Machine Learning Algorihms
This course is designed for professionals, who are working into different domains. A basic qualification would be an engineering, IT, Statistics or analyst background. No programming background is required. This course is very methodical, designed with a step by step approach, to bridge the gap to be a data scientist. Total duration of the course is 4 months. First month – We introduce the Big picture. We start with a Churn prediction project – which is domain agnostic, since churn happens across all domains – Telecom, Banking, Ecommerce etc. We cover bivariate analysis including fine classing, coarse classing, Weight of evidence, Information value. First project we build using RPART in R, It’s a single tree model, a very accurate algorithm used in Data Science industry. We also cover the business impact part using the data science model – a must skill to crack high value interviews. Here you also learn lot of data science skills in Microsoft Excel.
Next project is building the same churn model using Random Forest, a mainstream classification model algorithm. We compare advantages of both the model algorithms and discuss an application of those in Fintech Credit Risk projects – in MSME renewal credit risk model, An application of trade off between Risk classification and Churn, a high business impact project that helped many students to crack high CTC Data Science and Credit Risk interviews.
Second month – We introduce Python and construct building. Python is the mainstream programming language today in Data science, analytics, Machine learning and Artificial intelligence.
We learn 7 pillars in Python programming for Data science – Subset, aggregation, data structure, loop, function, join and Numpy. Along with those we learn 6 most frequently appeared bugs in Pythons and how to handle those. At the end of the 1 month course, you can effortlessly write and automate python codes using loops and functions. Along with Python skills, we teach construct building in the second month. Construct is approach to solve a data science problem – a must skill to be a successful data scientist. In fact how good you are at building constructs, would decide how successful you would be in your data science career. We pick up a real business problem, and solve it using at least 5 constructs, while programming part is done in Python.