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# How to find squareroot of a number in R?

This recipe helps you find squareroot of a number in R

How to find square root of a no in R? A square root of a number is, suppose A and y are two numbers then, y * 2 = x , then the value of y is the square root of x. Square root is done in order to find the original number that was squared. The square root of a number is calculated as multiplying the number with 0.5. **Syntax — sqrt (x)** x — input number This recipe provides an example of calculating the square root of a number.

```
x <- 36
print(sqrt(x))
```

"Output of the no is" : 6

```
y <- c(4,9,16,25,36)
print(sqrt(y))
```

"Output of the no is" : 2 3 4 5 6

Classification ML Project for Beginners - A Hands-On Approach to Implementing Different Types of Classification Algorithms in Machine Learning for Predictive Modelling

Build your own image similarity application using Python to search and find images of products that are similar to any given product. You will implement the K-Nearest Neighbor algorithm to find products with maximum similarity.

Given big data at taxi service (ride-hailing) i.e. OLA, you will learn multi-step time series forecasting and clustering with Mini-Batch K-means Algorithm on geospatial data to predict future ride requests for a particular region at a given time.

Use cluster analysis to identify the groups of characteristically similar schools in the College Scorecard dataset. Considerations: Clustering Algorithm Data Preparation How will you deal with missing values? Categorical variables? Feature intercorrelations? Feature normalization or scaling? Dimensionality reduction? Hyperparameters How will you set the parameters -- the algorithm's knobs and dials, so to speak -- in order to achieve valid and useful output? Interpretation Is it possible to explain what each cluster represents? Did you retain or prepare a set of features that enables a meaningful interpretation of the clusters? Do the compositions of the clusters seem to make sense? Validation How will you measure the validity of your clustering process? Which metrics will you use and how will you apply them?

In this human activity recognition project, we use multiclass classification machine learning techniques to analyse fitness dataset from a smartphone tracker.

Music Recommendation Project using Machine Learning - Use the KKBox dataset to predict the chances of a user listening to a song again after their very first noticeable listening event.

The project will use rasa NLU for the Intent classifier, spacy for entity tagging, and mongo dB as the DB. The project will incorporate slot filling and context management and will be supporting the following intent and entities. Intents : product_info | ask_price|cancel_order Entities : product_name|location|order id The project will demonstrate how to generate data on the fly, annotate using framework and how to process those for different pieces of training as discussed above .

FEAST Feature Store Example- Learn to use FEAST Feature Store to manage, store, and discover features for customer churn prediction machine learning project.

In this deep learning project, you will implement one of the most popular state of the art Transformer models, BERT for Multi-Class Text Classification

CRNNs combine both convolutional and recurrent architectures and is widely used in text detection and optical character recognition (OCR). In this project, we are going to use a CRNN architecture to detect text in sample images. The data we are going to use is TRSynth100k from Kaggle. Given an image containing some text, the goal here is to correctly identify the text using the CRNN architecture. We are going to train the model end-to-end from scratch.