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Machine Learning

Categories Machine Learning seaborn

How To Plot Confusion Matrix in Python and Why You Need To?

Visualizations play an essential role in the exploratory data analysis activity of machine learning. You can plot confusion matrix using the confusion_matrix() method from sklearn.metrics package. Why Confusion Matrix? After … Read more →

Categories Machine Learning Pandas sklearn

How to Convert Sklearn Dataset to Pandas Dataframe in Python

Sklearn datasets become handy for learning machine learning concepts. When using the sklearn datasets, you may need to convert them to pandas dataframe for manipulating and cleaning the data. You … Read more →

Categories NumPy Python Statistics

How to Normalize a Numpy Array to A Unit Vector in Python?

Numpy arrays are a grid of values of the same type. You can use these arrays to store a list of values that needs to be used for data analysis … Read more →

Categories Statistics Python

How to Normalize Data Between 0 and 1

Normalization of data is transforming the data to appear on the same scale across all the records. You can normalize data between 0 and 1 range by using the formula … Read more →

Categories Machine Learning Python

How To Do Train Test Split Using Sklearn in Python – Definitive Guide

In machine learning, Train Test split activity is done to measure the performance of the machine learning algorithm when they are used to predict the new data which is not … Read more →

Categories Machine Learning Python

How To Save And Load Machine Learning Models To Disk in Python?

Machine learning models are created for classifying or predicting future data. To use it for predicting the future data, these models must be saved into the disk, so that they … Read more →

Categories AWS Machine Learning

How To Load Data From AWS S3 into Sagemaker (Using Boto3 or AWSWrangler)

SageMaker provides the compute capacity to build, train and deploy ML models. You can load data from AWS S3 to SageMaker to create, train and deploy models in SageMaker. You … Read more →

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