Latest

"Structured data classification with deep learning offers groundbreaking potential for heart disease prediction. By harnessing the capabilities of neural networks, this approach can process medical data efficiently and deliver precise diagnoses, leading to improved patient care and better health outcomes." This series of five blogs will guide you

Introduction After data collection and data preprocessing, we have to build the model, train it, validate it and test it. In this article, we will delve into Model creation, its architecture, training, validation and testing.  The input all_features = layers.concatenate(    [         sex_encoded,         cp_encoded,         fbs_encoded,         restecg_encoded,         exang_

Introduction Model deployment is crucial as it bridges the gap between theoretical models and real-world applications. It enables practical utilisation of machine learning and AI solutions, delivering valuable outcomes to businesses and users. Efficient deployment ensures scalability, reliability, and accessibility, maximising the impact of AI technology in various domains. We

Introduction MLflow is an open-source platform designed to manage the machine learning models. It enables data scientists and engineers to track experiments. With components like tracking, projects, and models, MLflow allows users to organise and reproduce experiments efficiently. It supports various machine learning frameworks and cloud platforms, promoting collaboration and

Evaluation Metrics are quantitative measures that are used to assess the performance of a statistical or machine-learning model. These metrics provide insights as to how well the model is performing and help in comparing different models and algorithms.  There are two types of models, regression model (continuous output) and classification

In the previous article, we explored the different data preprocessing steps using the Auto MPG dataset. In this article, we will be looking at exploratory data analysis and the different methods. Exploratory Data Analysis is used to analyze and investigate datasets and summarize their main characteristics, often employing data visualization

Real-world data is often incomplete: lacking attribute values, lacking certain attributes of interest, or containing only aggregate data, Noisy: containing errors or outliers. Inconsistent: containing discrepancies in codes or names. Data preprocessing is a technique that converts raw data into understandable data. It transforms the data into a format that