What is Machine Learning and Why is it important

What is Machine Learning?

Machine Learning is a computer science field that enables the computer or any other machine to learn without explicit programming. The main focus of machine learning is to provide algorithms that can be trained to accomplish a task. It is subset of artificial intelligence. Machine learning algorithms create a mathematical model based on sample data, known as "training data," so that predictions or decisions can be made without explicit programming for the task.

Machine learning is closely related to the statistical as well as mathematical interpretation and optimization of data.It includes various techniques and methods like Supervised Learning, Unsupervised Learning, Semi-supervised Learning and Reinforcement Learning, each of which has their separate algorithms and importance.

Why is Machine Learning important?

Several studies and research performed in recent years have shown that Machine Learning can be used to automate many different tasks that only humans can do like Image Recognition, Text Generation, understanding and responding to human voice or playing games.

Data is every business' lifeblood. Data-driven decisions increasingly make the difference between competing or falling further behind. Machine learning can be the key to unlocking the value of corporate and customer data and taking decisions that keep a company ahead of the competition.

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Understanding KNN(K-nearest neighbor) with example

Understanding KNN(K-nearest neighbor) with example. It is probably, one of the simplest but strong supervised learning algorithms used for classification as well regression purposes. It is most commonly used to classify the data points that are separated into several classes, in order to make prediction for new sample data points. It is a non-parametric and lazy learning algorithm. It classifies the data points based on the similarity measure (e.g. distance measures, mostly Euclidean distance). Assumption of KNN : K- NN algorithm is based on the principle that, “the similar things exist closer to each other or Like things are near to each other.” In this algorithm ‘K’ refers to the number of neighbors to consider for classification. It should be odd value. The value of ‘K’ must be selected carefully otherwise it may cause defects in our model. If the value of ‘K’ is small then it causes Low Bias, High variance i.e. over fitting of model. In the same way if ‘K’ is very large then it l

What are various Data Pre-Processing techniques? What is the importance of data pre-processing?

What is Data Pre-Processing? What is the importance of data pre-processing? The real-world data are susceptible to high noise, contains missing values and a lot of vague information, and is of large size. These factors cause degradation of quality of data. And if the data is of low quality, then the result obtained after the mining or modeling of data is also of low quality. So, before mining or modeling the data, it must be passed through the series of quality upgrading techniques called data pre-processing. Thus, data pre-processing can be defined as the process of applying various techniques over the raw data (or low quality data) in order to make it suitable for processing purposes (i.e. mining or modeling). What are the various Data Pre-Processing Techniques? Fig: Methods of Data Pre-Processing source: Fotolia Once we know what data pre-processing actually does, the question might arise how is data processing done? Or how it all happens? The answer is obvious; there are series o

Supervised Machine Learning

Supervised Machine Learning What Is Supervised Learning? It is the machine learning algorithm that learns from labeled data. After the data is analyzed and learned, the algorithm determines which label should be given to new data supplied by the user based on pattern and associating the patterns to the unlabeled new data. Supervised Learning algorithm has two categories i.e Classification & Regression Classification predicts the class or category in which the data belongs to. e.g.: Spam filtering and detection, Churn Prediction, Sentiment Analysis, image classification. Regression predicts a numerical value based on previously observed data. e.g.: House Price Prediction, Stock Price Prediction. Classification Classification is one of the widely and mostly used techniques for determining class the dependent belongs to base on the one or more independent variables. For simple understanding, what classification algorithm does is it simply makes a decision boundary between data points

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