Essential Tasks for Machine Learning Projects with Unstructured Data

A concise overview of the six vital tasks in machine learning projects handling unstructured data, covering aspects from feature engineering to workflow optimization.

Tags: unstructured-data-ml project-management machine-learning-tasks workflow-optimization feature-engineering

Category: Deep Learning

Essential Tasks for Machine Learning Projects with Unstructured Data

Summary

A predictive modeling machine learning project can be divided into six main tasks, as described below using Python. These tasks are part of the prototyping process and are tailored for unstructured data, such as text, images, or video.

Task No. 1 | Define Problem

Understand the fundamentals: Gain a deep understanding of the goals of the project and the input data, including data structure and data gathering methods.
Define the model’s prediction target and evaluation metric.

Task No. 2 | Analyze Data

Perform Exploratory Data Analysis (EDA) to understand the raw data, including unique values, missing data, and data distribution.
Create visualizations to analyze data, such as word clouds for text data or histograms for image data.

Task No. 3 | Data Preparation

Preprocess the data, such as normalizing image pixel values or tokenizing text.
Perform data augmentation techniques, such as flipping or rotating images, to increase the size of the training set.
Split the data into training, validation, and test sets.

Task No. 4 | Evaluate Candidate Models: Baseline

Establish baseline scores using simple models, such as logistic regression or k-nearest neighbors.
Use transfer learning or pre-trained models, such as ResNet or BERT, to leverage existing models for feature extraction.
Use appropriate metrics, such as F1 score or IoU, for evaluating the performance of models on unstructured data.

Task No. 5 | Model Development

Experiment with deep learning models, such as convolutional neural networks (CNNs) for image data or recurrent neural networks (RNNs) for text data.
Fine-tune pre-trained models or train models from scratch using transfer learning.
Optimize hyperparameters using techniques such as grid search or random search.

Task No. 6 | Model Evaluation and Interpretation

Evaluate the model on the test set and calculate appropriate metrics, such as precision, recall, and accuracy.
Analyze model performance and errors to gain insight into areas for improvement.
Use techniques such as Grad-CAM or attention mechanisms to interpret and visualize model predictions.

Note that there is overlap between these tasks and the tasks for structured data, but the subtasks and approaches are different due to the unique characteristics of unstructured data.