Best Python libraries for your machine learning initiatives.


Best Python libraries for your Machine Learning initiatives.

  • 19th October 2022

“Python is the most persuasive choice when one needs to explore the facets in terms of Data Science, Full Stack Development, Artificial Intelligence, Machine Learning, etc.

Machine learning aids the machine to learn from previous experience and take apt decisions accordingly. Machine learning and AI go conjointly. Machine Learning has vast applications ranging from classification and predictions to image processing. IoT the most popular buzzword resonating nowadays is harnessing the power of Machine learning to perform atomization in various day-to-day tasks. Python provides robust Machine Learning and Data Science libraries that are portable, easy to implement, and available for free.

1. NumPy

It is the most useful python library that can cater to your needs of scientific computing. It provides a faster and simple way for Vector operations, Multidimensional arrays, Algebraic expressions, and statistical functions.

Pros:   Space efficient and Faster than Python when comes to array manipulation
Cons:   Requires contiguous memory allocation leading to data shifting problem
Alternatives:   TensorFlow

2. TensorFlow

If you want to deal with real-life applications that involve pattern recognition like signature identification, and handwriting recognition, TensorFlow is a good choice. It helps to model the nonlinear and complex relationships of the real world.

Pros:   Wide range of device support, provides an end-to-end development platform for Machine Learning
Cons:   Slow
Alternatives:   PyTorch (Except for visualization tool)

3. Theano

It’s all about Data. If the project design entails an application that uses data, responds to your queries, and makes the data available to you according to your priority, Deep learning can serve the purpose. Theano works great for Deep learning algorithms.

Pros:   Faster on Graphics Processing Units
Cons:   Difficult to Debug, Comparatively slow while dealing with complex models,
Alternatives:   TensorFlow, Keras

4. Natural Language Toolkit (NLTK)

Natural Language processing is the ability of machines to comprehend human language. There are many scenarios like consumer reviews that need Natural Language Processing. Python Natural Language Toolkit is a powerful library that works great for Natural language processing.

Pros:   Great in evaluating public opinions
Cons:   Slow, Semantic structure is not analyzed correctly
Alternatives:   Apache OpenNLP library

5. Keras

It manifests a word-based neural language model. Language models form the base of many natural language tasks. It helps to extract the most suitable meaning from the context in case of even patchy and uneven text. It is also useful for text translation and automated speech recognition.

Pros:   Ease of use, Equipped with pre-trained models, Extensible
Cons:   Doesn’t support dynamic chart creation
Alternatives:   TensorFlow

6. Scikit-Learn

Classification, regression, and clustering are the most common tasks in Machine learning. Classification plays role in the categorization of data. Regression helps identify the relationship between variable/variables. Clustering groups the items according to similarity. Scikit-Learn provides a library to perform these tasks seamlessly.

Pros:   Versatility in recognizing the user actions
Cons:   More focused on modeling than the data loading process
Alternatives:   Pecan

7. Pandas

Pandas is a Python library used for working with data sets. It has functions for analyzing, cleaning, exploring, and manipulating data.

Pros:   Excellent Data Representation, Efficacy in the handling of enormous data
Cons:   Poor 3D matrix compatibility, Poor documentation.
Alternatives:   PySpark, Dask

8. OpenCV

OpenCV is a Python library that embraces the computer-vision technology. Computer vision includes understanding and analyzing digital images using the computer and processing the images. It also extracts relevant data after analyzing the image.

Pros:   Portable, Great for facial recognition, Less RAM requirement
Cons:   Less user-friendly for the programmers as compared to Matlab, Difficulty in integration with other libraries
Alternatives:   Webrtc

9. PyTorch

Exclusive library fully equipped with a framework that provides deep learning models. The applications designed with PyTorch augment computer vision and natural language processing.

Pros:   Optimized GPU support, Ease of use, Data parallelism, Deep learning, Computer vision
Cons:   Absence of monitoring and visualization tools
Alternatives:   TensorFlow

10. Seaborn

Seaborn facilitates in designing statistical graphics. It provides a data visualization library. It is integrated with matplotlib and pandas. The most significant feature of Seaborn is Data Visualization which helps in the exploration and understanding of data.

Pros:   Aids quick and aesthetic visualization
Cons:   Less customizable
Alternatives:   Matplotlib, Plotly

All the above-mentioned Machine learning libraries in Python provide a comprehensive tool bundle to comprehend data in textual or image form, analyze it, process it, make decisions, and present them in a striking and more comprehensible graphical format.