Libraries for Machine learning in Python

-

Libraries for Machine learning in Python

Machine Learning is the study of computer algorithms that improves
automatically through experiences. In the older days, people used to do
the machine learning tasks manually coding all the algorithms,
mathematical, and statistical formulae. This made the process, time-
consuming, and inefficient. But these days it became very efficient by
python libraries. Libraries have made coding easy and time-saving.

Some of the python libraries used in machine learning:

● Numpy

● Pandas

● Scipy

● Scikit-learn

● Matplotlib

● Keras

● Tensorflow

Numpy:

Numpy stands for numerical python. It is used for working with arrays.

Numpy is a very popular Python library for large multi-dimensional

array and matrix processing, with the help of a large collection of high-

level mathematical functions. It is very useful for fundamental scientific

computations in Machine Learning. It is particularly useful for linear

algebra, Fourier transform, and random number capabilities.

Pandas:

Pandas is a popular Python library for data analysis. It is not directly

related to Machine Learning. As we know that the dataset must be

prepared before training. In this case, Pandas was developed specifically

for data extraction and preparation. It provides high-level data structures

and a wide variety of tools for data analysis. It also provides many

inbuilt methods for groping, combining, and filtering data.

Scipy:

Scipy stands for scientific python. It uses NumPy for mathematical and

numerical data analysis. It is built on the NumPy extension and allows

the user to manipulate and visualize data with a wide range of high-level

commands. It is also used for image manipulation such as reading the

image, tinting the image, and resizing the image.

Scikit-Learn:

Scikit-learn is used to build machine learning models. It is built on top

of two basic Python libraries, NumPy and SciPy. Scikit-learn supports

most of the supervised and unsupervised learning algorithms. Scikit-

learn can also be used for data-mining and data analysis, which makes it

a great tool that is starting out with ML.

Matplotlib:

Matplotlib is a python library used for data visualization. It is a plotting

library for the Python programming language and its numerical

mathematics extension NumPy. It is a 2D plotting library used for

creating 2D graphs and plots. A module named pyplot makes it easy for

programmers to plotting as it provides features to control line styles, font

properties, formatting axes, etc. It provides various kinds of graphs and

plots for data visualization, histogram, error charts, bar charts, etc.

Keras:

Keras is an open-source library that provides a python interface for

artificial neural networks. It is a high-level neural networks API capable

of running on top of TensorFlow, CNTK, or Theano. It can run

seamlessly on both CPU and GPU. Keras makes it really for ML

beginners to build and design a Neural Network. One of the best thing

about Keras is that it allows for easy and fast prototyping. It provides

clear and actionable feedback upon user error.

Tensorflow:

Tensorflow is a Python-friendly open source library for numerical

computation that makes machine learning faster and easier. It is a

symbolic math library based on dataflow and differentiable

programming. It is a framework that involves defining and running

computations involving tensors. It can train and run deep neural

networks that can be used to develop several AI applications.

TensorFlow is used in the field of deep learning research and

application.

Comments

Post a Comment

Popular posts from this blog

Our First Workshop Went Like...!

-
Image
Here's the story of the QuAIT's first workshop with its peaks and valleys. It took a team of 6 members and active participation from the students of SRI INDU College Of Engineering and Technology(Hyderabad), to make this workshop a success! Wanna know the full story? Read to know more!  THE TEAM: First of all! the team that has toiled day and night for the workshop. T. RISHWI (CEO) T.NITHYA (AI Researcher) A. SAI KUMAR (APP Developer) RAGHAVENDRA(Software Developer) M. SREENIDHI IYENGAR (AI & ML Engineer) V HARIKA (Quantum Researcher) THE PURPOSE: We know for a fact that Engineering students always want to learn something new but also are a bit lazy when it comes to the traditional way of learning things. So we came up with an idea to conduct a 2-day workshop, where we can share our knowledge on the basics of ML/AI to the passionate students who are willing to put an all-out effort in learning new technologies.  And that's when we got to know that SRI INDU College'...
Read more

Convolutional Neural Networks

-
Image
  Hello, everyone! Welcome back to another interesting series on Applied Deep Learning. Last week we came across Artificial neural networks and its implementations. Today we are going to learn about CNN (convolutional Neural Networks). So let's get started… Convolutional Neural Networks(CNN) are seen and implemented everywhere in our day to day life. CNN is probably considered the most desired deep learning architecture. These networks are quite familiar to feed-forward neural networks which we discussed in the last session. CNN undergoes the concepts of weights and biases. Neuron's arrangements are represented in the form of 3-dimensional arrays. The limelight to CNN in recent years is all because of its immense popularity in deep learning and its effectiveness towards convents.  CNN is computationally efficient and it has a unique ability that it surpassed its predecessors in terms of detecting important components without the involvement of human supervision. The spatial ...
Read more

Classification of Animals Using CNN Model

-
Image
Classification of animals using CNN Model In this model, we try to predict whether the animal is a dog or a cat. #We will use the Keras library to create our model. Keras: is an open - source library that provides a python interface for artificial neural networks. from keras.models import Sequential from keras.layers import Conv2D,Activation,MaxPooling2D,Dense,Flatten,Dropout import numpy as np #Let’s initialize a convolutional neural network using the sequential model of Keras. classifier = Sequential() # Adding Convolution Layer classifier.add(Conv2D(32,(3,3),input_shape=(64,64,3))) #Conv2D stands for a 2-dimensional convolutional layer. Here, 32 is the number of filters needed. A filter is an array of numeric values. (3,3) is the size of the filter, which means 3 rows and 3 columns. The input image is 64 64 3 in dimensions, that is, 64 height, 64 widths, and 3 refer to RGB values. Each of the numbers in this array (64,64,3) is given values from 0 to 255, which describes the pixel in...
Read more