Data science courses
About Data science
Data scientists try to make sense of the data that’s all around us. Taking a data science course can help you make informed decisions, create beautiful visualizations, and even try to predict future events through Machine Learning. If you’re curious about what you can learn about the world using the data produced every day, then data science might be for you!
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- Practice projectMachine learning • AI • Data science
Pet Image Classification and Detection with Transformers
Use transformers to fine-tune a vision transfomer (ViT) for image classification and evaluate an object detection model (e.g., DETR) on the Oxford-IIIT Pet Dataset.More guidance, - Practice projectData science • Machine learning • AI • Python
Classifying Microscopic Histopathology Images with PyTorch
Train and evaluate a CNN to detect tumors in histopathological scans from the PatchCamelyon dataset.More guidance, 45 min - Practice projectData science • AI • Machine learning • Python
Explainable AI in Employee Attrition Prediction
Predicting employee attrition is challenging for HR departments, as it helps organizations retain valuable talent and reduce turnover costs. However, AI models used in such decisions must be transparent and interpretable, ensuring that HR professionals can trust and understand the predictions. In this project, we will use the IBM HR Analytics Employee Attrition Dataset to predict whether an employee will likely leave the company. We will train different machine learning models and apply Explainable AI (XAI) techniques to interpret their predictions. By the end of this project, we will have a fully interpretable pipeline that can help HR professionals make informed, fair, and data-driven decisions regarding employee retention.More guidance, - Practice projectAI • Machine learning • Data science
Explaining Breast Cancer Diagnosis Predictions with LIME
This project demonstrates how to use the LIME (Local Interpretable Model-agnostic Explanations) library to understand and explain predictions made by a machine learning model for breast cancer diagnosis. Using the Wisconsin Breast Cancer , we analyze how individual features, such as mean radius, texture, and smoothness, influence the model’s decision to classify a tumor as benign or malignant. By visualizing LIME values, we gain insights into the contribution of each feature to the model’s predictions, enabling better transparency and trust in AI-driven healthcare diagnostics.More guidance,
Related articles
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Standard Normal Distribution Explained with Real-World Examples
Learn about standard normal distribution, its properties, and how to calculate probabilities using z-tables, charts, and real-world examples. - Article
Differences Between Z-Test and T-Test
Learn the key differences between Z-Test and T-Test in hypothesis testing. Find out when to use each test based on sample size, variance, and assumptions. - Article
Chain of Thought Prompting Explained (with examples)
Learn the basics and implementation of chain of thought (CoT) prompting using LangChain. - Article
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