Machine learning (ML) is a subset of Artificial intelligence(AI) , it's the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions manually, instead it relies on patterns and inference. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to perform the task.
Course will cover Introduction on Machine Learning , deep dive technology behind it , applied use case & available job prospects.
Topics - covered in the course
Machine learning tasks are classified into several broad categories- In supervised learning world, the algorithm builds a mathematical model from a set of data that contains both the inputs and the desired outputs. Semi-supervised learning algorithms develop mathematical models from incomplete training data, where a portion of the sample input doesn't have labels. In unsupervised learning, the algorithm builds a mathematical model from a set of data which contains only inputs and no desired output labels.Active learning algorithms access the desired outputs (training labels) for a limited set of inputs based on a budget, and optimize the choice of inputs.
Performing machine learning involves creating a model, which is trained on some training data and then can process additional data to make predictions. Various types of models have been used and researched for machine learning systems.
Artificial neural networks- An artificial neural network is an interconnected group of nodes, akin to the vast network of neurons in a brain.
Deep learning consists of multiple hidden layers in an artificial neural network. This approach tries to model the way the human brain processes light and sound into vision and hearing. Brief on Decision tree learning - we will use a decision tree as a predictive model to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modeling approaches used in statistics, data mining and machine learning.
Some challenges in adopting ML are Inaccessible Data and Sensitive Data Security, Infrastructure Requirements for Testing and Experimentation , Inflexible Business Models & Affordability of Organisations