Analytics is growing at an unprecedented rate. However, beginners usually struggle to gather the necessary resources and understand the jargons. This module aims to introduce novices to Data Analytics and understand the thin demarcations between Machine Learning and Data Analytics.
This module majorly covers all the basic knowledge of Statistics and the required Statistical Tools to implement it. A must-read for all the budding data scientists out there!
A popular technique for classification under supervised learning. Read on to understand the technicalities with the step by step implementations using Python.
Reduction of dimensionality is the method of reducing with consideration the dimensionality of the function space by obtaining a collection of principal features.Reduction of the dimensionality can be further divided into a collection of features and extraction of features.
One of the most basic yet a very useful technique in supervised learning algorithms. Read on to know how simple modelling techniques can find utility in solving real life problems.
A time series is a sequence of numerical data points in successive order. This module explains the major terms associated with time series analysis, various models used for doing it and it's real life implementation in our case study.
Bagging and Boosting are the most general and yet most effective way of improving the accuracy of any given algorithm and form the basis of Ensemble learning. Read on to understand the technicalities along with its implementation.
Clustering is the process of segregating the available data points into different groups on the basis of similarity measures. Read this module to learn about the different methods of clustering and their practical implementations.
Neural networks, are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another.
Neural networks, a beautiful biologically-inspired programming paradigm which enables a computer to learn from observational data, is rapidly becoming the sole solution to achieveing most of the AI goals. This module describes the working of deep neural networks and contains a basic introduction to convolutional and recurrent NNs.