Top 10 Python Libraries used for Machine Learning

Machine Learning (ML) and Artificial Intelligence (AI) are two fields in which Python is a popular and adaptable programming language. One of the key reasons behind the popularity of Python for ML is its vast collection of libraries.

The top 10 Python libraries used for machine learning will be covered in this article.

1. Pandas

Pandas takes the crown for one of the most versatile libraries in Python, which provides tools for data manipulation and analysis. Built on top of NumPy, It offers data structures like DataFrame and Series for effective data handling. Pandas can manage missing data, data alignment, and data reshaping. Data cleaning, data exploration, and data visualisation are all done with it.

Dataframes are commonly used for handling data in Machine Learning. Pandas provides various DataFrame functions, some of these are listed below:

• Head: The head() function displays a specified portion of rows from the top of the DataFrame.

Pandas head() function example

• Describe: The describe() method returns the description of the data in the DataFrame.

Pandas describe() function example

• Info: The info function displays a summary of the dataframe.

Pandas info() function example

• Drop: The drop() method removes the specified row or column. By specifying the column axis ( axis=’columns’ ), the drop() method removes the specified column.

For a full list of Pandas functions, refer to the documentation.

2. NumPy

NumPy stands for Numerical Processing, and supports multi-dimensional arrays and matrices. It is a core library for scientific computing with Python and is extensively used for data manipulation and pre-processing. NumPy provides mathematical functions for linear algebra, Fourier transforms, and random number generation.

Numpy provides various functions, some of these are listed below:

• Sort: The sort() method is used to sort the elements of NumPy array in an ordered sequence.

Numpy sort() function example

• Random.randint: The random.randint() method is used to get a random integer number from the given range. The first parameter stands for the starting range, the second parameter starts for the ending range, and the third parameter stands for the number of values to be generated.

Numpy random.randint() example

Min/Max: max() method is used to find out the maximum value from the array elements or the elements of an array, while min() function is used to find out the minimum value from the array elements or an array.

Numpy max() and min() example

For a full list of NumPy functions, refer to the documentation.

3. Matplotlib

Matplotlib is used for plotting high quality Figures in Python, i.e. Data Visualization. Line plots, scatter plots, histograms, and bar plots are just a few of the many visualisation options it offers. Plots and animations may be made interactively with Matplotlib.

Matplotlib provides various types of graphs, some of these are listed below:

• Line: The plot function is used to display a line plot, which is a way to display data along a number line.

Matplotlib plot() function example

• Scatter: The scatter function is used to display a scatter plot, which is a diagram where each value in the data set is represented by a dot.

Matplotlib scatter() function example

• Bar: The bar function is used to display a bar plot, which are a type of data visualization used to represent data in the form of rectangular bars, in a categorical way.

Matplotlib bar() function example

For a full list of Matplotlib plots, refer to the documentation.

4. Seaborn

Seaborn is another python package for visualization data in a more creative and efficient way. It is developed on top of Matplotlib and intended to make the process of making attractive and useful charts as simple as possible. For data exploration, Seaborn frequently works in combination with other libraries like Pandas and NumPy, and is excellent for producing statistical charts.

Seaborn provides various types of graphs, some of these are listed below:

• Displot: The distplot method is used to display a distribution plot, which displays the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables.

Seaborn distplot() function example

• Countplot: The countplot() method is used to display a count plot, which can be thought of as a histogram across a categorical, instead of quantitative variable.

Seaborn countplot() function example

• Barplot: The barplot() method is used to display a bar plot, which plots the means of each category on the x axis.

Seaborn barplot() function example

For more detail on Seaborn, visit this medium article. Credits to Rising Odegua for the graphs above.

For a full list of Seaborn plots, refer to the documentation.

5. Sci-kit Learn

Scikit-learn is a machine learning library for Python that provides tools for data mining and analysis. It contains a range of methods for supervised and unsupervised learning, including regression, classification, clustering, and dimensionality reduction. Scikit-learn additionally provides tools for model selection and assessment.

Scikit-learn provides various types of model, some of these are displayed below:

• Linear Regression: sklearn.linear_model is used to develop a Linear Regression model, which is used to predict the value of a variable based on the value of another variable.

Dataset for Developing a Predictive Model

Splitting Training and Testing Set

Setting up Linear Regression Model

Prediction and Model Metrics

• Random Forest Regression: sklearn.ensemble is used to develop a Random Forest Regression Model, which is a supervised learning algorithm that uses ensemble learning method for regression.

Setting up Random Forest Regression Model

Prediction and Model Metrics

• Decision Tree Regression: sklearn.tree is used to develop a Decision tree, which builds regression or classification models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed.

Setting up Decision Tree Regression Model

Prediction and Model Metrics

For a full list of sci-kit learn models, refer to the documentation.

6. TensorFlow

TensorFlow is a popular open-source library for deep learning that provides tools for building and training neural networks. For speech recognition, natural language processing, and image recognition, it is widely used in both industry and academia. For the creation of models, TensorFlow offers a variety of high-level APIs, including Keras and Estimators.

Setting up a TensorFlow model

TensorBoard: A tool called Tensorboard enables the viewing of any neural network information, including the training parameters (loss, accuracy, and weights), pictures, and even the graph. Doing so can help you debug and improve the model by helping you understand how the tensors move across the graph.

Visualizing Graphs

For more detail on TensorBoard, visit this medium article. Credits to Bruno Eidi Nishimoto for the visual above.

For a full list of TensorFlow functions, refer to the documentation.

7. PyTorch

PyTorch is A deep learning package for Python, and offers resources for creating and refining neural networks. It is known for its ease of use and flexibility. A variety of high-level APIs, like nn.Module and autograd, are available in PyTorch for creating models. It is widely used in research and organisation for reinforcement learning, natural language processing, and computer vision. It has helped in accelerating the research into deep learning models by making them more affordable and computationally efficient.

One leading difference between PyTorch and TensorFlow is that PyTorch supports dynamic dataflow graphs whereas TensorFlow is limited to static graphs. Compared to TensorFlow, PyTorch is easier to learn and implement since TensorFlow needs heavy code work.

PyTorch provides various types of functions, some of these are displayed below:

• Arange: The torch. arange() function will return the 1 dimensional tensor which will be of size (end — start / stop) with values from the interval [start, end] taken with common difference step beginning from start.

Arange() function example

• Reshape: The .reshape returns a tensor with the same data and number of elements as input , but with the specified shape. When possible, the returned tensor will be a view of input . Otherwise, it will be a copy.

Reshape function example

• Transpose: The .t function takes transpose of tensor matrix ( i.e interchanging of rows and column), and returns a tensor that is a transposed version of the input.

Transpose() function example

For a full list of PyTorch functions, refer to the documentation.

8. OpenCV

OpenCV (Open Source Computer Vision) is a popular open-source computer vision and machine learning software library that is used to develop real-time computer vision applications.

OpenCV provides various types of functions, some of these are displayed below:

• imread: imread function is used to load an image in the Python program from the specified file. It returns a numpy. ndarray (NumPy N-dimensional array) after loading the image successfully.

imread() function example

• imshow: cv2.imshow is used to display a window, where window_name is the title of the window in which the image numpy. ndarray will be shown.

imshow function example

imshow() image display example

• imwrite: imwrite() returns a boolean value. True if the image is successfully written and False if the image is not written successfully to the local path specified.

imwrite() image saved example

For a full list of OpenCV functions, refer to the documentation.

9. NLTK

NLTK (Natural Language Toolkit) is a library for natural language processing in Python. It is well suited for applications like text categorization, sentiment analysis, and language translation and is frequently employed when working with text data. Tokenization, stemming, and lemmatization are just a few of the many text data processing functions available in the NLTK library.

NLTK provides various types of functions, some of these are displayed below:

• word_tokenize: .word_tokenize method is used to split a given sentence into tokens using the NLTK library.

word_tokenize function example

• stopwords: Stop words are words that are so common they are basically ignored by typical tokenizers. By default, NLTK (Natural Language Toolkit) includes a list of 40 stop words, including: “a”, “an”, “the”, “of”, “in”, etc.

stopwords function example

• pos_tag: pos_tag method is for the practice of marking up the words in text format for a specific segment of a speech context, known as POS Tagging (Parts of Speech Tagging). It is in charge of interpreting a language’s text and associating each word with a specific token.

pos_tag function example

For a full list of NLTK functions, refer to the documentation.

10. 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.

SciPy provides various types of functions, some of these are displayed below:

• Exponential Functions: The special.expn method is used to calculate the exponential function values in python.

Scipy Exponential Function Example

• Trignometric Functions: .sindg, .cosdg or .tandg methods are used for calculating Trignometric Functions.

Trigometric Functions Example

• Linear Algebra: .linalg function is used to work with linear algebra in Python, you can count on SciPy, which is an open-source Python library used for scientific computing, including several modules for common tasks in science and engineering.

linalg function example

For a full list of SciPy functions, refer to the documentation.

So. these are the top 10 most useful libraries for ML and AI using Python.

That is it, signing off!

Note: Click here for the code on my GitHub and kindly visit my Linkedin profile.

Credits to this medium article for the inspiration.

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