Neural Network

Neural Network Artificial neural network is the predictive model. Artificial Neural network in machine learning is inspiration for human brain and how it works. Our brain consists of the collection of neurons wired together. Every neuron connected to each other takes output from previous neurons that feed into it, does the calculation and then either … Continue reading Neural Network

Advance Gradient Descent

In Simple BGD (Batch Gradient Descent) the parameters are updated by some of all the square error of data set. However, BGD convergence much accurately but when there are large data set then the convergence will take lot of time and memory. Therefore, to overcome these problems, following evolution were made on optimizing parameters. Stochastic … Continue reading Advance Gradient Descent

Decision Tree

When learning about Decision tree, I learned about Entropy. In my opinion, entropy is important to learn for data science and in decision tree. In simple words, entropy is the measurement of level of impurity in the data set or it is measurement of uncertainty. It is defined as Entropy = – p(a)*log(p(a)) – p(b)*log(p(b)) The … Continue reading Decision Tree

Logistic Regression

Logistic regression is a statistical model, in which analyzing one or more independent variables with the possible outcome. The outcome is measure with a dichotomous(separate line) variable. In logistic regression, the outcome is binary for example if cancer is benign then True/0 or if maligned then False/1 Logistic regression goal is to find the best … Continue reading Logistic Regression

Machine Learning: Regression

Regression in statistics is defined as estimating the relationship among variables. It is defined as to determine the strength of relationship with dependent variable (Y) and change in variables (independent variables (X)). It is to used for financial data, stock market data and business analysis. It helps to understand the relationship between variables(data) generated. For … Continue reading Machine Learning: Regression

Naives Bayes Classifier

Naive Bayes is the method of supervised learning. It is a classification algorithm which is used to predict the class of  a given data points. Naive Bayes is based on Bayes Theorem in which the probability of event which is based on previous knowledge and event. Example: If the vehicles is long width and high height then … Continue reading Naives Bayes Classifier

Hexagonal/Cockburn Architecture

My 2 years of experiences in software development, I found the big challenge is to write program that don’t break business logic of application, since the application code is so coupled with framework classes as well as database. Changing in database, requires the business logic to change according to database changes. Furthermore, If the framework’s … Continue reading Hexagonal/Cockburn Architecture