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) …
So what were the answers popping in your head? Random forest, SVM, K means, Knn or even Deep Learning and its variants? Now some of you might laugh and say how on earth can you predict so far ahead, predicting things 100 yrs into future is crazy. Well the answer is Lindy effect. Yes, the heuristic I am using to predict this is Lindy Effect.
Sometimes its ok and good for everyone to un-develop something existing to uncover the hidden gems which are already there and are useful. May be its like Un-Develop to Innovate? Alan Turing published "Turing Test" that speculates the possibility of creating machines that think. In order to pass the test, a computer must be able to carry on a conversation that was indistinctive from a conversation with a human being. AI apart from its traditional definition also includes things like planning, understanding language, recognizing objects and sounds, learning, and problem solving.
In this course you will get a complete understanding of Machine Learning concepts. The industry standard best practices for formulating, applying and maintaining data driven products. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Next we take up data wrangling and EDA with Pandas. We step into Machine Learning algorithms linear and logistic regression and build real world solutions with them.
In this guide, we'll take a practical, concise tour through modern machine learning algorithms. While other such lists exist, they don't really explain the practical tradeoffs of each algorithm, which we hope to do here. We'll discuss the advantages and disadvantages of each algorithm based on our experience. Categorizing machine learning algorithms is tricky, and there are several reasonable approaches; they can be grouped into generative/discriminative, parametric/non-parametric, supervised/unsupervised, and so on. However, from our experience, this isn't always the most practical way to group algorithms.
However, both are equally important concepts of data science. Having said that, there are several dissimilarities between the two concepts also. In case of regression, as we all know the predicted outcome is a numeric variable and that too continuous. For a classification task, the predicted outcome is not numeric at all and represents categorical classes or factors i.e. the outcome variable in such a task has to be assuming limited number of values which may be binary in nature (dichotomous) or multinomial (having more than 2 classes). We in our analysis are motivated to work only on the'classification' scheme of tasks from a predictive analysis domain keeping our focus not on regression trees but only on classification trees, as the name suggests'Classification and Regression Trees'.
Machine Learning(Revisited) is a Red hot cake in the intellectual & scientific world right now. Where the human intelligence is collaborating with this powerful machines to create some wonderful solutions for the problems which are futuristic and realistic too. The magical prediction this stream of AI can do is really mind-boggling . He gave the most simplified definition of Machine Learning and said " It's a field of study that gives computers the ability to learn without being explicitly programmed." There is one very famous theory called Bait Shyness where Rodents learn to avoid the foods which they feel will harm them.
Once we start delving into the concepts behind Artificial Intelligence (AI) and Machine Learning (ML), we come across copious amounts of jargon related to this field of study. Understanding this jargon and how it can have an impact on the study related to ML goes a long way in comprehending the study that has been conducted by researchers and data scientists to get AI to the state it now is. In this article, I will be providing you with a comprehensive definition of supervised, unsupervised and reinforcement learning in the broader field of Machine Learning. You must have encountered these terms while hovering over articles pertaining to the progress made in AI and the role played by ML in propelling this success forward. Understanding these concepts is a given fact, and should not be compromised at any cost.
Data science, also known as data-driven decision, is an interdisciplinery field about scientific methods, process and systems to extract knowledge from data in various forms, and take descision based on this knowledge. A data scientist should not only be evaluated only on his/her knowledge on mahine learning, but he/she should also have good expertise on statistics. I will try to start from very basics of data science and then slowly move to expert level. Supervised machine learning requires training labeled data. Unsupervised machine learning doesn't required labeled data.
Why Big Data is important • Data contains information. "Field of study that gives computers the ability to learn without being explicitly programmed." Starting with a different attribute • Seems like a much better starting point than address • Each node almost completely uniform • Almost completely predicts whether we will be paid back yes no a, -c, i, e, o, u: Y -a, c, -i, e, -o, -u: N a, -c, i, -e, -o, -u: Y -a, -c, i, e, -o, -u: Y -a, c, i, -e, -o, -u: N -a, -c, i, -e, -o, u: Y a, -c, -i, -e, o, -u: N a, c, i, -e, o, -u: N criminal? Support Vector Machine • Binary classification algorithm • SVM generates a (N -- 1) dimensional hyperlane to separate those points into 2 groups. Singular Value Decomposition • PCA is actually a simple application of SVD 25.
The world of AI has a lot of things around it to thank for its existence in our technological landscape of today. Not only have humans spent decades of research perfecting the mathematical calculations to make these wonderfully complex learning algorithms work but during this time we have looked further than our own species as inspiration to make the next generation of intelligent presence on our planet. Mother Nature, and all that it encompasses, has it's roots firmly planted in the workings of Artificial Intelligence -- and it's here to stay. Aren't Sir David Attenborough's wildlife documentaries just incredible? They go into incredible, high definition detail about the behaviours and properties of the Earth's many inhabitants, and they allow us to understand how they fit into the natural ecosystem and work together in order to allow our planet to flourish -- to make it Earth.