ML for Software Industry

Machines unintentionally make mistakes and learns from them. Here we have discussed the step-wise learning process of machines with the help of simplistic examples.

Every Machine Learning project needs to go through these seven steps: Problem finalisation, data gathering, data pre-processing, model selection, data splitting, performance evaluation, model deployment. In this article, we will discuss all of them in detail.

Machine learning is the science of getting computers to act without being explicitly programmed. Here computer takes Input Data and Output Data as its input parameters and tries to produce the best suitable function that maps Inputs to Outputs. The machine learns a mapping function that maps the input data to the output using existing experiences.

Pandas is a famous library package of Python used by Data scientists and analysts for data understanding, data preprocessing, and much more. we will cover installation and all the basic Pandas functions frequently used while building machine learning projects.

In this blog, we have discussed techniques of conditions and branching using which the instructions of our program get executed.

In this article, we want to identify the compounds with the best effect on Acetylcholinesterase protein using XGBoost Machine Learning Algorithm. We will be using the famous ‘Chembl’ database to demonstrate the complete use-case.