Coursera MOOC / Non-credit 0

Transfer Learning Foundations for AI Models

Name: Transfer Learning Foundations for AI Models
Price: 49 USD
Availability: InStock

About this course

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.

74/100

CourseAsk score

What the provider tells you: 38/45
Who stands behind it: 20/35
How complete the listing is: 16/20

Scores how much the provider publishes and who stands behind it — not how well it is taught.

What you'll learn

Use Python and data science libraries to prepare and analyze machine learning data
Build, train, and evaluate fundamental machine learning models
Explain the learning process of neural networks and CNNs
Apply transfer learning techniques to adapt pretrained models
Select suitable pretrained models for different AI use cases
Describe self-attention, multi-head attention, and transformer architectures

Artificial Intelligence #python #machine learning #data visualization #transfer learning #model evaluation #feature extraction #data analysis #neural networks #cnn #self-attention #transformer architectures #data science libraries

$49.00

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