Arthur Samuel defined Machine Learning in the 90s as "a field of study that gives the ability to the computer to self-learn without being explicitly programmed" which means permeating knowledge to machines without explicitly coding it.
Machine Learning concerns itself with the development of such algorithms or computer programs that can teach themselves to grow and transform when exposed to the new data. Machine Learning studies the algorithms that could make the machine self learn to perform certain tasks. For example, it can make accurate predictions or behave intelligently by self-learning from the historical data.
You might be wondering, “What exactly is an algorithm and what does it do?” So, an algorithm is basically a well-defined set of instructions or computer-implementable instructions used to perform computations.
Let us look at different types of machine learning algorithms that are widely used in different industries and also serve as the foundation stone on which incredible ML algorithms can be created:
In linear regression, the predictions of scores for one variable are determined by the ratings on the second variable. The variable that is being predicted is called the criterion variable and is referred to as Y. The variable on which these predictions are based is called predictor variable and is known as X.
Mathematically, Linear Regression can be represented by a linear equation that combines the input data (X) to predict the output value (Y). Further, the linear equation assigns factor to each of the input variables and is represented by a greek letter β (Beta). Linear Regression Algorithm can be used to predict the general trend of a stock over a period of time.
Bracketed under the supervised learning type of machine learning, Logistic regression measures the relationship between dependent variables which is categorical with one or more independent variables by estimating the probabilities using the sigmoid function or logistic function. In the Logistic regression, we are aiming at finding a discrete value, either 1 or 0 which can help in finding a definite answer in our scenario.
It computes the weighted sum of input variables through a special non-linear function called the sigmoid function to generate the output value. Logistic regression can be used to predict the direction of the market.
KNN ( K Nearest Neighbour) is one of the most significant and straightforward machine learning algorithms for beginners. It is also one of the most widely used machine learning algorithms. The purpose of this algorithm is to separate the data into different categories/classes so that they can be classified on the basis of their similarity features.
KNN is a self-learning algorithm and learns as it progresses in the sense that it does not need an explicit training phase and starts to classify the data points decided by a majority vote of its neighbors. The object is assigned to the class which is most common among the K nearest neighbors. Furthermore, KNN is also a lazy machine learning algorithm in the sense that it does not use any training data points to do any generalization.
A few applications of the KNN Classification ML algorithm are the determination of credit ratings, handwriting recognition, image recognition, etc.
Support Vector Machine OR SVM is a type of a classifier wherein the discriminative classifier is defined by a separating hyperplane. Initially, the Support Vector Machine was used for data analysis. A set of training examples belonging to different categories is fed into the Support vector machine algorithm which then builds a model that starts assigning new data to one of the categories that it has learned in the training phase.
In this algorithm, a hyperplane is created serving as a demarcation between different categories. Hence, when SVM processes new data, depending on the side of the hyperplane it appears, it is classified into one of the classes.
Based on Bayes Theorem, Naive Bayes is a type of a classification technique with an assumption of independence among the predictors. Simply put, a Naive Bayes classifier assumes that the presence of a particular feature of a class is not related to the presence of any other function in that class.
For eg, to determine whether or not you will be late to the office, one needs to know how far your home is or how much traffic you faced on the road.
On the contrary, the Naive Bayes classifier algorithm assumes that all events are independent of each other thereby simplifying the calculations to a great extent. Hence, it can be put to use while finding simple relationships across different parameters in incomplete data.
These are some of the machine learning algorithms for beginners to try their hands on! For more such articles, keep coming back to this space.
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