What are Activation Functions?

Activation functions are mathematical equations that determine the output of a neural network. The function is attached to each neuron in the network after it calculates a “weighted sum(Wi)” of its input(xi), adds a bias and determines whether it should be activated (“fired”) or not, based on whether each neuron’s input is relevant for the model’s prediction.

Important use of any activation function is to introduce non-linear properties to our Network.

All the input Xi’s are multiplied with their weight Wi’s assigned to each link and summed together along with Bias b .

Activation function Types…


The complete lifecycle of a Predictive modeling includes Data Cleaning, Pre-processing and Data Wrangling. Now comes the stage where we try to fit in the model and the end goal is to achieve Low Bias and Low Variance. The most important step is of Model Evaluation post Model Creation . We often fail to understand the resultant performance Evaluation metrics.

Let us know go through the Confusion Matrix-

Supervised Classification problems are evaluated with the help of Confusion Matrix.

Today, let’s understand the confusion matrix once and for all.

It is a table of 2x2 having combinations of actual and…


Along with R, Python, having a SQL knowledge is now an need of an hour for all those working/ looking out jobs in the field of Data Science. By 2025, the amount of data generated each day is expected to reach 463 exabytes globally. In order to store this large amount of data it is absolutely necessary for one to have Database knowledge.

“Data is the new oil” is perhaps one of the most popular catchphrases that can describe the fuel that makes our increasingly interconnected world go round.


As we know most of the time of data oriented industries goes in Data Preparing and Data Cleaning. In some cases the time consumption for data preparation for data professionals can go upto 90%. Dealing with missing data is one of the most difficult parts in the data preparation phase.

They are often represented as NaNs, blanks or 0 in the data.


As we know most of the time of data oriented industries goes in Data Preparing and Data Cleaning. In some cases the time consumption for data preparation for data professionals can go upto 90%. Dealing with missing data is one of the most difficult parts in the data preparation phase.

They are often represented as NaNs, blanks or 0 in the data.


Photo by Jessica Ruscello on Unsplash

What are Outliers?

Outliers are datapoints in dataset in which are abnormal observations amongst the normal observations and can lead to weird accuracy scores which can skew measurements as the results do not present the actual results.

Formal Definition:

Outlier is an observation that appears far away and diverges from an overall pattern in a sample. Outliers in input data can skew and mislead the training process of machine learning algorithms resulting in longer training times, less accurate models and ultimately poorer results.


OutLiers in Machine Learning

What are Outliers?

Outliers are datapoints in dataset in which are abnormal observations amongst the normal observations and can lead to weird accuracy scores which can skew measurements as the results do not present the actual results.

Formal Definition:

Outlier is an observation that appears far away and diverges from an overall pattern in a sample. Outliers in input data can skew and mislead the training process of machine learning algorithms resulting in longer training times, less accurate models and ultimately poorer results.

Prerna Nichani

Data Scientist at Capgemini www.linkedin.com/in/prerna-nichani/

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