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Intro To Matrices With Python And Numpy
Matrices With Numpy Numpy is really good at working with matrix mathematics, and matrix math is everywhere in data science. It will save you a lot of time if you get acquainted with the numpy library. Okay, without further ado, let’s get started by importing numpy and creating a sample matrix to work with.
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How To Create Vectors And Matrices With Numpy
Numpy Vectors And Matrices Numpy vectors and matrices will show up all the time in data science and machine learning. So, let’s take a look at how to create them. Let’s first import numpy. import numpy as np Next, let’s try to create our first vector row. np.array([1,2,3,4]) # array([1, 2, 3, 4]) Wasn’t
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How To Add/Subtract With Numpy Matrices
Adding/Subtracting With Matrices Adding and subtracting matrices is a fundamental part of linear algebra and a lot of data science techniques. Let’s take a look at how to add/subtract with numpy arrays and matrices. First, let’s import numpy and create some matrices to practice our operations with. import numpy as np a = np.array([[1,2,3,4],[5,6,7,8],[9,10,11,12],[13,14,15,16]])
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How To Calculate Dot Product With Numpy
Numpy dot product Dot product is a common linear algebra matrix operation to multiply vectors and matrices. It is commonly used in machine learning and data science for a variety of calculations. It can be simply calculated with the help of numpy. This post will go through an example of how to use numpy
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How To Calculate Eigenvectors And Eigenvalues With Numpy
Eigenvectors and eigenvalues with numpy In machine learning, eigenvectors and eigenvalues come up quite a bit. They are used in a variety of data science techniques such as Principal Component Analysis for dimensionality reduction of features. Let’s take a look at how to calculate these linear algebra values efficiently with Numpy, a popular python
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Python Scikit Learn Random Forest Classification Tutorial
Random Forest Random forest is a classic machine learning ensemble method that is a popular choice in data science. An ensemble method is a machine learning model that is formed by a combination of less complex models. In this case, our Random Forest is made up of combinations of Decision Tree classifiers. How this work
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Build Your First Neural Network With Python And Keras
Keras Deep Learning Keras is one of the most popular software frameworks used currently for deep learning in python. It is a higher level api that makes it extremely simple to build deep neural nets on top of frameworks such as Tensorflow, Theano, and CNTK. In this example, we will use the mnist dataset that
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The Different Types Of Machine Learning Problems
Types Of Machine Learning There are 3 main types of machine learning problems: Supervised Machine Learning Unsupervised Machine Learning Reinforcement Learning Supervised Machine Learning In supervised machine learning, there is usually labeled data that is used to predict a target attribute. The main classes of supervised machine learning: Classification Regression Classification is used when you
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Python Perceptron Tutorial
Python Perceptron Neural networks have been a popular topic lately. It comes up quite often in conversations throughout the tech circle. The basic building block of any neural network is the perceptron. It has actually been around for some time in computer science. To learn about how they work, lets build our own perceptron
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How To Standardize Data In Python With Scikit Learn
Standardization Sometimes when you are working with datasets for data science, you will need to standardize your dataset before fitting a machine learning model to it. What exactly does standardization do? It essentially takes your values and shifts the values so that the original mean value is placed at 0 and the standard deviation is
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