educative.blog
For developers, by developers
Trending
blog cover

Comparison of Evaluation Metrics used in Machine Learning Models

Understanding Evaluation Metrics such as accuracy, precision, recall, etc. that are used to evaluate machine learning models.
Khawaja Muhammad Fahd
Apr 10 · 2025
blog cover

How to land a data analyst role at Google as a fresher

Landing a data analyst role at Google as a fresher is challenging but far from impossible. This blog breaks down what Google looks for, how to prepare effectively, and where freshers often face roadblocks so you can avoid them.
Zarish Khalid
Apr 10 · 2025
blog cover

How Does Reinforcement Learning Work

Reinforcement Learning (RL) is a type of machine learning in which an agent learns by interacting with an environment and receiving feedback in the form of rewards or penalties. In this article, we explain how RL works, using the example of the CartPole problem, where the agent learns to balance a pole. We also highlight real-world applications of RL to show its practical use in solving complex problems.
Hamna Waseem
Feb 10 · 2025
blog cover

How to use convolutional neural networks (CNNs) for images

Convolutional Neural Networks (CNNs) power groundbreaking innovations like facial recognition, self-driving cars, and medical imaging. This blog breaks down how CNNs work, exploring their core layers—convolutional layers, pooling layers, and fully connected layers— and explaining their training process with backpropagation, making the concepts accessible even to machine learning beginners. You’ll also explore a hands-on example of building a simple CNN with TensorFlow and Keras.
Hamna Waseem
Jan 1 · 2025
blog cover

A guide to anomaly detection in health care with machine learning

Explore the role of machine learning in revolutionizing healthcare by detecting anomalies in vital signs, sensor data, and medical imaging. This guide covers supervised, unsupervised, and semi-supervised techniques, tailored for structured, unstructured, real-time, and imbalanced datasets. With hands-on examples, learn to build models for detecting patient falls or heart arrhythmias using tools like scikit-learn, TensorFlow, and Keras, enabling timely life-saving interventions.
Hamna Waseem
Dec 10 · 2024
blog cover

Essential Data science skills for new grads and early-career devs

Finding valuable insights from massive datasets is a critical skill in today's competitive job market. Key competencies include Python programming, basic statistics, data analysis tools, data visualization, data cleaning, data wrangling, and machine learning concepts. Learning data science skills will significantly boost your career, opening opportunities for advanced problem-solving, data-driven decision-making, and competitive roles across various industries.
Nimra Zaheer
Aug 29 · 2024
blog cover

NumPy vs. pandas: What’s the difference?

We dive into the differences between NumPy and pandas, two pivotal libraries in Python’s data science toolkit.
Saif Ali
Mar 7 · 2025
blog cover

Text summarization with Hugging Face transformers: Part 3

This blog in the text summarization series using Hugging Face transformers focuses on model evaluation for abstractive summarization. It explains the setup for generating outputs and evaluating them against reference summaries using metrics like ROUGE and BERT/BART-Score. The process involves configuring data loaders, setting the model to evaluation mode, generating predictions, and computing scores. It also suggests best practices for research and experiments, including using multiple runs for reliable results, optimizing hyperparameters, and considering human evaluation to validate model performance.
Mehwish Fatima
May 10 · 2024
blog cover

How to solve cold start problems with synthetic data generation

Let's learn about the utilization of synthetic data to address cold start problems in training models for deduplication. It highlights issues businesses face due to unresolved, duplicative records affecting various functions such as purchases, manufacturing, sales, marketing, and legal compliance. Using a dataset provided by the DuDe team, it elaborates on training a CatBoost classification model to identify duplicates in restaurant records by leveraging pre-computed similarity features and augmented data. The approach includes generating synthetic duplicates with slight variations using nlpaug, improving the robustness of the training set against real-world data discrepancies. The blog concludes with the evaluation of model performance on synthetic versus actual data, stressing the need for more sophisticated data handling and model training techniques to effectively manage duplicate records and enhance data integrity.
Paul Kinsvater
May 9 · 2024