Data is the Genesis of every data science project. It all begins with data.Data is the basic unit for every ML and AI project. Wiki defines data as a set of values of qualitative or quantitative variables about one or more persons or objects.You have been tasked to get access to the datasets found in this databox. Your goal is to collect as much dataset as possible. You need to aggregate them into a usable format for future data science teams.Flags may be found in various locations and datasets. You are to combine these flags into a dataset.Happy Hacking, Happy Data Science!!

Are you able to extract useful insights when given data? As a wannabe data scientist you were given access to this databox. It has several questions you need to find answers to. Via EDA, explore the dataset and find answers and insights.

Machine learning (ML) models may be deemed confidential due to their sensitive training data, commercial value, or use in several essential applications. Given an ML model,extract essential information from the serialized model provided. Given access to a model can you extract the target labels.What is the constituents of the model and what is the frequency count for the expected flag ciphersuites***384.

The end of every ML & Data Science project includes productionized Web App.Although the models may be working properly,the web app may not be secure. You have been tasked to find out the vulnerabilities in the ML Web App.

Hidden in plainsight is a favorite slogan.In order to secure his identity Agent Romonov uses a photo sharing site as a means to communicate with HQ.You have been given a recent photo she shared. Identify the flag

Building a model is one thing, deploying it is also another. In order to produce the right predictions, you need to supply data similar to the one used in training when performing model inference. Ensure that this is the case when given a productized model.