An Introduction to Machine Learning and AI
An Introduction to Machine Learning
Machine learning is a field of Artificial Intelligence, art of machine learning is to reduce a range of real time problems to a set of narrow prototypes. Much of the science of machine learning is trying to solve those problems and are providing good solutions.
Machine learning lies at the intersection of computer science, engineering, and statistics. Basically machine learning uses statistics. Nowadays world has moved from manual labor to knowledge work also things are much more ambiguous, to maximize profits, minimize risk, and find the best marketing strategy all are done using machine learning. The development of self-learning algorithms to gain knowledge from that data in order to make predictions is Machine Learning.
Machine Learning algorithms can be categorized as supervised, semi-supervised or unsupervised. Supervised algorithms require humans to provide feedback about the accuracy of predictions along with input and desired output. Unsupervised algorithms do not need any training or human involvement, they have unlabeled data for prediction. They use an iterative approach called deep learning to review data and making conclusions.
Many Machine Learning algorithms are built there to apply complex mathematical calculations automatically such as k-means clustering, Ada boosting, KNN (k-nearest neighbors), linear regression etc. Many more applications include Fraud detection, web search result, sentiment analysis, image and pattern recognition.
What is Artificial Intelligence?
It is the science of making computers and machines intelligent that works like human and do tasks such as speech recognition, decision-making, reasoning, learning, problem solving and translation between languages. In simple words, it is developed to make human life easy in different aspects and perform enormous works with more accuracy. It has become an intelligent part of todays industry and is so crucial as data is growing day by day. However, machines understand only logic while we humans understand logic and apply common sense as well. But machines don’t possess common sense.
Humans tend to learn from their past experiences and they apply these experiences in future to get better results. But machines can’t do so, because they don’t learn from the external environment. Bridging this gap, leads to Artificial Intelligence. It made machines to perform tasks such as identifying patterns in data more effectively than humans thus making business more profitable. Knowledge Engineering and Machine learning is core of AI. Logics are used to provide insights into the reasoning problem without direct implementation. Direct implementations of ideas from logic and model construction techniques are used in AI. There is no need to program machines explicitly.
These days self-improvement of machines is becoming advantages as it allow digital minds to modify themselves. Algorithms can be improved to give a larger boost than hardware improvements also new modules can be designed for new kinds of domains. Thus AI plans can overtake human experts. Artificial Intelligence will continue to play an important role in the various fields such as medical, military, financial field. This technology and its applications will likely have far reaching effects on human life in the years to come.
An Overview to Natural Language Processing?
Natural Language Processing is a field of computer science, used to interface between human and machines. It analyses and synthesizes natural language and speech. That is it deals with analyzing, understanding and generating the languages that humans use naturally in order to interface with computers in both written and spoken contexts using natural human languages instead of computer languages like symbols. It is required where we want intelligent systems to work like robots and another expert systems.
NLP algorithms are typically based on machine learning algorithms. Instead of hand-coding large sets of rules, NLP depends on machine learning to automatically learn these rules by analyzing a set of examples and making a statical inference. In general, the more data analyzed, the more accurate the model will be.
It involves identifying and analyzing the structure of words, words in the sentence for grammar showing relationship among the words, draws exact meaning or the dictionary meaning from the text and also brings about the meaning of immediately succeeding sentence and deriving those aspects of language which require real world knowledge. With knowledge of Python one can easily get into Natural Language processing, mainly the library used for it is NLTK (Natural Language Tool kit). It is a suite of libraries and programs for symbolic and statistical NLP written in Python language. Machine translation, Automatic summarization, Sentiment analysis, Text classification and Conversational agents are some applications of Machine Learning.
An Introduction about Deep learning?
Deep learning is a subfield of Machine Learning inspired by human brain. It is based on machine learning algorithms having multiple layers for feature extraction and transformation, each successive layer uses output from previous layer as an input. Deep learning includes learning of deep structured and unstructured representation of data and allow to build a solution optimized from algorithm to solve machine learning problems. It is fastest-growing field in machine learning using deep neural networks to abstract data such as images, sound, and text. Thus deep learning has become a growing trend in machine learning. To abstract better results when data is large and complex. It makes our learning algorithms easy to use and understand.
As computer learns from each layer, and then uses that learning in the next layer to learn more, till the learning reaches its last stage through cumulative learning in multiple layers. It was the only method of feature extraction present in traditional Machine Learning. Thus, in Deep Learning the computer is not limited to fed or supervised logic; rather, the computer is trained to learn from each stage or layer of learning to use in the next.
Deep learning has enabled many practical applications of Machine Learning. Deep Learning breaks down tasks in ways that makes all kinds of machine assists seem possible, image recognition. Speech recognition, driver-less car, health-care, recommendation system, are all applications of deep learning. One of the most widely used types of deep network is deep convolutional networks.