Neural Networks from Scratch

What you'll learn

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

A comprehensive course on neural networks from scratch, covering core concepts, practical applications, and industry best practices.

What this course covers: Neural Networks from Scratch works through feature engineering and data preparation, neural network architectures, model training and evaluation and deep learning frameworks (TensorFlow / PyTorch).

Skills you will build: preparing data for training, building and evaluating ML models 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 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 feature engineering and data preparation and preparing data for training 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

  Neural Networks from Scratch covered model training and evaluation in more depth than I expected. The pace was right for my level. The hands-on notebooks made backpropagation finally make sense to me.

• D
  Deepa J. ★★★★★ 5.0/5 Sample review

  Well organised. Revisiting the model training and evaluation part a second time really helped it click. The deployment section filled a gap most ML courses skip.

• R
  Rohan P. ★★★★★ 5.0/5 Sample review

  I joined to get better at preparing data for training. The practical exercises on feature engineering and data preparation were exactly what I needed. Loved that model evaluation got as much attention as model building.

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