Transfer Learning Techniques

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand transformers and large language models
• Understand neural network architectures

Planned outline shown above (sample metadata). The detailed module and lesson list for this course hasn't been published yet.

Description

Master transfer learning techniques with hands-on projects, real-world examples, and expert guidance designed for ai courses learners.

What this course covers: Transfer Learning Techniques works through transformers and large language models, neural network architectures, deep learning frameworks (TensorFlow / PyTorch) and working with Python libraries such as NumPy, pandas and scikit-learn.

Skills you will build: tuning model performance, building and evaluating ML models and preparing data for training.

Practical applications: prediction and classification systems, computer vision and NLP applications and AI features inside products.

Who should take this course: developers, data analysts and engineering students moving into machine learning and AI. It is designed for learners with solid experience in machine learning and AI who want advanced, in-depth practice.

Learning outcomes: by the end of the course you should be able to apply transformers and large language models and tuning model performance to your own work or projects, and know which areas to practise next.

Requirements

• Solid working knowledge of machine learning and AI
• Comfort with intermediate concepts
• A computer with internet access

Your instructor

• A
  Arnav S. ★★★★★ 5.0/5 Sample review

  The section on deep learning frameworks (TensorFlow / PyTorch) was explained step by step, and the examples made working with Python libraries such as NumPy, pandas and scikit-learn much easier to follow. Loved that model evaluation got as much attention as model building.

• A
  Akhil R. ★★★★★ 5.0/5 Sample review

  Clear structure from neural network architectures through to deep learning frameworks (TensorFlow / PyTorch). I'd like a few more practice tasks, but overall very useful. The deployment section filled a gap most ML courses skip.

• P
  Pooja H. ★★★★★ 5.0/5 Sample review

  Good balance of theory and practice. The transformers and large language models lessons helped me apply tuning model performance in real situations. The hands-on notebooks made backpropagation finally make sense to me.

Sample reviews are illustrative demo content, not verified purchases. Ratings from learners will appear here once submitted.