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How to install r studio for machine learning pro#
MacBook Pro 13″: Processor 2.3 GHz Quad-Core Intel Core i5 Memory 8 GB 2133 MHz LPDDR3 Graphics Intel Iris Plus Graphics 655 1536 MB.You will use it a lot, it contains the classes of different statistical features of the data.īest Hardware Environment Setup for Machine Learning Stats: this here is the statistical package in R.Matrix: this package contains all the Matrices types and methods you are going need when you are doing high dimensional calculations in R.We are using Python for most of the projects requiring cluster analyses so we rarely use this library. Cluster: this package is useful for cluster analysis.This will help you visualize your data, similar to Matplotlib in Python. Graphics: this is the graphics package in R.Datasets: this is an R package that contains the datasets that we use when we want to try our models out, or just to find how data is related, find trust intervals, find the distribution, etc.You can find our Deep Learning articles and read more. It has tons of stuff like layers, optimizers, loss functions that will help you in creating the best possible models. Keras is a high-level API for Tensorflow that we like to use because it allows creating Neural Networks in the easiest and most understandable way possible.
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Tensorflow + Keras: whenever we do something that is Deep Learning related we use Tensorflow.We have a couple of Computer Vision articles that you can read. You can do tons of stuff on images and videos. It is very powerful and you will find it useful in most of the cases regarding Computer Vision. OpenCV: this is one of the best Computer Vision and Image Processing libraries out there.We use it when we create and train our Machine Learning models. Scikit-learn: this is an open-source Python library that features many classifications, regression, and clustering algorithms like support vector machines(SVMs), random forests, gradient boosting, k-means, and DBSCAN.The main packages include modules for: optimization, linear algebra, integration, FFT, signal, and image processing. SciPy: this is an open-source Python library for scientific and technical computing.It organizes the data in structures called data frames and those will help you a lot with a clear representation of your data as well as customization of it. Pandas: this library is very nice to use when you need to organize your data and make it easier to use and understand.We use it a lot in Computer Vision if we need to plot a couple of images to make some comparisons. It can do tons of stuff with your data, like plot functions, bar charts, etc. Matplotlib: we use this library for a visual representation of the results.
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It will help you organize your data for your models. It allows you high dimensional arrays manipulations like arithmetic operations, conversions, etc. NumPy: this is one of the essential libraries when you work with Python Projects.Essential Libraries for Machine Learning Setup Environment