MACHINE LEARNING RECIPES
DATA CLEANING PYTHON
DATA MUNGING
PANDAS CHEATSHEET
ALL TAGS
# How to compute Z to the power of n using numpy if Z is a large vector?

This recipe helps you compute Z to the power of n using numpy if Z is a large vector

So this recipe is a short example on how to compute power of a large array. Let's get started.

```
import numpy as np
```

Let's pause and look at these imports. Numpy is generally helpful in data manipulation while working with arrays. It also helps in performing mathematical operation.

```
def power(Z,n):
return np.power(Z,n)
```

We have a created a function which will return an array passed to it raised to power n.

```
print(power(np.random.random(1000),3))
```

We call power function to find out the array of size 1000 raised to the power n.

Once we run the above code snippet, we will see:

Scroll down to the ipython file for visualizing the output.

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

In this data science project, you will work with German credit dataset using classification techniques like Decision Tree, Neural Networks etc to classify loan applications using R.

Use the Zillow dataset to follow a test-driven approach and build a regression machine learning model to predict the price of the house based on other variables.

In this Deep Learning Project, you will use the credit card fraud detection dataset to apply Anomaly Detection with Autoencoders to detect fraud.

In this time series project, you will learn how to build an autoregressive model in Python from Scratch for forecasting time series data.

This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Use the Amazon Reviews/Ratings dataset of 2 Million records to build a recommender system using memory-based collaborative filtering in Python.

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.

In this machine learning and IoT project, we are going to test out the experimental data using various predictive models and train the models and break the energy usage.

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