- Course type
- Paid course
- 4 hours
- 49 lessons
- Available on completion
- Course author
- UNP United Network of Professionals
- You will be able to apply data science algorithms for solving industry problems
- You will have a clear understanding of industry standards and best practices for predictive model building
- You will be able to derive key insights from data using exploratory data analysis techniques
- You will be able to efficiently handle data in a structured way using Pandas
- You will have a strong foundation of linear regression, multiple regression and logistic regression
- You will be able to use python scikit-learn for building different types of regression models
- You will be able to use cross validation techniques for comparing models, select parameters
- You will know about common pitfalls in modeling like over-fitting, bias-variance trade off etc..
- You will be able to regularize models for reliable predictions
85% of data science problems are solved using exploratory data analysis (EDA), visualization, regression (linear & logistic). So naturally, 85% of the interview questions come from these topics as well.
This concise course, created by UNP, focuses on what matter most. This course will help you create a solid foundation of the essential topics of data science. With this solid foundation, you will 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:
independently build machine learning and predictive analytics models
confidently appear for exploratory data analysis, foundational data science, python interviews
demonstrate mastery in exploratory data science and python
demonstrate mastery in logistic and linear regression, the workhorses of data science
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. In addition, concepts of overfitting, regularization etc., are discussed in detail. These fundamental understandings are crucial as these can be applied to almost every machine learning method.
This course also provides an understanding of the industry standards, best practices for formulating, applying and maintaining data-driven solutions. It starts with a basic explanation of Machine Learning concepts and how to set up your environment. Next, data wrangling and EDA with Pandas are discussed with hands-on examples. Next, linear and logistic regression is discussed in detail and applied to solve real industry problems. Learning the industry standard best practices and evaluating the models for sustained development comes next.
Final learnings 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.