Natural Language Processing with Transformers

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand working with Python libraries such as NumPy, pandas and scikit-learn
• Understand deep learning frameworks (TensorFlow / PyTorch)

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

Description

Deep dive into NLP using transformer models. Build chatbots, sentiment analyzers, and text summarizers using BERT, GPT, and T5 architectures.

What this course covers: Natural Language Processing with Transformers works through working with Python libraries such as NumPy, pandas and scikit-learn, deep learning frameworks (TensorFlow / PyTorch), feature engineering and data preparation and supervised and unsupervised learning.

Skills you will build: building and evaluating ML models, tuning model performance 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 working with Python libraries such as NumPy, pandas and scikit-learn and building and evaluating ML models 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

• R
  Rajat H. ★★★★★ 5.0/5 Sample review

  Natural Language Processing with Transformers covered feature engineering and data preparation in more depth than I expected. The pace was right for my level. The hands-on notebooks made backpropagation finally make sense to me.

• B
  Bhavna S. ★★★★★ 5.0/5 Sample review

  Good balance of theory and practice. The deep learning frameworks (TensorFlow / PyTorch) lessons helped me apply tuning model performance in real situations. Loved that model evaluation got as much attention as model building.

• S
  Saanvi N. ★★★★★ 5.0/5 Sample review

  The section on working with Python libraries such as NumPy, pandas and scikit-learn was explained step by step, and the examples made deep learning frameworks (TensorFlow / PyTorch) much easier to follow. The deployment section filled a gap most ML courses skip.

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