Data Collection and Preparation is the first step in the ML Development process, where data is gathered, cleaned, and preprocessed.
We are a team of experienced TPs-Polyglots who are passionate about creating innovative and cutting-edge ML solutions. In this site, we will guide you through the world of ML and provide you with useful insights on how to build robust and efficient ML models.
Machine Learning is an AI technology that allows machines to learn from data and improve their performance without being explicitly programmed.
Unsupervised Learning is a type of ML where the model is trained on unlabeled data, and the goal is to find patterns or structures within the data.
Data Collection and Preparation is the first step in the ML Development process, where data is gathered, cleaned, and preprocessed.
Model Building and Training is the second step in the ML Development process, where ML models are designed and trained using the preprocessed data.
Model Evaluation and Validation is the third step in the ML Development process, where the performance of the trained model is assessed on new, unseen data.
Linear Regression is a simple and widely used ML algorithm that models the relationship between a dependent variable and one or more independent variables.
This algorithm is commonly used for tasks such as predicting house prices, stock prices, and sales forecasts.
Logistic Regression is an ML algorithm used to model the probability of a binary or categorical outcome based on one or more independent variables.
This algorithm is commonly used for tasks such as customer churn prediction, fraud detection, and credit scoring.
Decision Trees are a popular ML algorithm used for both classification and regression tasks, where the model learns a series of if-then rules to make predictions.