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.
Who this course is for:
- Anyone willing to take the first step towards data science
- Anyone willing to develop a solid foundation for data science
- Anyone planning to build the first regression / machine learning models
- Anyone willing to learn exploratory data analysis
- Basic programming in any language
- Basic Mathematics
- Some exposure to Python (but not mandatory)
Last updated 7/2018
Beginning with Machine Learning & Data Science in Python.zip | Mirror [Size: 448.9 MB]
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