Udemy - Beginning With Machine Learning & Data Science In Python [TP]
85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). Naturally, 85% of the interview questions comes from these topics as well.
This is a concise course created by UNP to focus on what matter most. This course will help you create a solid foundation of the essential topics of data science. With a solid foundation, you will be able to go a long way, understand any method easily, and create your own predictive analytics models.
At the end of this course, you will be able to:
Get your hands dirty by building machine learning models
Master logistic and linear regression, the workhorse of data science
Build your foundation for data science
Fast-paced course with all the basic & intermediate level concepts
Learn to manage data using standard tools like Pandas
This course is designed to get students on board with data science and make them ready to solve industry problems. This course is a perfect blend of foundations of data science, industry standards, broader understanding of machine learning and practical applications.
Special emphasis is given to regression analysis. Linear and logistic regression is still the workhorse of data science. These two topics are the most basic machine learning techniques that everyone should understand very well. Concepts of over fitting, regularization etc. are discussed in details. These fundamental understandings are crucial as these can be applied to almost every machine learning methods.
This course also provide an understanding of the industry standards, best practices for formulating, applying and maintaining data driven solutions. It starts off with basic explanation of Machine Learning concepts and how to setup your environment. Next data wrangling and EDA with Pandas are discussed with hands on examples. Next linear and logistic regression is discussed in details and applied to solve real industry problems. Learning the industry standard best practices and evaluating the models for sustained development comes next.
Final learning are around some of the core challenges and how to tackle them in an industry setup. This course supplies in-depth content that put the theory into practice.
