Machine Learning & AI Engineer
From mathematical foundations to neural network architectures and production MLOps. Master Python, NumPy, Pandas, Scikit-Learn, PyTorch, model training, feature engineering, and deploying models to cloud endpoints.
🎯 Core Competencies You Will Master
Target Job Roles & Titles
Course Outline & Milestones
📁 Phase 1: Python, Math & Data Engineering for ML
2 CoursesAdvanced Python, NumPy & Pandas for High-Performance ML
Vectorized operations, multidimensional arrays, data manipulation, and exploratory data analysis.
Linear Algebra, Calculus & Statistics for Machine Learning
Matrices, eigenvectors, gradients, probability distributions, and hypothesis testing.
📁 Phase 2: Classical Machine Learning & Deep Learning
3 CoursesSupervised & Unsupervised Learning with Scikit-Learn
Regression, decision trees, random forests, XGBoost, k-means clustering, and PCA dimensionality reduction.
Deep Learning with PyTorch: Tensors, Autograd & Neural Nets
Building multilayer perceptrons, backpropagation, loss functions, optimizers, and regularization.
Transformers & Modern NLP Architecture Essentials
Self-attention mechanisms, BERT, GPT tokenization, embeddings, and transfer learning.
📁 Phase 3: MLOps, Model Serving & Capstone
3 CoursesBuilding Production Inference APIs with FastAPI & Docker
Asynchronous endpoints, request validation with Pydantic, containerization, and low-latency inference.
MLOps: Experiment Tracking, Model Registry & Data Drift
Using MLflow, automated pipelines, model versioning, and continuous model evaluation in production.
Graduation Capstone: End-to-End Enterprise ML Solution
Deliver an end-to-end trained model with clean documentation, unit tests, and live cloud API.
Real-World Capstone Project
Production ML Model Pipeline: Train, optimize, package, and deploy a machine learning model capable of real-time predictions served via FastAPI and monitored with Prometheus.
Official PBIAcademy Career Diploma
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