Artificial intelligence and machine learning are two of the hottest topics of discussion in recent times. We get to hear these terms very commonly due to their wide array of applications in different types of industries. Both these technologies are evolving at a great pace. Most importantly, a blend of the two makes it even more powerful. It is precisely the reason why the current trend lies in focusing on an appropriate combination of AI and ML to enhance its potential further. And this makes industries to gain more profits.
In this video post I have explained about the below ML FAQ:
1. What is Gini
2. What is Gini Index
3. How to calculate Gini and Gini Index
4. How Gini Index helps to decide the Parent and Decision nodes in Decision Tree
In this video I will be explaining about the following ML questions:
1. How to Interpret Decision Tree
2. How splitting happens in the decision tree.
3. What is Entropy?
4. What in Information Gain?
5. How Information gain helps to decide the parent node and further node split?
What is popular between blockchain and big data/data science? A few aspects that quickly struck our minds are that they are both of the best new technologies. Both have the ability to revolutionize the way industry works, and both provide promising career prospects. Blockchain encompasses many of the lucrative coins like BTC, Etherium, free xlm, etc.
Although data science is a comparatively well-established technique, blockchain is at a developing level. Let us explain more about each of them in order to evaluate them better.
The term Machine Learning was given by Arthur Samuel in the year 1959 and he defined it as the “field of study that gives computers the ability to learn without being explicitly programmed”. Now Machine Learning is helping organizations to take data driven decisions rather completely relying on the experience driven decisions.
Further in this article, we will discuss the benefits of Machine Learning in the development process of a mobile app and everything which revolves around Machine Learning and its use in developing a mobile app.
In the field of Machine Learning, logistic regression is still the top choice for classification problems. It is simple yet efficient algorithm which produces accurate models in most of the cases. In its basic form, it uses the logistic function to calculate the probability score which helps to classify the binary dependent variable to its respective class. Logistic regression is the transformed form of the linear regression. In this post I have explained the end to end step involved in the classification machine learning problems using the logistic regression and also performed the detailed analysis of the model output with various performance parameters.