Home

Machine Learning/Artificial Intelligence

This section contains latest tutorials, articles on Machine learning and Artificial Intelligence.

Articles, Tutorials on Machine Learning/Artificial Intelligence

• Machine Learning/ Artificial Intelligence/ Deep Learning/ Data Science.
  Machine Learning/ Artificial Intelligence/ Deep Learning/ Data Science: Well, this is the very first article on Machine Learning and its brother subjects. Before we proceed into studying further...
• How to learn Machine Learning and Artificial Intelligence?
  How to learn Machine Learning and Artificial Intelligence? In this article, I am going to acknowledge you with some of the renowned and useful resources that are really handy for you. I have extracted these resources from many blogs and videos by experts of this domain...
• Basic concepts of Machine Learning.
  This article is about Machine Learning, we will be focusing about the basic concept behind machine learning and some terminologies associated with it. We will discuss about its basic mechanism, Artificial Intelligence, analogy between AI and Internet of things (IOT).
• Machine Learning and its types.
  Machine Learning and its types: In this article, we are going to study about the different forms of a machine learning Problem. From this article onwards, we are going to dive deeper into ML concepts. This article will be followed by another upcoming article leading to its example problems for your better understanding. For the convenience few images have been downloaded from Google image section. For now, let’s just start, cause now is the time to take flight with me. Feel Free to ask your doubts in comment section.
• Naive Bayes Algorithm in Machine learning.
  In this article, we are going to learn about Naive Bayes Algorithm in Machine learning, what is the algorithm and how it will be solved?
• Linear Regression in Machine Learning.
  In this article, we are going to discuss about linear regression and its implication in the field of machine learning.
• Spyder: A powerful weapon for Machine Learning in Python.
  In this article, we are discussing about a powerful tool for machine learning i.e. Spyder aka Scientific python development environmentand some of its basic features that gives you an upper edge over the conventional IDLE.
• Data splitting | Machine Learning.
  In this article, we will learn one of the methods to split the given data into test data and training data in python.
• Validation before Testing | Machine Learning.
  In this article, we are going to learn about the validation before testing in Machine learning.
• Pearson’s correlation and its implication in machine learning.
  In this article, we are going to learn about Pearson’s correlation and its implication in machine learning.
• Spearman's correlation and its implication in machine learning.
  In this article, we are going to learn about Spearman's correlation and its implication in machine learning.
• Root-Mean-Square Error (RMSE).
  Root-Mean-Square Error (RMSE): In this article, we are going to learn one of the methods to determine the accuracy of our model in predicting the target values.
• Kendall's Tau correlation in machine learning.
  In this article, we would be looking at Kendall’s correlation and why should we use this for machine learning?
• Decision Trees in Machine learning.
  In this article, we are going to learn about the decision trees in Machine Learning, where are they used and its advantages over other algorithms.
• K - Nearest Neighbors (KNN) Algorithm | Machine Learning
  Here, we are going to implement K - Nearest Neighbors (KNN) Algorithm | Machine Learning using Python code.
• Probabilistic graphical model (PGMs) Algorithm | Machine Learning
  In this article, we are going to discuss about PGMs (probabilistic graphical model) Algorithm in machine learning.
• Needs and Examples of Probabilistic graphical model (PGMs) | Machine Learning
  In this article, we are going to learn about the needs and examples of PGMs (Probabilistic graphical model).
• Bayesian Network in Machine Learning
  In this article, we are going to discuss about Bayesian Network which is a part of directed graph in PGMs.
• Applications of PGMs and relation and conversion of Bayes and Markov's model
  In this article, we will know more about PGMs: its application, Relation between Bayes network and Markov’s Random field and conversion of Bayes network into Markov’s random field.
• The boyfriend problem using PGMs and Neural Network.
  In this article, we will see that How PGMS and neural network are used to solve a problem and also to know which is the best algorithm which iseasy to use and to solve a problem?
• Markov Random Field Model in Machine Learning.
  In this article, we are going to discuss about undirected graph model called Markov's random field model.

Machine Learning with Java

• Introduction to weka and Machine learning in Java.
  In this article, we will learn about weka api and will see that how can we use it in our eclipse IDE?
• Attribute relation file format | Machine Learning.
  In this article, we will be looking at the use of attribute relation file format for machine learning in java.
• Attribute Selection | Machine Learning.
  In this article, will be looking at the method for selecting the desired set of attributes that will form the basis of our machine learning model.
• Training and Testing Sets in Java | Machine Learning.
  In this article, we would be looking at how can we perform cross-validation the given data set and then split out data into training and testing sets?