Computer Vision & Image Recognition

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 deploying and monitoring models

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

Description

Learn computer vision fundamentals and advanced techniques. Build object detection, image segmentation, and facial recognition systems using OpenCV and PyTorch.

What this course covers: Computer Vision & Image Recognition works through working with Python libraries such as NumPy, pandas and scikit-learn, deploying and monitoring models, transformers and large language models and supervised and unsupervised learning.

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

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 working with Python libraries such as NumPy, pandas and scikit-learn and preparing data for training 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

• Y
  Yash B. ★★★★★ 4.5/5 Sample review

  Computer Vision & Image Recognition covered transformers and large language models in more depth than I expected. The pace was right for my level. The deployment section filled a gap most ML courses skip.

• S
  Siddharth A. ★★★★★ 4.5/5 Sample review

  I joined to get better at building and evaluating ML models. The practical exercises on deploying and monitoring models were exactly what I needed. Loved that model evaluation got as much attention as model building.

• I
  Ishaan K. ★★★★★ 4.5/5 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 deploying and monitoring models much easier to follow. 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.