A course designed for aspiring AI and ML Engineers should build a strong technical foundation, progressing from core concepts to practical deep learning and application development. Here is a suggested course outline, titled “AI & ML Engineering Fundamentals”:
Course Outline: AI & ML Engineering Fundamentals
Module 1: Foundational AI and Python for ML
- Introduction to AI/ML: Defining AI, Machine Learning (ML), and Deep Learning (DL). History and ethical considerations (Responsible AI).
- Essential Math & Statistics: Review of Linear Algebra (vectors, matrices), Calculus (derivatives, gradients), Probability, and Statistics.
- Python Programming Fundamentals: Data structures, control flow, functions, and object-oriented programming (OOP).
- ML Libraries: Introduction to key libraries: NumPy (numerical operations), Pandas (data manipulation), and Matplotlib/Seaborn (data visualization).
- Development Environment Setup: Setting up a working environment (VS Code, Jupyter Notebooks/Lab, virtual environments).
Module 2: Classic Machine Learning and Data Workflow
- Data Preprocessing and Feature Engineering: Data cleaning, handling missing data, feature scaling, encoding categorical variables, and dimensionality reduction (e.g., PCA).
- Supervised Learning:
- Regression: Linear and Logistic Regression.
- Classification: K-Nearest Neighbors (KNN), Support Vector Machines (SVM), and Decision Trees.
- Unsupervised Learning: Clustering (K-Means, Hierarchical Clustering) and Anomaly Detection.
- Model Evaluation and Validation: Metrics (Accuracy, Precision, Recall, F1-Score, AUC, MSE), cross-validation, and bias-variance trade-off.
- Ensemble Methods: Introduction to Random Forests and Gradient Boosting (e.g., XGBoost, LightGBM).
Module 3: Deep Learning and Frameworks
- Neural Networks Fundamentals: Perceptrons, Multi-Layer Perceptrons (MLPs), activation functions, backpropagation, and optimization (SGD, Adam).
- Deep Learning Frameworks: In-depth training with PyTorch and/or TensorFlow/Keras. Understanding Tensors and GPU acceleration.
- Computer Vision (CV):
- Convolutional Neural Networks (CNNs): Architectures (LeNet, AlexNet, VGG), Transfer Learning, and object detection concepts.
- Natural Language Processing (NLP):
- Recurrent Neural Networks (RNNs) and LSTMs.
- Introduction to Transformers and Large Language Models (LLMs).
- Basic Prompt Engineering.
Module 4: Practical Applications and MLOps
- Reinforcement Learning (RL) Basics: Concepts like agents, environments, rewards, and simple algorithms (e.g., Q-learning).
- Introduction to MLOps: Version control (Git), experiment tracking, model serialization (e.g., Pickle, joblib), and containerization (Docker).
- Model Deployment: Basics of deploying a trained model as a web service (e.g., using Flask/Streamlit) or to a cloud platform (e.g., Azure ML, Google AI Platform).
- Responsible AI: Deep dive into fairness, transparency, and model interpretability (e.g., using SHAP or LIME).
This curriculum is structured to provide both the theoretical knowledge and the practical, code-centric skills required for an AI/ML Engineer role
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