Decoding AI Vs Machine Learning Vs Deep Learning

Posted by OodlesAI on December 20th, 2019

Artificial intelligence is infusing cognitive abilities into machines to analyse their environment in a human-like manner. Machine learning development services are an integral part of the dynamic artificial intelligence technologies. Machine learning is used in various AI solutions such as the development of image classification models and chatbot development services. Machine Learning focuses on the identification of a pattern in a set of data. It either classifies the data or organizes it. To do this, machine learning has several classification and regression algorithms. 

Different kind of learning in Machine Learning:-

1. Supervised
2. Unsupervised
3. Semi-Supervised
4. Reinforcement  

Before diving into types of machine learning, here are some important keywords:-

• Feature – A single detectable and measurable property that can be used to distinguish data. 
• Over fitting model – A machine learning model that is trained too well with data and hence is only useful to make predictions for that data set. 
• Under fitting model – A machine learning model that is unable to learn from it’s training set.

In Supervised Learning, the system is fed with a labeled dataset (also called training dataset). The system trains using this data set. After training the system, every feature has some weight-age. Using these weights predictions are made. To make sure that the system is not over fitting we use the validation set. Finally, the testing dataset is used to test the system’s prediction accuracy. 

Unsupervised learning is where the system is not provided with any training set. Without labels, the system is able to find all the patterns in the data. 

Semi-Supervised Learning is a hybrid of Supervised and Unsupervised Learning.

Deep Learning is a subset of Machine Learning. In Machine Learning, we need to perform feature extraction and provide features to the system. With Deep Learning we just need to provide the data and the system does the rest. It extracts features and learns using Neural Networks with multiple hidden layers. The output of each layer serves as the input for the next layer. This Artificial Neural Network is similar to the biological neural network in animals. Using techniques like Back Propagation it trains the neural network.

In the world of Big Data, Machine Learning becomes ineffective. But Deep Learning does the trick. With its artificial neural networks, it is able to find patterns even in Big Data. Machine Learning is effective only in a relatively small set of data. We deploy machine learning algorithms for comparatively smaller datasets whereas deep learning is used to analyze large data sets.

 Summary

Artificial Intelligence is a very vast study, and Machine Learning and Deep Learning are just a part of Artificial Intelligence. Artificial Intelligence can learn itself using Machine Learning and Deep Learning.