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### Top Trends in AI in 2018

Added by Pradeep Menon on February 19, 2018 at 10:00pm — 1 Comment

### Data Science Simplified Part 11: Logistic Regression

In the last blog post of this series, we discussed classifiers. The categories of classifiers and how they are evaluated were discussed. We have also discussed regression models in depth. In this post, we dwell a little deeper in how regression models can be used for classification tasks.

Logistic Regression is a widely used regression model used for classification tasks. As usual, we will discuss by example. No Money bank approaches us with a problem. The bank wants…

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### Data Science Simplified Part 10: An Introduction to Classification Models

Webster defines classification as follows:

A systematic arrangement in groups or categories according to established criteria.

The world around is full of classifiers. Classifiers help in preventing spam e-mails. Classifiers help in identifying…

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Added by Pradeep Menon on September 19, 2017 at 6:00pm — 1 Comment

### Data Science Simplified Part 9: Interactions and Limitations of Regression Models

In the last few blog posts of this series discussed regression models at length. Fernando has built a multivariate regression model. The model takes the following shape:

price = -55089.98 + 87.34 engineSize +…
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### Data Science Simplified Part 8: Qualitative Variables in Regression Models

The last few blog posts of this series discussed regression models. Fernando has selected the best model. He has built a multivariate regression model. The model takes the following shape:

price = -55089.98 + 87.34 engineSize + 60.93…

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### Data Science Simplified Part 7: Log-Log Regression Models

In the last few blog posts of this series, we discussed simple linear regression model. We discussed multivariate regression model and methods for selecting the right model.

Fernando has now created a better model.…

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### Data Science Simplified Part 6: Model Selection Methods

In the last article of this series, we had discussed multivariate linear regression model. Fernando creates a model that estimates the price of the car based on five input parameters.…

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Added by Pradeep Menon on August 9, 2017 at 4:00pm — 1 Comment

### Data Science Simplified Part 5: Multivariate Regression Models

In the last article of this series, we discussed the story of Fernando. A data scientist who wants to buy a car. He uses Simple Linear Regression model to estimate the price of the car.…

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Added by Pradeep Menon on August 6, 2017 at 5:30am — 1 Comment

### Data Science Simplified Part 4: Simple Linear Regression Models

In the previous posts of this series, we discussed the concepts of statistical learning and hypothesis testing. In this article, we dive into linear regression models.

Before we dive in, let us recall some important aspects of statistical learning.

Independent and Dependent…

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### Data Science Simplified Part 2: Key Concepts of Statistical Learning

In the first article of this series, I had touched upon key concepts and processes of Data Science. In this article, I will dive in a bit deeper. First, I will define what is Statistical learning. Then, we will dive into key concepts in Statistical learning. Believe me; it is simple.

As per …

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### Data Science Simplified Part 1: Principles and Process

In 2006, Clive Humbly, UK Mathematician, and architect of Tesco’s Clubcard coined the phrase “Data is the new oil. He said the following:

”…

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### Data Science Simplified Part 3: Hypothesis Testing

Edward Teller, the famous Hungarian-American physicist, once quoted:

“A fact is a simple statement that everyone believes. It is innocent, unless found guilty. A hypothesis is a novel suggestion that no one wants to believe. It is guilty, until…
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### Architectural Primer: Conversation as a Platform (CAAP) on Azure

According to Mckinsey report, Artificial Intelligence (AI) is poised to unleash digital disruption and companies needs to start preparing…

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### Demystifying Data Lake Architecture

According to Gartner, 80% of successful CDOs will have value creation or revenue generation as their Number 1 priority through 2021.

To create the maximum value out the organization’s data landscape, traditional decision support system architecture are no longer adequate. New architectural patterns need to be developed to harness the…

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