AI Engineer Certification — Build, Train & Deploy ML Models | BeinGenie

• CS / IT / ECE / EEE B.Tech students
• Final year students building final year projects
• M.Tech students specializing in AI/ML
• Students targeting AI roles at top companies

• Python / Java / C++ developers adding AI skills
• Backend developers building AI-powered APIs
• Web developers integrating ML models
• Developers at product companies pivoting to AI

• Data analysts moving into ML Engineering
• BI analysts wanting predictive modeling skills
• SQL/Excel professionals going into data science
• Business analysts adding ML to their toolkit

• Python for ML — NumPy, Pandas, Matplotlib, data manipulation
• Statistics fundamentals — mean, variance, distributions, hypothesis testing
• Linear algebra for ML — vectors, matrices, dot products (intuitive, not theoretical)
• Calculus basics — gradient descent intuition (no heavy math)
• Setting up environment — Jupyter Notebooks, Google Colab, Anaconda
• Data loading, exploration, cleaning — real-world messy datasets

• Supervised learning — regression (Linear, Polynomial), classification (Logistic, SVM, Decision Trees)
• Unsupervised learning — K-Means clustering, PCA, dimensionality reduction
• Ensemble methods — Random Forest, Gradient Boosting, XGBoost
• Model evaluation — accuracy, precision, recall, F1, ROC-AUC, confusion matrix
• Bias-variance tradeoff, overfitting, underfitting, cross-validation
• Feature engineering — feature selection, encoding, scaling, handling missing data
• Scikit-learn end-to-end ML pipeline — from raw data to trained model

• Neural network architecture — neurons, layers, activation functions, forward/backprop
• TensorFlow + Keras — build, compile, train, evaluate neural networks
• PyTorch fundamentals — tensors, autograd, training loops
• Convolutional Neural Networks (CNN) — image classification, feature extraction
• Recurrent Neural Networks (RNN, LSTM) — sequence data, time series
• Regularization — Dropout, Batch Normalization, L1/L2
• Transfer learning — fine-tuning pretrained models (ResNet, VGG, BERT)

• NLP fundamentals — tokenization, word embeddings (Word2Vec, GloVe)
• Transformer architecture — attention mechanism, self-attention, BERT, GPT
• Hugging Face Transformers — loading, fine-tuning, inference
• Text classification, sentiment analysis, NER, question answering
• Building applications with LLM APIs (OpenAI, Claude, Gemini)
• RAG (Retrieval-Augmented Generation) — building AI apps with your own data
• Prompt engineering for developers — system prompts, chain-of-thought, few-shot

• OpenCV fundamentals — image processing, transformations, filters
• Image classification with CNNs — training on custom datasets
• Object detection — YOLO (v8, v11), bounding boxes, confidence scores
• Image segmentation — U-Net, Mask R-CNN basics
• Face detection and recognition systems
• Real-world project: build a defect detection system / face attendance system

• Model serialization — saving, loading models (Pickle, ONNX, TorchScript)
• FastAPI — building REST APIs to serve ML models in production
• Docker — containerizing your ML application for any environment
• Cloud deployment — AWS SageMaker, Google Vertex AI, Azure ML (overview + hands-on)
• MLflow — experiment tracking, model registry, versioning
• CI/CD for ML — automated retraining, monitoring, data drift detection
• Deploying on free cloud — Hugging Face Spaces, Render, Railway

• Build and deploy a complete end-to-end AI application (your choice of domain)
• GitHub portfolio — showcasing your ML projects professionally
• LinkedIn AI Engineer profile optimization
• AI/ML interview preparation — concepts, coding rounds, system design
• Leetcode ML coding patterns + Kaggle competition strategies
• BeinGenie assessment and AI Engineer Certificate issued

• Python 3.x
• NumPy, Pandas
• Matplotlib, Seaborn
• Scikit-learn

• TensorFlow + Keras
• PyTorch
• Hugging Face
• OpenCV

• FastAPI
• Docker
• Streamlit
• Hugging Face Spaces

• AWS SageMaker
• Google Vertex AI
• MLflow
• GitHub Actions (CI/CD)

End-to-end regression model — data cleaning → feature engineering → model training → deployed FastAPI endpoint.

NLP classification — text preprocessing → TF-IDF / BERT fine-tuning → deployed web app on Hugging Face Spaces.

CNN model on custom dataset → transfer learning with ResNet → Streamlit web app deployed to cloud.

Build a document Q&A chatbot using OpenAI/Claude API + vector database (FAISS/ChromaDB) — real production-ready AI app.

LSTM time series model → training on historical data → predicting future price trends — full MLflow tracking.

Design, build, and deploy an end-to-end AI application of your choice — this becomes the highlight of your GitHub portfolio.