Session Summary

In this session, you were introduced to the basics of Data Science and Machine Learning. You started with the difference between Data Science and Data Analytics. You learnt the workings of a database through the e-commerce example. 

Further, you were introduced to EDA. You learnt the importance of features and feature engineering to draw insights from the data. You also learnt about Univariate and Bivariate analysis and saw which one is preferred. 

Then you moved on to the big world of Big Data and learnt the 3 Vs that help any dataset to qualify as big data. In parallel, you learnt about Parallel  Computing and learnt how to distribute work to ensure that work is done more efficiently.

Then you moved to the world of Predictive analytics and learnt about different prediction techniques like regression and time series analysis. Further, you were introduced to the world of Machine Learning where you learnt about Supervised and Unsupervised learning algorithms and their implementations in the form of Classification and Clustering respectively.

You further moved to Optimization problems and were introduced to the concepts to Deep Learning, Neural Networks and Natural Language Processing. You finished with an introduction to Artificial Intelligence and Recommender System.

You can download the lecture notes for this session from the link below.