AI Engineer

Topics Covered:

• Python for AI/ML development
• Jupyter & Google Colab for experimentation
• Git, GitHub, and version control
• OOP, functions, and modular code for ML pipelines

Topics Covered:

• Linear Algebra: vectors, matrices, eigenvalues
• Calculus: gradients and optimization
• Probability & statistics essentials
• Bayesian thinking and distributions

Topics Covered:

• Supervised & Unsupervised Learning
• Scikit-learn workflows and pipelines
• Model selection and evaluation (cross-validation, ROC, F1)
• Hyperparameter tuning (GridSearch, RandomizedSearch)

Topics Covered:

• Neural networks (forward/backward pass)
• Keras and TensorFlow basics
• CNNs for image data
• RNNs/LSTMs for sequence modeling

Topics Covered:

• PyTorch for dynamic computation graphs
• Transfer Learning with pretrained models
• GANs: concepts, DCGAN, conditional GANs
• Custom architectures and loss functions

Topics Covered:

• Text cleaning and vectorization (TF-IDF, Word2Vec)
• spaCy & NLTK for NLP tasks
• Transformer models (BERT, GPT intro)
• Text classification, summarization, and sentiment analysis

Topics Covered:

• OpenCV image processing
• Image augmentation techniques
• CNN tuning and object detection
• YOLO and other pretrained CV models

Topics Covered:

• Model packaging and versioning
• Docker for containerization
• CI/CD basics for ML
• Monitoring and performance logging

Topics Covered:

• AWS SageMaker: training and deployment
• Google Vertex AI & AutoML
• Azure ML pipelines and endpoints
• Model registry and experimentation tools

Topics Covered: