In general, any of the machine learning problems we try to find the best possible optimal model for a given problem. That means finding the best possible model within the given model family, for example, finding the best possible decision tree or finding the best possible KNN model. And if we have more time then we can try all model families available, and come up with the best possible regression model, best possible KNN model, best possible SVM model etc. And among these again select the best possible model, which will be either KNN, SVM or any other.Continue reading “What is Bagging in Ensemble Learning”
It does not matter how much subject knowledge you have, until you do not get a chance to show case it. It will be hidden somewhere within you. If you cannot market yourself well then you will always be lost in the crowd. So why am I talking about all these things. Imagine you are searching for a job and you have all the credentials required, however you are not getting shortlisted even. So what to do? Where is the problem? Are you marketing yourself well? Let’s discuss all these points in details.Continue reading “How to create an Impressive Resume | Make it Simple”
It does not matter how much experience you have, actually anybody can start or switch to data science and machine learning. The only important this is, how much eager you are for it. What it means to you. If you are very much keen to work in this field then nobody can stop you. There might be some short term hurdles however if you are focused enough and know your goals regarding where you want to see yourself after certain years, then you will definitely be successful in overcoming those hurdles.Continue reading “How to start career in Data Science and Machine Learning”
Lot of research is being done in medical field, where researchers are working to develop AI models which can even develop the “Sense of smell”.
It will help medical field to detect illness by smelling the human’s breath. They have achieved great success in detecting chemicals called aldehydes. Aldehydes are associated with human illnesses and stress. It is also helpful in detecting cancer, diabetes, brain injuries by detecting the “woody, musky odor” emitted from Parkinson’s disease even before any other symptoms are identified. Artificially intelligent bots could identify gas leaks or other caustic chemicals, as well. IBM is even using AI to develop new perfumes.
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Bayes Theorem is the extension of Conditional probability. Conditional probability helps us to determine the probability of A given B, denoted by P(A|B). So Bayes’ theorem says if we know P(A|B) then we can determine P(B|A), given that P(A) and P(B) are known to us.Continue reading “Bayes’ Theorem with Example for Data Science Professionals”
As the name suggests, Conditional Probability is the probability of an event under some given condition. And based on the condition our sample space reduces to the conditional element.
For example, find the probability of a person subscribing for the insurance given that he has taken the house loan. Here sample space is restricted to the persons who have taken house loan.Continue reading “Conditional Probability with examples For Data Science”
Probability in itself is a huge topic to study. Applications of probability are found everywhere whether it is medical science, share market trading, sports, gaming Industry and many more. However in this post my focus is on to explain the topics which are needed to understand data science and machine learning concepts.Continue reading “Probability Basics for Data Science”
Variance and Standard Deviation are the most commonly used measures of variability and spread. Variability and spread are nothing but the process to know how much data is being varying from the mean point. And Variance tells us the average distance of all data points from the mean point. Standard deviation is just the square root of the variance. As variance is calculated in squared unit (explained below in the post) and hence to come up a value having unit equal to the data points, we take square root of the variance and it is called as Standard Deviation.Continue reading “Variance, Standard Deviation and Other Measures of Variability and Spread”
k-Nearest Neighbors or kNN algorithm is very easy and powerful Machine Learning algorithm. It can be used for both classification as well as regression that is predicting a continuous value. The very basic idea behind kNN is that it starts with finding out the k-nearest data points known as neighbors of the new data point for which we need to make the prediction. And then if it is regression then take the conditional mean of the neighbors y-value and that is the predicted value for new data point. If it is classification then it takes the mode (majority value) of the neighbors y value and that becomes the predicted class of the new data point.Continue reading “A Complete Guide to K-Nearest Neighbors Algorithm – KNN using Python”