AI in Supply Chain & Operations

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 neural network architectures

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

Description

Transform supply chain operations with AI. Build demand forecasting, inventory optimization, and logistics planning systems using machine learning and time-series models.

What this course covers: AI in Supply Chain & Operations works through model training and evaluation, 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, deploying models to production 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 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 tuning model performance 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
  Aishwarya N. ★★★★★ 4.0/5 Sample review

  I joined to get better at preparing data for training. The practical exercises on deep learning frameworks (TensorFlow / PyTorch) were exactly what I needed. The hands-on notebooks made backpropagation finally make sense to me.

• S
  Saanvi N. ★★★★★ 4.5/5 Sample review

  Good balance of theory and practice. The neural network architectures lessons helped me apply deploying models to production in real situations. Loved that model evaluation got as much attention as model building.

• M
  Madhuri I. ★★★★★ 4.0/5 Sample review

  The section on model training and evaluation was explained step by step, and the examples made neural network architectures 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.