Sabudh Data Science Internship(2021)

   In this week we were taught about the genetic algorithms, and everything discussed during the week is mentioned below- Introduction to Genetic Algorithms A  genetic algorithm  is a search heuristic that is inspired by Charles Darwin’s theory of natural evolution. This algorithm reflects the process of natural selection where the fittest individuals are selected for reproduction in order to produce offspring of the next generation. Notion of Natural Selection The process of natural selection starts with the selection of fittest individuals from a population. They produce offspring which inherit the characteristics of the parents and will be added to the next generation. If parents have better fitness, their offspring will be better than parents and have a better chance at surviving. This process keeps on iterating and at the end, a generation with the fittest individuals will be found. This notion can be applied for a search problem. We consider a set of solut...

 Week 13 started with continuing the Unsupervised Learning topic. What Is the Problem with the K-Means Method? The k-means algorithm is sensitive to outliers! Since an object with an extremely large value may substantially distort the distribution of the data. K-Medoids:  Instead of taking the mean value of the objects in a cluster as a reference point, medoids can be used, which is the most centrally located object in a cluster Drawbacks of Partitional Clustering Natural clusters may be split if clusters are not well separated (large inter-cluster distances) have very different cluster sizes When cluster shapes are not convex Hierarchical Clustering Create a nested series of partitions represented in the form of a dendogram Shows how objects are grouped together step by step Typical stopping criteria Number of clusters Minimum distance between clusters being greater than a user defined threshold Types: Agglomerative Starts by assuming that each object is a separate cluster Su...

During this week's internship, we attended 30 hours of the Master Class on Deep Learning and its applications conducted by BML Munjal University.

   It’s week 10!,here we are gonna discuss ensemble models. Some briefing of the teachings are written below What is an ensemble method? Ensemble models in machine learning combine the decisions from multiple models to improve the overall performance. They operate on the similar idea as employed while for eg buying headphones. The main causes of error in learning models are due to  noise, bias and variance . Ensemble methods help to minimize these factors . These methods are designed to improve the stability and the accuracy of Machine Learning algorithms. Scenario 2:  Let’s suppose that you have developed a health and fitness app. Before making it public, you wish to receive critical feedback to close down the potential loopholes, if any. You can resort to one of the following methods, read and decide which method is the best: You can take the opinion of your spouse or your closest friends. You can ask a bunch of your friends and office colleagues. You can launch a ...