10 Best Development Tools For Machine Learning

Machine learning is the technology of possibilities. While the conversation regarding the use of artificial intelligence is more or less stuck between hopeful futurism and shameless fearmongering, machine learning is doing its job in a silent way, steadily establishing its worth through flashy case studies with astounding results.

These days, the application of machine learning algorithms into the business operation is a step towards gaining a competitive advantage and gathering business intelligence in a more efficient manner.

In this article, we will look at the 10 most prominent development tools for machine learning applications.

Best Machine Learning Development Tools

Table of Contents

1. Pandas

Pandas (aka Python Data Analysis Library) is a Python-based library for data manipulation, analysis, and statistical modeling. It is one of the go-to tools when comes to any significant data analysis task due to its flexibility and availability of the analytical tools.

Pandas is able to convert complex sets of data (CSV, JSO, TSV files or SQL database) into a comprehensive data frames of numerical tables (the ones with rows and columns) and time series.

Its most prominent use case is data munging aka data wrangling. One of its most powerful features involves reshaping of the data sets, its merging, and unification. The other great feature is filtering at specific criteria.

In addition to that, at the exploratory stage of data analysis, pandas can be very helpful at handling thrashy, missing or incomplete data.

2. Matplotlib

Matplotlib is a Python-based library for data visualization. It is a multi-purpose tool that can turn any dataset inside out and show what is inside in a manner that is accessible for those who don’t have data science skills.

As such, it is one of the major tools to explore data and open up its secrets for all to see.

Matplotlib is at its best when it comes to the following operations:

  • Exploratory data analysis
  • Scientific plotting.
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While the scope of use cases for the latter is limited in the business, the former is widely used to explore datasets and finds something interesting in the sea of data.

3. NumPy

When it comes to data analysis – you need some big guns to do real business. NumPy is one of those “guns” that can provide you with convincing arguments given the right kind of information.

NumPy is a core library for scientific computing and as such, it is a go-to tool when it comes to handling multi-dimensional data and conducting complex operations with it.

If you need numerical analysis, matrix computations and linear algebra stuff – that’s your tool of choice.

NumPy’s greatest asset is accessibility – it makes handling complex sets of data seem easy and manageable while extracting all sorts of goods out of it.

4. SciPy

If NumPy is a tool for ground level operation, SciPy is a tool for the big picture rumblings. It provides you with the high-performance array of multidimensional tools to tackle data in every way imaginable and then some.

Think about it as Watchmen’ Doctor Manhattan who could deconstruct any object at his force of will. That’s the scope of possibilities SciPy is giving you if you are using it right.

In essence, SciPy is a bigger, better and more expansive version of NumPy designed for more intricate and diverse operations. It is the go-to option if you need to handle complex data with many moving parts.

5. Scikit-learn: easy-to-use machine learning framework for numerous industries

Scikit-learn is a machine learning framework based in Python. It takes SciPy, NumPy, and Matplotlib and turns them into one big Machine Learning leviathan.

In essence, sci-kit-learn is a set of various algorithms for different machine learning operation all wrapped in one. You get the Gentlemen’s package of classification, regression, clustering coupled with support vector machines, random forests, gradient boosting, k-means and DBSCAN. In other words, the whole bunch of goodies.

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The greatest thing about SciKit-Learn is that it is easy to handle. The API is clear-cut and flexible and there are many variations for all sorts of operations. It is also capable of modifications depending on the specific use case.

6. NLTK: Python-based human language data processing platform

In one way or another, Natural Language Processing is involved in the majority of the data analysis operations. You need it to comprehend information beyond the surface level and extract the gist of it.

NLTK is one of the major tools to develop viable NLP models. Based in Python, NLTK is a text processing juggernaut capable of handling the entirety of Natural Language Processing operation, building NLP models and analyzing the corpora.

For example, you can break down the text to the particles, arrange the corpora, find interesting stuff in it, train NLP model that can generate customer support yodeling in a jive slang and analyze user behavior on a subject of customer satisfaction.

It is easy to handle and flexible enough to bend into the shape you need it to be.

7. TensorFlow: a flexible framework for large-scale machine learning

If there is an overarching network that can handle all sorts of machine learning operations – from the basic one-two’s to complex neural networks – it is probably TensorFlow.

Given the fact, that TensorFlow is a Google product – it is extremely versatile and easy to use. The entire workflow is based on flow graphs with nodes and edges which makes it easy to understand where is what and what is how.

This makes it good both for research purposes and recurring tasks.

The core advantage of TensorFlow is that it makes the process of acquiring data and training model less tangled and as a result, makes it easier to build and refine predictive models.

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8. TensorBoard: a good tool for model training visualization

TensorBoard is a collection of tools for visualization of various elements of machine learning operation in TensorFlow. Think of it as a helping hand.

What TensorBoard does is interpret TensorFlow event files on the go and makes them eligible for more intricate analysis. This visualization helps to tweak the model on the go or analyze the model’s behavior with certain kinds of data or sources.

These features are making TensorBoard a go-to tool for model performance evaluations and model training monitoring.

9. PyTorch: easy to use tool for research

PyTorch is a framework for deep neural networks. In a way, it is an overarching construction for different ML-related Python libraries.

The main asset of PyTorch is its speed and flexibility. With its help, you can construct a machine learning model without breaking a sweat.

The other good thing about PyTorch is that you can fix and tweak the model on the go. The debug mode is one of the most comfortable out there.

10. Keras: lightweight, easy-to-use library for fast prototyping

Keras is a Python-based library of deep learning tools compatible with TensorFlow. Its purpose is to run prototyping experiments fast and loose.

The main asset of Keras is its speed. A combination of TensorFlow and Keras is capable of implementing the neural network in a twice shorter timeframe.

The other good thing about Keras is its simplicity. The interface is easy to follow and the way the dashboard is laid out makes the work effortless.


These are the most prominent tools for the development of machine learning applications. With its help, you can create the system as versatile and efficient as you need.

About Mike Stanley

Mike Stanley is a dedicated and passionate writer with a keen interest in the world of celebrity finance. With a background in journalism and economics, Mike has found his niche in researching and documenting the net worth of some of the most influential figures in the entertainment industry. His work is characterized by meticulous research and a commitment to providing accurate, up-to-date financial profiles.