Machine Learning is the hype of today’s IT. Nearly every professional secretly covets a career in this domain. As there is no dearth of opportunities in this field, the popularity of ML will continue to remain intact in the foreseeable future. Anyone attempting to land a lucrative career in this domain should become firstly familiar with Machine Learning algorithms. It is also important to remember that a given algorithm does not work best for every task. There are many factors that are at play here including the structure and size of the datasets the user uses. As a result, the user should try different algorithms for the problem to find out the most appropriate one.
This blog will list out the best machine learning algorithms and briefly discuss each of their use cases. On another note, beginners can benefit from Machine Learning training. There are a range of courses available today that can take aspirants closer to a career in this domain.
Top 10 Machine Learning Algorithms
Principal Component Analysis(PCA)/SVD
PCA is an unsupervised technique to understand the global properties of a dataset that consists of vectors. Here, the Covariance Matrix of data points is examined to gain an understanding which data points or dimensions are more important. The eigenvectors of the top PCs of a matrix can be thought as having highest eigenvalues. Basically SVD is a way to compute ordered components; a user can get it even if they don’t have covariance matrix of points.
PCA is used in applications like predicting stock market, gene expression analysis and for pattern classification that does not take into account class labels.
K-Means is an unsupervised algorithm for solving cluster problems. Data sets are categorized into a specific number of clusters in a way that all homogeneous data points are clustered together and heterogeneous together in another cluster.
K- Means form clusters by:
- Selecting k number of points for each cluster, which is termed as centroids
- Formation of a cluster by each data point with the nearest centroids that is k clusters
- Creating novel centroids which are modelled on the existing cluster members
- The closest distance for every data point is calculated within these new centroids. This process is replicated again and again till the centroids are resistant to change
This algorithm is used widely in applications such as detection of different activity types in motion sensors, grouping images into various categories. This algorithm is also used in business like data segmentation based on purchase history, grouping inventories by sales & manufacturing metrics and classifying personas according to various interests.
Support Vector Machine (SVM) Learning Algorithm
Support Vector Machine Learning Algorithm marginalizes the class and increases the distance between them to offer unique distinctions. This algorithm can be used for classifying demands more data efficiency and accuracy. SVM is used in business applications like for the comparison of stock performance over a time period. This comparison is used later to make good investment choices.
Recommender System Algorithm
The Recommender Algorithm operates by filtering and forecasting user preferences and ratings for things by using content based and collaborative techniques. This algorithm filters information and recognizes groups having similar tastes to a target user and join the ratings of the group to make recommendations to that particular user. Recommender System Algorithm also does global product-based associations and offer personalized recommendations according to the user’s ratings.
In this algorithm, data sets are classified into different groups according to certain features, then a test is carried out at each node and at last using branch judgement the data is split into two distinct groups. Tests are executed based on the data that is already there and when new data is added it is categorised into the corresponding group.
This algorithm is used in applications such as pattern recognition, identifying diseases, data exploration and option pricing in finances.
Linear Regression is one of the simplest machine learning algorithms for beginners. It shows the relationship between independent and dependent variables and what happens to the dependent variables when certain changes are made to the independent variables. It is used mainly for applications like risk assessment investigation in health insurance companies, sales forecasting. This algorithm needs very less tuning.
A constrained linear regression with a nonlinearity application after the application of weights which restricts the outputs close to +/- classes is termed as Logistics Regression. This algorithm is trained with optimization methods including L-BFGS and Gradient Descent. Using Gradient descent cross- entropy loss functions are optimised. One thing to remember here is that Logistic Regression contrary to its name is not used for regression; it is used only for classification.
This algorithm widely in applications like-
· Identifying risk factors for diseases as well as planning measures for preventing it
· To classify words into verbs, nouns and pronouns
· For weather forecasting like forecasting rainfall and other weather conditions
· It is used in voting applications to forecast if a particular voter would vote for a specific candidate or not
Feedforward Neural Networks
These are essentially classifiers of multilayered Logistic Regression. They are also alternatively called as Multi-layered perceptrons. Feedforward Neural Networks are utilized for classifying and as autoencoders for unsupervised feature learning.
Convolutional Neural Networks (Convnets)
Invented in the late 80’s by Yann Lecun, the role of Convolutional Neural Networks in machine learning today is undeniable. They can be used for wide purposes such as object detection, image classification and image segmentation. Covnet has convoluted layers that behaves like hierarchical feature extractors.
Recurrent Neural Networks (RNNs)
Recurrent Neural Networks are used for any sequence modelling activities such as machine translation, language modeling and text classification. Today pure RNN are used rarely while its counterparts like GRUs and LSTMs are the latest tool in the majority of sequence modeling activities.