Project on ML - Churn Prediction Model using R Studio
Machine Learning
Machine Learning,R Programming,Development
Lectures -14
Resources -1
Duration -1 hours
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Course Description
This course covers all the steps that one should take while solving a business problem through linear regression. This course will give you an in-depth understanding of machine learning and predictive modeling techniques using R. RStudio is an integrated development environment for R, a programming language for statistical computing and graphics. It is available in two formats: RStudio Desktop is a regular desktop application while RStudio Server runs on a remote server and allows accessing RStudio using a web browser.
R and Python are the most popular languages used for machine learning. Both open-source languages provide a huge repertoire of statistical and predictive tools. However, they take very different approaches to data analytics.
Artificial Neural Networks (ANN) are now a staple within the sub-field of Machine Learning called Deep Learning. Deep learning algorithms can be vastly superior to traditional regression and classification methods (e.g. linear and logistic regression) because of the ability to model interactions between features that would otherwise go undetected. The challenge becomes explainability, which is often needed to support the business case. The good news is we get the best of both worlds with Keras and lime.
Goals
- This course has been carefully crafted in order to upgrade oneself in the genre of Data Analysis and Data Science. This training will go a long way in making you the data scientist organizations are looking out for by means of making you understand deeper concepts of Machine Learning!
Prerequisites
This course has some pre-requisite to ensure that the candidates who enroll for it are well prepared to understand the course material. The pre-requisite is not too long and is also possible that a student can take a bridge course if pre-requites are not met.
- Students should have enough familiarity with basic linear algebra, calculus, probability, and statistic. These courses need not be at a very high level. If you remember what you learned in high school or junior college or can revise it quickly, then that should be enough.
- Familiarity with at least one programming language is recommended. Anyone language such as C, C++, Java, PHP, etc. is fine. This ensures that you understand the programming examples and assignments and do not spend too much time there. If you have not done coding before, you can take a bridge course before enrolling for this machine learning training. This will make your life very easy.
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Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
2 Lectures
-
Introduction to the Course 07:03 07:03
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Reference Files
Types of Data
8 Lectures
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Transformation and Model
4 Lectures
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Instructor Details
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Corporate Bridge Consultancy Private Limited
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