Machine Learning, as the name suggests, — it is the science of computer programming with which they can study different kinds of data. A more general definition given by Arthur Samuel is “Machine Learning”; it is an area of learning that enables computers to learn without explicit programming. " They are commonly used to solve various types of life problems.
In the old days, people used to perform machine learning tasks by manually coding all the algorithms and mathematical and statistical formulas. This made the process time consuming, tedious, and inefficient. But these days it has become very easy and efficient compared to the days of old thanks to various Python libraries, frameworks and modules. Python is one of the most popular programming languages for this task today and has replaced many languages in the industry, one of the reasons is its vast collection of libraries. Python Libraries Used in Machine Learning:
NumPy — it is a very popular python library for handling large multidimensional arrays and matrices with a large set of high level math functions. This is very useful for fundamental scientific computing in machine learning. This is especially useful for linear algebra, Fourier transforms, and random number possibilities. High quality libraries like TensorFlow use NumPy to manipulate Tensors.

Output:
219 [29 67] [[19 22] [43 50]]
For more information refer to Numpy .
SciPy — it is a very popular library among machine learning enthusiasts as it contains various modules for optimization, linear algebra, integration and statistics. There is a difference between the SciPy library and the SciPy stack. SciPy is one of the main packages that make up the SciPy stack. SciPy is also very useful for image manipulation.

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For more information, see documentation .
Skikitlearn — one of the most popular ML libraries for classic ML algorithms. It is built on top of two main Python libraries, namely NumPy and SciPy. Scikitlearn supports most supervised and unsupervised learning algorithms. Scikitlearn can also be used for data analysis and data analysis, making it a great tool for beginners with ML.

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_sleafs = 1 0.0, presort = False, random_state = None, splitter = `best`) precision recall f1score 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 information, refer to documentation .
We all know machine learning is — it`s mostly mathematics and statistics. Theano — a popular Python library that is used to efficiently define, evaluate, and optimize mathematical expressions involving multidimensional arrays. It does this by optimizing CPU and GPU usage. It is widely used for unit testing and selftesting to detect and diagnose various types of errors. Theano — a very powerful library that has been used for a long time in largescale computationally intensive scientific projects, but it is simple and affordable enough to be used by individuals for their own projects.

Exit:
array ([[0.5, 0.73105858], [0.26894142, 0.11920292]])
For more information, refer to documentation .
# Python program using TensorFlow
# to multiply 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)
# Initialize session
sess
=
tf.Session ()
# Print result
print
(sess.run (result))
# Close session
sess.close ()
Exit:
[5 12 21 32]
For see documentation .
#
Keras & & quot; 8212; very popular machine learning library for Python. It is a highlevel neural network API capable of running on top of TensorFlow, CNTK, or Theano. It can run on both CPU and GPU. Keras makes it really for ML beginners to build and design a neural network. One of the best benefits of Keras is that it makes prototyping easy and fast.
For more details, refer to the documentation .
PyTorch — is a popular open source machine learning library for Python based on Torch, which is an open source machine learning library that is implemented in C with a Lua wrapper. It has a wide variety of tools and libraries that support Computer Vision, Natural Language Processing (NLP) and many other ML programs. This allows developers to perform GPUaccelerated tensor computations and also helps in the creation of computational graphs.

Logout :
0 47168344.0 1 46385584.0 2 43153576.0 ... ... ... 497 3.987660602433607e05 498 3.945609932998195e05 499 3.897604619851336e05
For see documentation for more details.
Pandas — it is a popular Python library for data analysis. This is not directly related to machine learning. As we know, the dataset must be prepared prior to training. This is where Pandas comes in handy as it was designed specifically for retrieving and preparing data. It provides highlevel data structures and a variety of data analysis tools. It provides many builtin methods for finding, merging and filtering data.
