Final Project
The final project gives you the chance to put your bootcamp knowledge and newly acquired skills to the test in a dynamic, hands-on environment. It’s an opportunity to create something real, showcase your technical abilities, and develop a project that will be a key part of your professional portfolio. It allows you to express your creativity and highlight how much you’ve evolved throughout your bootcamp experience.
Additionally, the final project is designed to replicate the challenges you will encounter in a real tech job, enabling you to showcase your skills in solving complex problems and equipping you for the expectations of your future career.
- Problem Identification: Choose a real-world problem relevant to your industry or field of interest. Clearly define the project scope and objectives, highlighting how advanced deep learning techniques could enhance the solution.
- Data Collection & Preprocessing: Gather data from various sources, clean, and preprocess it to handle missing values, outliers, and inconsistencies. Ensure the data is suitable for deep learning models, including normalization and augmentation if necessary.
- Exploratory Data Analysis (EDA): Perform data visualization and statistical analysis to identify trends, correlations, and insights. Refine your project direction based on EDA findings, while considering the suitability for deep learning architectures like CNNs, RNNs, or transformers.
- Model Building & Evaluation: Develop and train machine learning models, incorporating advanced deep learning techniques such as Convolutional Neural Networks (CNNs) for image data, Recurrent Neural Networks (RNNs) or LSTMs for time series or sequence data, or transformer models for NLP tasks. Evaluate model performance using metrics like accuracy, precision, recall, or AUC, and apply hyperparameter tuning to optimize deep learning models.
- Deployment & Presentation: Deploy the final model using web frameworks, APIs, or cloud-based services, ensuring scalability for deep learning models. Present your findings, model performance, and business or real-world impact to stakeholders in a professional setting.