WHY GATISTAVAM?
  • Multiple domain expertise
  • Highly experienced team
  • Daily, weekly reporting structure
  • Full time project manager at no cost
  • Good infrastructure to support the staff
  • Best Product Development model
GATISTAVAM OFFICE LOGIN
Enter Your User Name!
Enter Your Password!

It is process of designing, building, and implementing machine learning models.

Gatistavam Systems can help you to :

Machine Learning is a subset of Artificial Intelligence that focuses on the development of algorithms that can learn from and make predictions on data.

There are three main types of machine learning: Supervised Learning, Unsupervised Learning, and Reinforcement Learning.

Supervised learning involves training a model on labeled data and using the model to predict the output for new, unseen data.

Unsupervised learning involves finding patterns or relationships in a dataset without having any prior labels.

Reinforcement learning involves an agent learning how to interact with an environment in order to maximize a reward signal.

Common evaluation metrics for machine learning models include accuracy, precision, recall, and F1 score.

Overfitting occurs when a model is too complex and has learned the noise in the training data rather than the underlying pattern.

Regularization is a technique used to prevent overfitting by adding a penalty term to the loss function during training.

Feature engineering involves transforming raw data into a format that is more suitable for feeding into a machine learning model.

Bias in machine learning refers to errors in the model that systematically favor or disfavor certain outcomes or groups.