Machine Learning & Deep Learning Bootcamp

What you'll learn

• Building and evaluating ML models
• Preparing data for training
• Tuning model performance
• Deploying models to production
• Understand model training and evaluation
• 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

Master machine learning and deep learning with PyTorch and TensorFlow. Covers neural networks, CNNs, RNNs, and transformer architectures with real-world projects.

What this course covers: Machine Learning & Deep Learning Bootcamp works through model training and evaluation, deep learning frameworks (TensorFlow / PyTorch), neural network architectures and working with Python libraries such as NumPy, pandas and scikit-learn.

Skills you will build: building and evaluating ML models, preparing data for training and deploying models to production.

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 suits learners who already know the basics of machine learning and AI and want to go deeper.

Learning outcomes: by the end of the course you should be able to apply model training and evaluation and building and evaluating ML models to your own work or projects, and know which areas to practise next.

Requirements

• Basic knowledge of machine learning and AI
• A computer with internet access
• Willingness to complete practice tasks

Your instructor

• A
  Ananya B. ★★★★★ 5.0/5 Sample review

  Good balance of theory and practice. The neural network architectures lessons helped me apply deploying models to production in real situations. The deployment section filled a gap most ML courses skip.

• A
  Anjali W. ★★★★★ 5.0/5 Sample review

  Machine Learning & Deep Learning Bootcamp covered deep learning frameworks (TensorFlow / PyTorch) in more depth than I expected. The pace was right for my level. Loved that model evaluation got as much attention as model building.

• S
  Sameer F. ★★★★★ 5.0/5 Sample review

  I joined to get better at building and evaluating ML models. The practical exercises on model training and evaluation were exactly what I needed. 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.