10 Python Libraries Every AI/ML Developer Should Know

26th Feb 2023
5 min read
Tags:
ai,
python,
machine-learning,
ml,
python-beginner

Updated on 16th Aug 2024
See changes ↗

Requests - Requests is a library used for making HTTP requests in Python. It provides support for handling HTTP methods like GET, POST, and DELETE, and includes features like request headers and authentication.

Example code snippet:

# Handwritten
import requests

response = requests.get('https://jsonplaceholder.typicode.com/posts')
print(response.json())

BeautifulSoup – BeautifulSoup is a library used for web scraping in Python. It provides support for parsing HTML and XML documents and includes features like navigating the document tree and extracting data.

import requests
from bs4 import BeautifulSoup

url = 'https://en.wikipedia.org/wiki/Python_(programming_language)'

response = requests.get(url)
soup = BeautifulSoup(response.content, 'html.parser')

title = soup.title.string

print(title)

NumPy – NumPy is a library used for scientific computing with Python. It provides support for large, multi-dimensional arrays and matrices, and includes a large collection of mathematical functions to operate on these arrays.

Example code snippet:
```
import numpy as np

a = np.array([1, 2, 3])
b = np.array([4, 5, 6])

c = a + b

print(c)

# Output
[5 7 9]
```

Pandas – Pandas is a library used for data manipulation and analysis. It provides support for handling tabular data, including reading and writing various data formats, filtering, sorting, grouping data, and much more.

Example code snippet:

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie', 'David'],
        'Age': [25, 32, 18, 47],
        'Gender': ['F', 'M', 'M', 'M']}

df = pd.DataFrame(data)

print(df)

# Output
       Name  Age Gender
0     Alice   25      F
1       Bob   32      M
2   Charlie   18      M
3     David   47      M

Matplotlib – Matplotlib is a library used for creating data visualizations in Python. It provides support for creating various types of plots, including line, bar, scatter, and more.

Example code snippet:
```
import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 4, 6, 8, 10]

plt.plot(x, y)
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Line Plot')

plt.show()
```

Scikit-learn – Scikit-learn is a library used for machine learning in Python. It provides support for various machine learning algorithms, including classification, regression, and clustering.

Example code snippet:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier

iris = load_iris()

X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2)

knn = KNeighborsClassifier(n_neighbors=3)
knn.fit(X_train, y_train)

print(knn.score(X_test, y_test))
0.9333333333333333

Keras – Keras is a high-level neural networks API written in Python. It provides support for building and training deep learning models, with a focus on enabling fast experimentation.

Example code snippet:

import keras
from keras.models import Sequential
from keras.layers import Dense

model = Sequential()
model.add(Dense(10, input_dim=4, activation='relu'))
model.add(Dense(3, activation='softmax'))

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

print(model.summary())

Output:

Model: "sequential"
________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 dense (Dense)               (None, 10)                50

 dense_1 (Dense)             (None, 3)                 33

=================================================================
Total params: 83
Trainable params: 83
Non-trainable params: 0
_________________________________________________________________
None

TensorFlow – TensorFlow is a library used for machine learning and deep learning in Python. It provides support for building and training various types of neural networks, including convolutional neural networks and recurrent neural networks.

Example code snippet:

import tensorflow as tf

model = tf.keras.Sequential([
    tf.keras.layers.Dense(10, input_shape=(4,), activation='relu'),
    tf.keras.layers.Dense(3, activation='softmax')
])

model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

print(model.summary())

Output:

Model: "sequential_1"
_________________________________________________________________
 Layer (type)                Output Shape              Param #
=================================================================
 dense_2 (Dense)             (None, 10)                50

 dense_3 (Dense)             (None, 3)                 33

=================================================================
Total params: 83
Trainable params: 83
Non-trainable params: 0
_________________________________________________________________

PyTorch – PyTorch is a library used for machine learning and deep learning in Python. It provides support for building and training various types of neural networks, and includes features like automatic differentiation and GPU acceleration.

Example code snippet that demonstrates the use of PyTorch to create and train a simple neural network:

import torch
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 5 * 5, 120)  # 5*5 from image dimension
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square, you can specify with a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = torch.flatten(x, 1) # flatten all dimensions except the batch dimension
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x


net = Net()
print(net)

Output:

Net(
  (conv1): Conv2d(1, 6, kernel_size=(5, 5), stride=(1, 1))
  (conv2): Conv2d(6, 16, kernel_size=(5, 5), stride=(1, 1))
  (fc1): Linear(in_features=400, out_features=120, bias=True)
  (fc2): Linear(in_features=120, out_features=84, bias=True)
  (fc3): Linear(in_features=84, out_features=10, bias=True)
)

NLTK – NLTK is a library used for natural language processing in Python. It provides support for various tasks, including text classification, sentiment analysis, and part-of-speech tagging.

Example code snippet:

import nltk

nltk.download('punkt')
text = "This is a sample sentence. It contains multiple sentences."
sentences = nltk.sent_tokenize(text)

print(sentences)

Output:

['This is a sample sentence.', 'It contains multiple sentences.']

Conclusion

In conclusion, Python has a vast collection of libraries that make it an incredibly versatile language for a wide range of applications, from web development to machine learning. In this blog post, we’ve highlighted 10 Python libraries that every AI/ML developer should know, each with its unique features and use cases.

Of course, this list is by no means exhaustive, and there are many other libraries worth exploring. But with the knowledge gained from these libraries, developers can continue to expand their skills and build innovative solutions to real-world problems.