Best Python libraries for Machine Learning

Best Python libraries for Machine Learning - An expanded list of best Python libraries for data science with a fresh look to the ones … With its help, you can implement many machine learning methods and …

Machine Learning, as the name suggests, is the science of programming a computer by which they are able to learn from different kinds of data. A more general definition given by Arthur Samuel is – “Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed.” They are typically used to solve various types of life problems.

In the older days, people used to perform Machine Learning tasks by manually coding all the algorithms and mathematical and statistical formula. This made the process time consuming, tedious and inefficient. But in the modern days, it is become very much easy and efficient compared to the olden days by various python libraries, frameworks, and modules. Today, Python is one of the most popular programming languages for this task and it has replaced many languages in the industry, one of the reason is its vast collection of libraries. Python libraries that used in Machine Learning are:
NumpyScipyScikit-learnTheanoTensorFlowKerasPyTorchPandasMatplotlib

Numpy

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. High-end libraries like TensorFlow uses NumPy internally for manipulation of Tensors.

# Python program using NumPy 
# for some basic mathematical 
# operations
  
import numpy as np
  
# Creating two arrays of rank 2
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6], [7, 8]])
  
# Creating two arrays of rank 1
v = np.array([9, 10])
w = np.array([11, 12])
  
# Inner product of vectors
print(np.dot(v, w), "\n")
  
# Matrix and Vector product
print(np.dot(x, v), "\n")
  
# Matrix and matrix product
print(np.dot(x, y)) 

Output:

219 

[29 67] 

[[19 22]
 [43 50]]

For more details refer to Numpy.

SciPy

SciPy is a very popular library among Machine Learning enthusiasts as it contains different modules for optimization, linear algebra, integration and statistics. There is a difference between the SciPy library and the SciPy stack. The SciPy is one of the core packages that make up the SciPy stack. SciPy is also very useful for image manipulation.

# Python script using Scipy 
# for image manipulation
  
from scipy.misc import imread, imsave, imresize
  
# Read a JPEG image into a numpy array
img = imread('D:/Programs / cat.jpg') # path of the image
print(img.dtype, img.shape)
  
# Tinting the image
img_tint = img * [1, 0.45, 0.3]
  
# Saving the tinted image
imsave('D:/Programs / cat_tinted.jpg', img_tint)
  
# Resizing the tinted image to be 300 x 300 pixels
img_tint_resize = imresize(img_tint, (300, 300))
  
# Saving the resized tinted image
imsave('D:/Programs / cat_tinted_resized.jpg', img_tint_resize) 

For more details refer to documentation.

Scikit-learn

Skikit-learn is one of the most popular ML libraries for classical ML algorithms. It is built on top of two basic Python libraries, viz., 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 who is starting out with ML.

# Python script using Scikit-learn 
# for Decision Tree Clasifier
  
# Sample Decision Tree Classifier
from sklearn import datasets
from sklearn import metrics
from sklearn.tree import DecisionTreeClassifier
  
# load the iris datasets
dataset = datasets.load_iris()
  
# fit a CART model to the data
model = DecisionTreeClassifier()
model.fit(dataset.data, dataset.target)
print(model)
  
# make predictions
expected = dataset.target
predicted = model.predict(dataset.data)
  
# summarize the fit of the model
print(metrics.classification_report(expected, predicted))
print(metrics.confusion_matrix(expected, predicted)) 

Output:

DecisionTreeClassifier(class_weight=None, criterion='gini', max_depth=None,
            max_features=None, max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, presort=False, random_state=None,
            splitter='best')
              precision    recall  f1-score   support

           0       1.00      1.00      1.00        50
           1       1.00      1.00      1.00        50
           2       1.00      1.00      1.00        50

   micro avg       1.00      1.00      1.00       150
   macro avg       1.00      1.00      1.00       150
weighted avg       1.00      1.00      1.00       150

[[50  0  0]
 [ 0 50  0]
 [ 0  0 50]]

For more details refer to documentation.

Theano

We all know that Machine Learning is basically mathematics and statistics. Theano is a popular python library that is used to define, evaluate and optimize mathematical expressions involving multi-dimensional arrays in an efficient manner. It is achieved by optimizing the utilization of CPU and GPU. It is extensively used for unit-testing and self-verification to detect and diagnose different types of errors. Theano is a very powerful library that has been used in large-scale computationally intensive scientific projects for a long time but is simple and approachable enough to be used by individuals for their own projects.

# Python program using Theano
# for computing a Logistic 
# Function
  
import theano
import theano.tensor as T
x = T.dmatrix('x')
s = 1 / (1 + T.exp(-x))
logistic = theano.function([x], s)
logistic([[0, 1], [-1, -2]]) 

Output:

array([[0.5, 0.73105858],
       [0.26894142, 0.11920292]])

For more details refer to documentation.

** TensorFlow**

TensorFlow is a very popular open-source library for high performance numerical computation developed by the Google Brain team in Google. As the name suggests, Tensorflow 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 widely used in the field of deep learning research and application.

#  Python program using TensorFlow
#  for multiplying two arrays
  
# import `tensorflow` 
import tensorflow as tf
  
# Initialize two constants
x1 = tf.constant([1, 2, 3, 4])
x2 = tf.constant([5, 6, 7, 8])
  
# Multiply
result = tf.multiply(x1, x2)
  
# Intialize the Session
sess = tf.Session()
  
# Print the result
print(sess.run(result))
  
# Close the session
sess.close() 

Output:

[ 5 12 21 32]

For more details refer to documentation.

Keras

Keras is a very popular Machine Learning library for Python. 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.

For more details refer to documentation.

PyTorch

PyTorch is a popular open-source Machine Learning library for Python based on Torch, which is an open-source Machine Learning library which is implemented in C with a wrapper in Lua. It has an extensive choice of tools and libraries that supports on Computer Vision, Natural Language Processing(NLP) and many more ML programs. It allows developers to perform computations on Tensors with GPU acceleration and also helps in creating computational graphs.

# Python program using PyTorch 
# for defining tensors fit a 
# two-layer network to random 
# data and calculating the loss 
  
import torch
  
  
dtype = torch.float
device = torch.device("cpu")
# device = torch.device("cuda:0") Uncomment this to run on GPU
  
# N is batch size; D_in is input dimension;
# H is hidden dimension; D_out is output dimension.
N, D_in, H, D_out = 64, 1000, 100, 10
  
# Create random input and output data
x = torch.randn(N, D_in, device = device, dtype = dtype)
y = torch.randn(N, D_out, device = device, dtype = dtype)
  
# Randomly initialize weights
w1 = torch.randn(D_in, H, device = device, dtype = dtype)
w2 = torch.randn(H, D_out, device = device, dtype = dtype)
  
learning_rate = 1e-6
for t in range(500):
    # Forward pass: compute predicted y
    h = x.mm(w1)
    h_relu = h.clamp(min = 0)
    y_pred = h_relu.mm(w2)
  
    # Compute and print loss
    loss = (y_pred - y).pow(2).sum().item()
    print(t, loss)
  
    # Backprop to compute gradients of w1 and w2 with respect to loss
    grad_y_pred = 2.0 * (y_pred - y)
    grad_w2 = h_relu.t().mm(grad_y_pred)
    grad_h_relu = grad_y_pred.mm(w2.t())
    grad_h = grad_h_relu.clone()
    grad_h[h < 0] = 0
    grad_w1 = x.t().mm(grad_h)
  
    # Update weights using gradient descent
    w1 -= learning_rate * grad_w1
    w2 -= learning_rate * grad_w2 

Output:

0 47168344.0
1 46385584.0
2 43153576.0
...
...
...
497 3.987660602433607e-05
498 3.945609932998195e-05
499 3.897604619851336e-05

For more details refer to documentation.

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 comes handy as it was developed specifically for data extraction and preparation. It provides high-level data structures and wide variety tools for data analysis. It provides many inbuilt methods for groping, combining and filtering data.

# Python program using Pandas for 
# arranging a given set of data 
# into a  table
  
# importing pandas as pd
import pandas as pd
  
data = {"country": ["Brazil", "Russia", "India", "China", "South Africa"],
       "capital": ["Brasilia", "Moscow", "New Dehli", "Beijing", "Pretoria"],
       "area": [8.516, 17.10, 3.286, 9.597, 1.221],
       "population": [200.4, 143.5, 1252, 1357, 52.98] }
  
data_table = pd.DataFrame(data)
print(data_table) 

Output:

For more details refer to Pandas.

Matplotlib

Matpoltlib is a very popular Python library for data visualization. Like Pandas, it is not directly related to Machine Learning. It particularly comes in handy when a programmer wants to visualize the patterns in the data. It is a 2D plotting library used for creating 2D graphs and plots. A module named pyplot makes it easy for programmers for 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, viz., histogram, error charts, bar chats, etc,

#  Python program using Matplotib 
# for forming a linear plot
  
# importing the necessary packages and modules
import matplotlib.pyplot as plt
import numpy as np
  
# Prepare the data
x = np.linspace(0, 10, 100)
  
# Plot the data
plt.plot(x, x, label ='linear')
  
# Add a legend
plt.legend()
  
# Show the plot
plt.show() 

Output:

For more details refer to documentation.

#machine-learning #python

What is GEEK

Buddha Community

Python Packages in SQL Server – Get Started with SQL Server Machine Learning Services

Introduction

When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.

Python Packages

When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,

• revoscalepy – This Microsoft Python package is used for remote compute contexts, streaming, parallel execution of rx functions for data import and transformation, modeling, visualization, and analysis.
• microsoftml – This is another Microsoft Python package which adds machine learning algorithms in Python.
• Anaconda 4.2 – Anaconda is an opensource Python package

#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services

Procedure To Become An Air Hostess/Cabin Crew

Minimum educational required – 10+2 passed in any stream from a recognized board.

The age limit is 18 to 25 years. It may differ from one airline to another!

 

Physical and Medical standards –

• Females must be 157 cm in height and males must be 170 cm in height (for males). This parameter may vary from one airline toward the next.
• The candidate's body weight should be proportional to his or her height.
• Candidates with blemish-free skin will have an advantage.
• Physical fitness is required of the candidate.
• Eyesight requirements: a minimum of 6/9 vision is required. Many airlines allow applicants to fix their vision to 20/20!
• There should be no history of mental disease in the candidate's past.
• The candidate should not have a significant cardiovascular condition.

You can become an air hostess if you meet certain criteria, such as a minimum educational level, an age limit, language ability, and physical characteristics.

As can be seen from the preceding information, a 10+2 pass is the minimal educational need for becoming an air hostess in India. So, if you have a 10+2 certificate from a recognized board, you are qualified to apply for an interview for air hostess positions!

You can still apply for this job if you have a higher qualification (such as a Bachelor's or Master's Degree).

So That I may recommend, joining Special Personality development courses, a learning gallery that offers aviation industry courses by AEROFLY INTERNATIONAL AVIATION ACADEMY in CHANDIGARH. They provide extra sessions included in the course and conduct the entire course in 6 months covering all topics at an affordable pricing structure. They pay particular attention to each and every aspirant and prepare them according to airline criteria. So be a part of it and give your aspirations So be a part of it and give your aspirations wings.

Read More:   Safety and Emergency Procedures of Aviation || Operations of Travel and Hospitality Management || Intellectual Language and Interview Training || Premiere Coaching For Retail and Mass Communication || Introductory Cosmetology and Tress Styling  ||  Aircraft Ground Personnel Competent Course

For more information:

Visit us at:     https://aerofly.co.in

Phone         :     wa.me//+919988887551 

Address:     Aerofly International Aviation Academy, SCO 68, 4th Floor, Sector 17-D,                            Chandigarh, Pin 160017 

Email:     info@aerofly.co.in

 

#air hostess institute in Delhi, 

#air hostess institute in Chandigarh, 

#air hostess institute near me,

#best air hostess institute in India,
#air hostess institute,

#best air hostess institute in Delhi, 

#air hostess institute in India, 

#best air hostess institute in India,

#air hostess training institute fees, 

#top 10 air hostess training institute in India, 

#government air hostess training institute in India, 

#best air hostess training institute in the world,

#air hostess training institute fees, 

#cabin crew course fees, 

#cabin crew course duration and fees, 

#best cabin crew training institute in Delhi, 

#cabin crew courses after 12th,

#best cabin crew training institute in Delhi, 

#cabin crew training institute in Delhi, 

#cabin crew training institute in India,

#cabin crew training institute near me,

#best cabin crew training institute in India,

#best cabin crew training institute in Delhi, 

#best cabin crew training institute in the world, 

#government cabin crew training institute

Top Machine Learning Projects in Python For Beginners [2021]

If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.

However, before you begin, make sure that you’re familiar with machine learning and its algorithm. If you haven’t worked on a project before, don’t worry because we have also shared a detailed tutorial on one project:

#artificial intelligence #machine learning #machine learning in python #machine learning projects #machine learning projects in python #python

Top Machine Learning Projects in Python For Beginners [2021] | upGrad blog

If you want to become a machine learning professional, you’d have to gain experience using its technologies. The best way to do so is by completing projects. That’s why in this article, we’re sharing multiple machine learning projects in Python so you can quickly start testing your skills and gain valuable experience.

However, before you begin, make sure that you’re familiar with machine learning and its algorithm. If you haven’t worked on a project before, don’t worry because we have also shared a detailed tutorial on one project:

The Iris Dataset: For the Beginners

The Iris dataset is easily one of the most popular machine learning projects in Python. It is relatively small, but its simplicity and compact size make it perfect for beginners. If you haven’t worked on any machine learning projects in Python, you should start with it. The Iris dataset is a collection of flower sepal and petal sizes of the flower Iris. It has three classes, with 50 instances in every one of them.

We’ve provided sample code on various places, but you should only use it to understand how it works. Implementing the code without understanding it would fail the premise of doing the project. So be sure to understand the code well before implementing it.

#artificial intelligence #machine learning #machine learning in python #machine learning projects #machine learning projects in python #python

5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

#machinelearningapps #machinelearningdevelopers #machinelearningexpert #machinelearningexperts #expertmachinelearningservices #topmachinelearningcompanies #machinelearningdevelopmentcompany

Visit Blog- https://www.xplace.com/article/8743

#machine learning companies #top machine learning companies #machine learning development company #expert machine learning services #machine learning experts #machine learning expert