What you'll learn

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

Requirements

Basic programming in any language
Basic Mathematics
Some exposure to Python (but not mandatory)

Description
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.

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

Citación :

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rapidgator_net:
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nitroflare_com:
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