CNNs have reshaped industries from healthcare to autonomous vehicles. In this training, kodestree walks you through the full CNN lifecycle- layers, filters, pooling, backpropagation, and transfer learning. You will work with leading frameworks like TensorFlow and PyTorch, tackle curated datasets, and graduate with a portfolio of projects that showcase actual, deployable skills rather than textbook theory.
Prerequisites
- Basic Python programming (functions, loops, data structures)
- Foundational knowledge of linear algebra – matrices, vectors, dot products
- Elementary statistics – mean, variance, probability distributions
- Familiarity with machine learning concepts such as supervised learning and loss functions
- Working knowledge of NumPy and data manipulation using pandas
- Any exposure to neural network fundamentals is a plus, though not mandatory
Course Objectives
- Understand the mathematical and architectural foundations of Convolutional Neural Networks
- Build CNN models from scratch using TensorFlow and PyTorch
- Implement convolution, pooling, batch normalisation, and dropout layers correctly
- Apply modern CNN architectures such as VGG, ResNet, Inception, and EfficientNet
- Use transfer learning to fine-tune pre-trained models on custom datasets
- Design solutions for image classification, object detection, and semantic segmentation
- Deploy trained CNN models to production environments and cloud platforms
- Interpret model behaviour using Grad-CAM and other explainability techniques
What You Will Learn
After completing this course, you will get the following skills.
- CNN Architecture and Core Concepts
- Advanced Architectures
- Transfer Learning and Fine-Tuning
- Object Detection and Segmentation
- Model Optimisation and Deployment
Who Is This Course For?
This training is designed for anyone who wants to build and apply CNNs in professional or research settings.
- Software developers moving into machine learning or AI roles
- Data scientists who want to add computer vision to their skill set
- Machine learning engineers building image-based products
- Research professionals working in healthcare, satellite imaging, or robotics
- Final-year students and postgraduates pursuing AI or data science careers
- IT professionals and cloud engineers who manage AI pipelines
- Entrepreneurs building computer vision products or SaaS tools
Tools and Technologies Covered
- Python 3.x – primary programming language throughout the course
- TensorFlow 2.x and Keras – for building and training deep learning models
- PyTorch – hands-on model development and research experimentation
- OpenCV – image preprocessing and augmentation pipelines
- NumPy and Pandas – data handling and numerical computation
- Matplotlib and Seaborn – visualising training metrics and results
- Google Colab and Jupyter Notebooks – interactive coding environment
- Hugging Face Transformers – vision transformer integration
- ONNX and TensorFlow Lite – model export and edge deployment
- AWS SageMaker / Google Vertex AI – cloud-based model training and deployment
- Grad-CAM and SHAP – model interpretability and explainability
- Weights and Biases (W&B) – experiment tracking and hyperparameter tuning
Career Outcomes
Completing this course positions you for:
- Computer Vision Engineer
- Deep Learning Engineer
- Machine Learning Engineer
- AI Research Scientist
- Data Scientist (Vision Specialist)
- Medical Imaging Analyst
- Autonomous Systems Engineer
- NLP + Vision Engineer
Why Choose kodestree for This Training?
kodestree has trained thousands of professionals worldwide with a curriculum that stays current with industry demands and real hiring trends.
- Expert-led live training by certified AI practitioners
- Hands-on projects aligned with industry hiring standards
- Flexible batch timings for working professionals
- Globally recognised certification upon completion
- Lifetime access to updated course recordings
- Dedicated placement assistance and mock interview prep
