Understand the concept of a model in machine learning through a classroom discussion.

Discover my objective and preparation strategy for clearing the AWS Certified Machine Learning Specialty Exam. Learn about the structure of the exam, its domains, and the importance of real-time experience in the machine learning field. Find out the online resources and courses I referred to during my preparation, including AWS resources, Udemy courses, SageMaker documentation, and additional articles. Explore the practice exams I took and how they helped me identify my weak areas. Gain insights into the tips and techniques I used during the real exam to pass with a high score. Get advice on preparing notes and staying organized throughout the learning process

In this post, the author explores the impact of COVID-19 on different countries using data visualization techniques. The focus is on countries like China, South Korea, Italy, the USA, and India. The analysis highlights trends in confirmed cases, active cases, recoveries, and deaths, shedding light on the effectiveness of measures taken by each country. The post emphasizes the importance of early action and proper containment strategies in controlling the spread of the virus

Regression testing plays a crucial role in software development, but retesting the entire program after making changes or adding new features is often impractical. To address this, a subset of test cases is executed for regression testing. In this blog post, we explore a proof of concept (POC) that uses a Classification Learning model to assist in the selection of manual regression test cases. By considering metadata and natural language descriptions of test cases, the model predicts which test cases should be selected. The post also covers data collection and preparation, as well as exploratory data analysis to understand the relationship between various features and test case selection

In this blog post, the author provides a beginner’s guide to exploratory data analysis (EDA) and feature engineering using the Titanic disaster dataset. They explain the importance of EDA in understanding data and its impact on data modeling and predictions. The post includes code snippets and visualizations to analyze various features such as age, gender, passenger class, fare, and embarked port. The author also discusses correlations between different variables and demonstrates the process of feature engineering by creating a new variable called Family Size. The post concludes by emphasizing the significance of EDA in gaining insights and improving data modeling