8 Best Machine Learning Tools For Data Scientists

Machine learning (ML) has changed how humans interact with machines, technologies, and data. What started as a niche industry has now grown into a billion-dollar market. ML is already part of several industries, including healthcare, manufacturing, finance, retail, and entertainment. It has become crucial for this business to embrace machine learning to increase revenue, cut costs, and automate operations. 

Netflix saved $1 billion because of its ML algorithm for the combined effect of content recommendations and personalization. And 65% of companies planning to adopt machine learning say the technology will help them in decision-making. 

This article will feature the eight best machine learning tools for data scientists in any industry. 

1. Pandas

Pandas is an open-source data manipulation and analysis library for Python. It provides easy-to-use data structures and analysis tools for handling structured data, like tabular or time series data. With Pandas, data scientists can perform various tasks such as data cleaning, exploration, aggregation, and visualization.

Pros: 

• Built for Python:
• Excellent representation of data
• Less coding done, more work accomplished
• Efficient handling of massive data
• Extensive feature set
• The flexibility of data and easy customization

Cons:

• It has a complex syntax that is not always compatible with Python
• Steep learning curve
• Poor documentation
• Poor 3D matrix compatibility

2. NumPy 

NumPy stands for “Numerical Python.” It is a powerful open-source numerical computing library for Python. NumPy enables efficient mathematical operations on large, multi-dimensional arrays and supports various mathematical and logical operations.

Pros:

• Numpy arrays consume less memory space. It provides better runtime speed when compared with similar data structures in Python.
• Numpy supports some specific scientific functions, such as linear algebra. 
• Because of their similar functionalities, it is an excellent substitute for MATLAB, OCTAVE, etc.
• NumPy is perfect for data analysis.

Cons:

• Numpy is not suitable for large-scale distributed computing
• More efficient than some other libraries.
• Numpy does not support GPU acceleration, which can be a limitation for some applications.

3. Matplotlib

Matplotlib is an open-source data visualization library for Python. It provides comprehensive functions and tools for creating high-quality static, animated, and interactive visualizations. Matplotlib allows data scientists, researchers, and developers to represent their data in various forms, such as line plots, scatter plots, bar plots, histograms, pie charts, and more.

Pros:

• Versatile and accessible
• Customizable
• Good documentation 
• A universal tool that plugs into many backends

Cons:

• Steep learning curve
• Users need to learn Python programming before using the tool
• Users need to understand the syntax of Matplotlib, which is based on the software, MATLAB

4. Scikit Learn

Scikit-learn is a machine learning tool built on top of other scientific Python libraries like NumPy, SciPy, and Matplotlib. It provides a comprehensive suite of tools and algorithms for various machine learning tasks, including classification, regression, clustering, dimensionality reduction, and model selection.

Pros: 

• User-friendly and handy tool that can do multiple things such as predicting customer behavior, and creating neuroimage.
• It is easy and free to use.
• The contributor and the international online community update the Scikit Learn library.
• Scikit learn library provides the API documentation for the user who wants to integrate the algorithm with his platform.
• Extensive documentation 

Cons: 

• Scikit-learn is not the best choice for in-depth learning.

5. TensorFlow

TensorFlow is another machine learning library for Python. It works with NumPy, SciPy, and Matplotlib. It’s an open-source machine learning framework developed by Google. In addition, it facilitates the development and deployment of machine learning models, particularly neural networks.

Pros:

• Features various classification, regression, and clustering algorithms
• Models are trained and tested on different datasets than one used for preparing data using a train-test split
• Implements the non-neural net-based algorithm

Cons:

• TensorFlow’s frequent updates increase the overhead for users to install and bind it with the existing system.
• It uses homonyms with varying meanings, making it inconsistent with its usability.
• TensorFlow has low speed compared with other machine learning tools. 
• It offers little support for Windows Operating System users. 

6. PyTorch

One of the critical features of PyTorch is its dynamic computational graph, which allows users to define and modify models on-the-fly during runtime. This dynamic nature makes experimenting with different architectures, control flow, and algorithms easy, making PyTorch particularly popular among researchers and developers.

Pros:

• Cloud support
• Considered as NumPy extension of GPUs
• Easy to debug and understand

Cons: 

• It was released in 2016, so it’s new compared to others and has fewer users.
• Absence of monitoring and visualization tools 
• Smaller developer community is small compared to other frameworks.

7. NLTK

NLTK Stands for Natural Language Toolkit. It is used to work with human language data. NTLK’s libraries and programs for symbolic and statistical natural language processing for English written in Python.

Pros:

• NLTK fully supports the English language
• It consists of algorithms such as tokenizing, parts of speech, stemming, topic segmentation
• Efficient at analyzing large datasets

Cons:

• NLTK can be clunky and slow if you need to familiarize yourself with NLP.
• It’s more academic because of its origins in teaching and research
• It may not provide out-of-the-box thinking for some innovative web processes and startup needs.

8. Tableau

Tableau is a popular data visualization and business intelligence software that allows users to explore and analyze data through interactive visualizations, dashboards, and reports. It provides a user-friendly interface and drag-and-drop approach to create visually appealing and interactive visualizations without extensive coding or programming knowledge.

With Tableau, users can connect to various data sources, including spreadsheets, databases, cloud services, and big data platforms. The software supports multiple data types and offers powerful data blending and transformation capabilities, enabling users to clean, combine, and reshape data for analysis.

Pros:

• Provides beautiful dashboards and reports
• Automate Reporting
• Perform ETL(Explore, Transform, and Load) operations quickly

Cons: 

• Tableau is expensive.
• The steeper learning curve for advanced features
• Performance limitations in large datasets. 
• Limited statistical analysis and modeling
• Limited customization options

Conclusion

The machine learning tools discussed in this article offer valuable resources and capabilities for data scientists. Each agency brings unique strengths and features, empowering users to efficiently tackle complex data analysis, model training, and predictive tasks.