If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
The knowledge is the output of learning through the inseparable combination of theory and practice. It's what remains in one's experience from all the data which got shaped into what we call information. This process can be noticed throughout the different stages of our lives and it's never limited to the academic journey. What I'm aiming to express is that machine learning is nothing but a human logic tailored for more complex problems that surely require more computational capabilities. The last quote represents the nature knowledge acquiring process which, as you may notice, is similar to CRISP-DM Methodology which I detailed in a previous article and which is essential to succeed in your data mining project.
For the majority of newcomers, machine learning algorithms may seem too boring and complicated subject to be mastered. Well, to some extent, this is true. In most cases, you stumble upon a few-page description for each algorithm and yes, it's hard to find time and energy to deal with each and every detail. However, if you truly, madly, deeply want to be an ML-expert, you have to brush up your knowledge regarding it and there is no other way to be. But relax, today I will try to simplify this task and explain core principles of 10 most common algorithms in simple words (each includes a brief description, guides, and useful links).
Isn't it true that we are living in a digitalized world that has eliminated tons of human work by positioning automation?. In fact, it is the most defined period as Google's self-driving car has been invented. But, this period is not in its final stages instead is multiplying to create many more awesome things to surface in the near future. The most exciting concept that sits beside all these major transformations is Machine Learning, which is nothing but allowing computers to learn on their own to arrive at useful insights. Supervised learning is similar to a teacher teaching his students with examples and after sufficient practice, the teacher stops supervising and let the students derive at their own solution.
It is very crucial for the machine learning enthusiasts to know and understands the basic and important machine learning algorithms in order to keep themselves up with the current trend. In this article, we list down 10 basic algorithms which play very important roles in the machine learning era. Logistic regression, also known as the logit classifier is a popular mathematical modelling procedure used in the analysis of data. Regression Analysis is used to conduct when the dependent variable is binary i.e. 0 and 1. In Logistic Regression, logistic function is used to describe the mathematical form on which the logistic model is based.
It's hard to ignore the cultural and organizational impact that Artificial Intelligence (AI) has had over us. Most organizations today have realized the impact of AI, and are doing all that they can to participate in and help facilitate the growth of the technology. For those who know the nuances of AI and the metrics involved in it, Deep Learning and Machine Learning may not look like challenging terms. But, for those who are new to AI, these terms might be hard to understand. To understand the complications organizations face when adopting machine learning, we must first fully understand the difference between deep learning and machine learning.
In machine learning and statistics, classification is a supervised learning approach in which the computer program learns from the data input given to it and then uses this learning to classify new observation. This data set may simply be bi-class (like identifying whether the person is male or female or that the mail is spam or non-spam) or it may be multi-class too. Some examples of classification problems are: speech recognition, handwriting recognition, bio metric identification, document classification etc. It is a classification technique based on Bayes' Theorem with an assumption of independence among predictors. In simple terms, a Naive Bayes classifier assumes that the presence of a particular feature in a class is unrelated to the presence of any other feature.
The team of healthcare data scientists and doctors have developed and tested a system of computer-based'machine learning' algorithms to predict the risk of early death due to chronic disease in a large middle-aged population. They found this AI system was very accurate in its predictions and performed better than the current standard approach to prediction developed by human experts. The study is published by PLOS ONE in a special collections edition of "Machine Learning in Health and Biomedicine." The team used health data from just over half a million people aged between 40 and 69 recruited to the UK Biobank between 2006 and 2010 and followed up until 2016. Leading the work, Assistant Professor of Epidemiology and Data Science, Dr Stephen Weng, said: "Preventative healthcare is a growing priority in the fight against serious diseases so we have been working for a number of years to improve the accuracy of computerised health risk assessment in the general population. Most applications focus on a single disease area but predicting death due to several different disease outcomes is highly complex, especially given environmental and individual factors that may affect them. "We have taken a major step forward in this field by developing a unique and holistic approach to predicting a person's risk of premature death by machine-learning.
Random features provide a practical framework for large-scale kernel approximation and supervised learning. It has been shown that data-dependent sampling of random features using leverage scores can significantly reduce the number of features required to achieve optimal learning bounds. Leverage scores introduce an optimized distribution for features based on an infinite-dimensional integral operator (depending on input distribution), which is impractical to sample from. Focusing on empirical leverage scores in this paper, we establish an out-of-sample performance bound, revealing an interesting trade-off between the approximated kernel and the eigenvalue decay of another kernel in the domain of random features defined based on data distribution. Our experiments verify that the empirical algorithm consistently outperforms vanilla Monte Carlo sampling, and with a minor modification the method is even competitive to supervised data-dependent kernel learning, without using the output (label) information.
As a recent graduate of the Flatiron School's Data Science Bootcamp, I've been inundated with advice on how to ace technical interviews. A soft skill that keeps coming to the forefront is the ability to explain complex machine learning algorithms to a non-technical person. This series of posts is me sharing with the world how I would explain all the machine learning topics I come across on a regular basis...to my grandma. Some get a bit in-depth, others less so, but all I believe are useful to a non-Data Scientist. In the upcoming parts of this series, I'll be going over: To summarize, an algorithm is the mathematical life force behind a model.
" All about Artificial Intelligence / AI " by Arish Ali, CEO at Neurofy This video covers - Basics of Artificial Intelligence - Artificial intelligence in India - AI revolution across Industries - Careers in AI - Skills needed to make a career in AI Do check out our "PG Certificate Program in Artificial Intelligence & Deep Learning" course http://bit.ly/2F42DeK AI and Deep learning have shown promising growth in recent years and in the near future can change the way companies operate. After completing the Deep Learning and Artificial Intelligence online course, you'll be able to: - Use Tensorflow, Scikit Learn library, Keras and other machine learning and deep learning tools.