Machine Learning and Deep Learning for Software Engineers

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This specialization empowers software engineers, backend developers, and full-stack professionals to integrate, deploy, and maintain machine learning models within production software systems. You will approach ML through an engineering lens — emphasizing software design, APIs, scalability, and maintainability rather than theory alone. Starting with applied ML fundamentals, you will build and train models using Scikit-learn, TensorFlow, and PyTorch while writing modular, testable ML code. As you progress, you will convert ML models into production-ready APIs using FastAPI and Flask, design scalable microservices for inference, and manage model versioning and performance optimization. The third course introduces MLOps foundations — covering reproducibility, experiment tracking, and version control using Git, DVC, and MLflow. The final course brings everything together with CI/CD pipelines, continuous delivery of models, monitoring inference performance and data drift, and implementing retraining and rollback strategies. By the end, you will have the engineering competencies to build, serve, operate, and maintain ML-powered applications across the full production lifecycle.

Syllabus

Course 1: Applied Machine Learning Systems with FastAPI for Developers
Course 2: Deep Learning: Train Neural Networks and Deploy with Docker
Course 3: Transformers and NLP: Fine-Tuning Models with Hugging Face

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This course teaches software developers how to implement, deploy, and maintain machine learning systems using Python, scikit-learn, FastAPI, and Docker. You'll learn to build ML pipelines, preprocess data, evaluate models, and serve them as production-ready REST APIs. Module 1 covers core ML algorithms and workflows, including supervised and unsupervised learning paradigms. You'll implement regression, classification, and clustering using scikit-learn and learn to evaluate models using appropriate metrics. Module 2 focuses on data preparation and feature engineering. You'll clean and preprocess data using pandas, construct feature pipelines with transformations and scaling, and optimize feature sets to enhance model performance. Module 3 explores building and testing ML code. You'll structure ML codebases for modularity and reuse, implement testing workflows using pytest, and learn logging and debugging techniques for ML pipelines. Module 4 covers serving and deploying ML models. You'll expose models as REST APIs using FastAPI, containerize services with Docker, and evaluate deployed models using inference testing. By the end of this course, you will: • Implement and evaluate ML algorithms for classification, regression, and clustering tasks • Build reproducible data pipelines with preprocessing and feature engineering • Develop modular, tested ML codebases following software engineering best practices • Deploy ML models as containerized REST APIs using FastAPI and Docker Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
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This Deep Learning and Neural Networks in Production course equips you with the skills to design, train, and deploy neural networks using PyTorch, TensorFlow, FastAPI, and Docker. Whether you're building models from scratch or serving them in production, this course bridges the gap between deep learning theory and real-world deployment. In Module 1, you'll explore the foundations of neural networks — building and training feed-forward networks, understanding activations, losses, and optimizers in PyTorch. Module 2 focuses on robust training and validation loops, experiment tracking with TensorBoard and Weights & Biases, and checkpoint analysis. Module 3 covers packaging trained models for inference, serving them via FastAPI, and evaluating latency and reliability. Module 4 teaches containerization with Docker, production monitoring, logging, and scaling strategies. By the end of this course, you will: - Design and train neural networks using PyTorch and TensorFlow - Track and visualize model performance using TensorBoard and Weights & Biases - Serve trained deep learning models through FastAPI for real-time inference - Package, deploy, and scale deep learning applications with Docker in production Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.
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Transformers, Fine-Tuning, and Model Evaluation is designed for learners with deep learning and NLP experience who want to master transformer architectures, fine-tune pre-trained models using Hugging Face, and deploy production-ready NLP solutions. You'll begin by exploring the transformer architecture in depth — including self-attention mechanisms, positional encodings, and model families like BERT, GPT, and T5. Next, you'll learn to prepare datasets, fine-tune models for classification tasks, and evaluate results using metrics like F1, precision, and confusion matrices. The third module covers reproducibility and version control using DVC and Git, along with publishing models to the Hugging Face Hub. Finally, you'll build and deploy transformer inference APIs using FastAPI, optimize performance through quantization, and integrate CI/CD practices for production systems. By the end of this course, you will: - Apply transformer architectures to solve real-world NLP tasks - Fine-tune and evaluate pre-trained models using Hugging Face Transformers and Datasets - Build reproducible ML pipelines with DVC and Git version control - Deploy and test transformer-based inference APIs using FastAPI Disclaimer: This is an independent educational resource created by Board Infinity for informational and educational purposes only. This course is not affiliated with, endorsed by, sponsored by, or officially associated with any company, organization, or certification body unless explicitly stated. The content provided is based on industry knowledge and best practices but does not constitute official training material for any specific employer or certification program. All company names, trademarks, service marks, and logos referenced are the property of their respective owners and are used solely for educational identification and comparison purposes.