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What is Machine Learning?
Machine learning is a branch of artificial intelligence (AI) focused on building applications that learn from data and improve their accuracy over time without being programmed to do so.
Types Of Machine Learning
There are also some types of machine learning algorithms that are listed below:
Recommender systems are an important class of machine learning algorithms that offer "relevant" suggestions to users. Categorized as either collaborative filtering or a content-based system, check out how these approaches work along with implementations to follow from example code
A classifier is any algorithm that sorts data into labeled classes, or categories of information. A simple practical example are spam filters that scan incoming “raw” emails and classify them as either “spam” or “not-spam.” Classifiers are a concrete implementation of pattern recognition in many forms of machine learning.
Facial recognition is the process of identifying or verifying the identity of a person using their face. It captures, analyzes, and compares patterns based on the person's facial details.
The face detection process is an essential step as it detects and locates human faces in images and videos.
The face capture process transforms analogue information (a face) into a set of digital information (data) based on the person's facial features.
The face match process verifies if two faces belong to the same person
TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library and is also used for machine learning applications such as neural networks.
Keras is High-Level Deep learning Python library extensively used by Data-scientists when it comes to architect the neural networks for complex problems. Higher level API means that Keras can act as front end while you can ask Tensor-flow or Theano to work as back end
LSTM stands for Short Term Long Term Memory. It is a model or an architecture that extends the memory of recurrent neural networks. Typically, recurrent neural networks have “short-term memory” in that they use persistent past information for use in the current neural network. Essentially, the previous information is used in the current task. This means that we do not have a list of all of the previous information available for the neural node.