In literal sense, a forest is an area full of trees. Likewise, in technical sense, a Random Forest is essentially a collection of Decision Trees. Although both are classification algorithms which are supervised in nature, which one is better to use?

A Decision Tree is built on an entire data set, using all the features/variables while a Random forest randomly (as the name suggests) selects observations/rows and specific features/variables to build several decision trees and then average the results. Each tree “votes” or chooses the  class and the one receiving the most votes by majority is the “winner” or the predicted class.

A Decision tree is comparatively easier to interpret and visualize, works well on large datasets and can handle categorical as well as numerical data. However, choosing a comfortable algorithm for optimal choice at each node and decision trees are also vulnerable to over fitting.

Random Forests come to our rescue in such situations. Since they select samples and the results are aggregated and averaged, they are more robust than decision trees. Random Forests are a strong modelling technique than Decision Trees.

Read more at: https://www.analyticsvidhya.com/blog/2020/05/decision-tree-vs-random-forest-algorithm/

Machine Learning is one of those technologies which have invaded in our lives to make it better. Without any doubt one can say that even though machine learning is in its initial phase, it has already become a part in our 24/7 running lives. Set of algorithms to use data, learn from it and then forecast future trends for that topic is expanding day by day.

Machine Learning and Data Sciences are often used together in order to predict future from varied data results available with us. One of the famous algorithm used in this field is SVM or Support Vector Machine which can be used for both regression and classification task. It uses the concept of hyperplanes and other mathematical functions in order to produce significant accuracy with less computation power. SVM has already proved itself in text categorization, image recognition, and in bioinformatics and now working in other.

To know more about how SVM works visit : https://towardsdatascience.com/support-vector-machine-introduction-to-machine-learning-algorithms-934a444fca47

We've been thinking about the Internet of Things all wrong. According to various predictions by various companies, there were various statements specifying volume and amount of money, number of connections. These are just numbers, Numbers, more numbers. If we believe in the predictions, there is no way that current analytical solutions can manage that level of information. In the immediate future artificial intelligence capabilities are required. Which means all companies who have an analytics platform play will have to invest in A.I. research, acquire and finally emerge with solutions based on methods beyond machine learning. Or risk being left behind. If this sounds vaguely familiar, it's because right now all efforts are pointing towards machine learning and algorithms as the goal for analytics. To read more visit on: http://www.forbes.com/sites/theopriestley/2015/12/08/a-series-of-unfortunate-tech-predictions-artificial-intelligence-and-iot-are-inseparable/#39f25ec8523a1253d985523a