Machine Learning

Best Python libraries for Machine Learning. The Complete Machine Learning Developer Course 2022 [Videos].

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:  

  • Numpy
  • Scipy
  • Scikit-learn
  • Theano
  • TensorFlow
  • Keras
  • PyTorch
  • Pandas
  • Matplotlib


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), " ")
# Matrix and Vector product
print(np.dot(x, v), " ")
# Matrix and matrix product
print(np.dot(x, y))



[29 67] 

[[19 22]
 [43 50]]

For more details refer to Numpy

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 imwatch, imsave, imresize
# Read a JPEG image into a numpy array
img = imwatch("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)

Original image: 

Tinted image: 

Resized tinted image: 

For more details refer to documentation

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 Classifier
# 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)
# 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))


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,
              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

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]])


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

For more details refer to documentation

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. 

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