Transfer Learning and Fine-Tuning for AI Models

Edureka via Coursera Specialization

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This specialization teaches you how to customize pretrained AI models into reliable, deployable systems using transfer learning and fine-tuning with Python, PyTorch, and Hugging Face. It is designed for developers and data scientists who want to move beyond training models from scratch and deliver AI solutions that perform in real business settings. By the end of this specialization, you will be able to: Explain how transfer learning reshapes pretrained models and transformers for new tasks Apply supervised and instruction fine-tuning in PyTorch and Hugging Face using LoRA Analyze model behavior through evaluation metrics, error analysis, and fairness auditing Build and deploy monitored inference services using FastAPI, Docker, and responsible AI practices No prior deep learning or fine-tuning experience is needed — just basic Python and foundational machine learning knowledge to get started. Join us now and begin your journey to become a fine-tuning and AI deployment expert.

Syllabus

Course 1: Transfer Learning Foundations for AI Models
Course 2: Fine-Tuning Techniques for AI Models
Course 3: Deploying Fine-Tuned AI Models

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Building a high-performing AI model is only part of the journey. To deliver real business value, models must be deployed, optimized, monitored, and integrated into production environments. This course equips you with the practical knowledge and tools required to move fine-tuned AI models from development to real-world deployment. You will begin by exploring model packaging, inference pipelines, APIs, and deployment architectures used to serve AI models efficiently. Next, you will learn how to deploy models using containers, cloud platforms, and scalable serving frameworks while optimizing latency, throughput, and resource utilization. Finally, you will explore production monitoring, model versioning, security, and continuous deployment practices to ensure deployed AI systems remain reliable, secure, and maintainable over time. By the End of This Course, You Will Be Able To: - Deploy fine-tuned AI models using modern serving frameworks and deployment workflows. - Apply model optimization techniques to improve inference performance and scalability. - Analyze production deployments using monitoring, logging, and version management practices. - Evaluate deployment architectures to select reliable and secure solutions for AI applications. Designed for AI engineers, machine learning practitioners, software developers, and MLOps professionals, this course provides the practical skills needed to successfully deploy and manage production-ready AI models.
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Pretrained AI models provide an excellent starting point, but real-world AI applications often require them to be adapted for specific tasks and domains. In this course, you will learn the practical techniques used to prepare high-quality datasets, fine-tune large language models, optimize training workflows, and evaluate model performance using industry-standard practices. You will begin by exploring data preparation techniques, including data cleaning, tokenization, dataset splitting, label engineering, and task formulation. Next, you will learn supervised fine-tuning concepts such as training dynamics, learning rate scheduling, gradient accumulation, hyperparameter tuning, and instruction fine-tuning through practical demonstrations. Finally, you will evaluate fine-tuned models using metrics such as Accuracy, F1 Score, BLEU, ROUGE, and Perplexity, perform error analysis, and explore parameter-efficient fine-tuning techniques including LoRA and PEFT. By the End of This Course, You Will Be Able To: - Prepare high-quality datasets for fine-tuning AI models. - Apply supervised fine-tuning techniques to optimize model performance. - Analyze model quality using standard evaluation metrics and error analysis. - Evaluate fine-tuning strategies for different AI applications. Designed for AI engineers, machine learning practitioners, software developers, and data scientists, this course provides the practical skills needed to customize pretrained AI models for real-world applications.
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Transfer learning has transformed modern artificial intelligence by making it possible to build powerful AI solutions without training models from scratch. This course provides a practical introduction to machine learning, neural networks, transfer learning, and transformer architectures while helping you develop hands-on skills using Python and widely used data science libraries. You will begin by working with NumPy, Pandas, Matplotlib, and Seaborn to prepare, analyze, and visualize data for machine learning. You will then build and evaluate your first machine learning models before exploring how neural networks learn, how CNNs extract features, and how pretrained models can be adapted through transfer learning. The course concludes with transformer fundamentals, including self-attention, multi-head attention, encoder-decoder architectures, and the evolution of modern transformer families. You will also learn how to choose between transfer learning and training from scratch and select the right pretrained model for different AI applications. By the End of This Course, You Will Be Able To: - Apply Python and data science libraries to prepare and analyze machine learning data. - Build, train, and evaluate fundamental machine learning models. - Explain how neural networks and convolutional neural networks learn. - Apply transfer learning techniques to adapt pretrained models. - Select suitable pretrained models for different AI use cases. - Explain self-attention, multi-head attention, and transformer architectures. Designed for aspiring AI engineers, machine learning practitioners, software developers, data professionals, students, and technology enthusiasts, this course provides a practical foundation for understanding and applying transfer learning and pretrained AI models.