As Machine Learning and Artificial Intelligence (AI) are making their ways in the market there will be a time surely when there will not arise any requirement of developers and IT professionals. The technical bugs will get fixed by the machines themselves. Lot of manual work will be automated.
Artificial neural networks (ANNs), usually called neural networks (NNs), are computing systems vaguely inspired by the biological neural networks that constitute animal brains. There is lot of hype these days regarding the Artificial Intelligence and its technologies.
In this article, we will talk about the Hype vs Reality on AI technologies and also will explain about the various terminology associated with Artificial Intelligence (AI), Transitioning from Machine Learning to Deep Learning, Basic building blocks for the study of AI and Artificial Neural Network (ANN).
In this post, we will develop a classification model where we’ll try to classify the movie reviews on positive and negative classes. I have used different machine learning algorithm to train the model and compared the accuracy of those models at the end. you can keep this post as a template to use various machine learning algorithms in python for text classification.
At the end we will validate the model by passing a random review to the trained model and understand the output class predicted by the model. You will learn how to create and use the pipeline for numerical feature extraction and model training together as a one function.
Word vectors – also called word embeddings – are mathematical descriptions of individual words such that words that appear frequently together in the language will have similar values. In this way we can mathematically derive context. As mentioned above, the word vector for “lion” will be closer in value to “cat” than to “dandelion”.
Machine Learning algorithms don’t understand the textual data rather it understand only numerical data. So the problem is how to convert the textual data to the numerical features and further pass these numerical features to the machine learning algorithms.
As we all know that the raw text stored in some dump repository contains a lot of meaningful information. And in today’s fast changing world, it becomes essential to consider data driven decision than fully rely on experience driven decision.
Sentence Segmentation or Sentence Tokenization is the process of identifying different sentences among group of words. Spacy library designed for Natural Language Processing, perform the sentence segmentation with much higher accuracy. Spacy provides different models for different languages. In this post we’ll learn how sentence segmentation works, and how to set user defined segmentation rules.