everything from advanced Python and machine learning to deep learning, NLP, generative AI, and MLOps.
The program focuses on hands-on projects, real-world AI applications, and career preparation ensuring you’re ready for high-paying roles or freelance opportunities.

Why this Program works ?

  • Comprehensive Curriculum: From Python automation to full AI model deployment and MLOps.
  • Hands-on Projects: Every phase includes mini-projects and one major capstone project.
  • Industry Mentorship: Live weekend classes with guided learning and feedback.
  • Optional Certification Prep: AWS ML / Google ML / Azure AI labs and exam guidance.
  • Career Support: Resume, LinkedIn, and portfolio-building assistance.

//Solidify programming and automation foundations.

· Object-Oriented Programming (OOP) and modular coding · Advanced data structures: sets, stacks, queues, trees, and graphs · File handling (CSV, JSON, XML) and error handling · Workflow automation using Python + no-code tools (Zapier, Make) Mini Project: Automate an end-to-end business process using Python. Outcome: Build scalable automation scripts for business workflows.

// Weeks 1–2

< Advanced Python & Automation> |

// Master ML algorithms and model building.

· Supervised & Unsupervised Learning · Regression, Classification, and Clustering models · Feature Engineering & Data Preprocessing · Model evaluation (accuracy, precision, recall, F1-score) · Hyperparameter tuning and ML pipelines Mini Project: Predictive analytics using real datasets. Outcome: Develop and deploy machine learning models confidently.

// Weeks 3–6

< Machine Learning Foundations > |

// Master modern deep learning architectures.

· Neural Networks (perceptron, backpropagation, activation functions) · Convolutional Neural Networks (CNNs) for image recognition · Recurrent Neural Networks (RNNs) & LSTMs for text/sequence data · Introduction to Transformers and Attention Mechanisms · Transfer Learning using pre-trained models (ResNet, BERT, etc.) Mini Project: Image classification or sentiment analysis project. Outcome: Build and train deep learning models using TensorFlow or PyTorch

// Weeks 7-10

< Deep Learning & Neural Networks> |

// Build intelligent systems that understand human language.

· Text preprocessing, tokenization, stemming, lemmatization · Feature extraction: Bag-of-Words, TF-IDF, Word2Vec, GloVe · Transformers and fine-tuning pre-trained LLMs (BERT, GPT, etc.) · Sentiment analysis, chatbots, text summarization Mini Project: AI chatbot or text classification model. Outcome: Build NLP systems for business and customer automation.

// Weeks 11-14

< Natural Language Processing (NLP) > |

//Build autonomous AI agents and generative applications.

· Advanced prompt engineering & LLM integration with Python · OpenAI API, LangChain, and AutoGen for multi-agent systems · Building intelligent assistants and content generators · Introduction to autonomous AI workflows Mini Project: Design a self-running AI process (e.g., automated research assistant). Outcome: Build and deploy practical Generative AI applications.

// Weeks 15-17

< Generative & Agentic AI > |

//Take AI from experimentation to production.

· Model deployment using Flask/FastAPI · Docker containerization and version control with Git · Cloud deployment (AWS, GCP, or Azure basics) · CI/CD pipelines and model monitoring Mini Project: Deploy an ML model as a live web service. Outcome: Deploy, manage, and maintain production-grade AI systems.

// Weeks 18-20

<AI Deployment & MLOps> |

// End-to-end AI solution building.

· Choose a real-world AI problem (e.g., predictive analytics, chatbot, recommendation system) · Data collection, model development, evaluation, and deployment · Documentation and presentation Mentorship: Peer and instructor review. Outcome: A full-scale portfolio project demonstrating all learned skills.

// Weeks 21-23

<Capstone Project Development> |

// Transition from learning to earning.

· Resume and LinkedIn optimization · GitHub portfolio enhancement · Freelancing: finding clients, pricing, and project pitching · Mock interviews and placement support (Delhi/NCR focus) Outcome: Students graduate with projects, certifications, and real-world readiness.

// Week 24

<Career & Freelance Readiness> |

//Tools and Technologies Used

Python

Open Ai API

LangChain

Autogen

CrewAI

Zapier

Make

Git

GitHub

BeautifulSoup

Pandas

NummPy

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