Machine Learning

Machine learning is a form of AI that enables a system to learn from data rather than through explicit programming. However, machine learning is not a simple process. As the algorithms ingest training data, it is then possible to produce more precise models based on that data.

A machine-learning model is the output generated when you train your machine-learning algorithm with data. After training, when you provide a model with an input, you will be given an output.

For example, a predictive algorithm will create a predictive model. Then, when you provide the predictive model with data, you will receive a prediction based on the data that trained the model.

Machine-learning techniques are required to improve the accuracy of predictive models. Depending on the nature of the business problem being addressed, there are different approaches based on the type and volume of the data. In this section, we discuss the categories of machine learning.

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Deep learning

Machine learning offers potential value to companies trying to leverage big data and helps them better understand subtle changes in behaviour, preferences or customer satisfaction. Business leaders are beginning to appreciate that many things happening within their organizations and industries can’t be understood through a query. It isn’t the questions that you know; it’s the hidden patterns and anomalies buried in the data that can help.

Prerequisites:

• At least degree level qualification in related field.
• Having 3-5 years experience in similar domain would be advantageous, but not essential.

Highlights:

• Based on Hands-on exercises
• Comprehensive theoretical and practical understanding
• Support for future training progression
• Training and certification materia.
• Real time project demo & understanding
• Interview preparation
• Placement assistance
• Certification based training