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Top 12 Machine Learning Tools in 2024 To Get Your Hands On

Last updated:
10th Jan, 2023
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Top 12 Machine Learning Tools in 2024 To Get Your Hands On

Artificial Intelligence and Machine Learning are the hottest trends in the tech world today. With AI and ML applications dominating almost every aspect of the modern-day world – from something as simple as a Recommendation Engine to the self-driving car, AI and ML are omnipresent. 

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As more and more companies are enthusiastically exploring the boundaries of these emerging technologies, it is creating substantial employment opportunities for aspirants. Globally, both giant corporations and small startups are harnessing the benefits of these disruptive technologies, thereby opening up new and exciting vistas for employment.

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However, to be able to land a job in AI or ML, you must first be well-versed with Machine Learning tools. Machine Learning tools and Machine Learning software allow web/mobile app developers to create efficient and functional ML algorithms. These algorithms can, in turn, be used for various purposes – build recommendation engines, accurately predict search patterns, spam filtering, fraud detection, and much more.

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We’ve compiled a list of the top twelve tools for Machine Learning that are excellent both for beginners and experienced professionals!

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Top Machine Learning Tools in 2019

  1. TensorFlow

Initially developed by Google, TensorFlow is an open-source Machine Learning software library for numerical computation using data flow graphs. It has a comprehensive and flexible suite of tools, libraries, and resources that allow you to build, train, and deploy ML applications without any hassle. 


TensorFlow is an excellent Machine Learning tool for Deep Learning systems and neural nets. Another great feature of TensorFlow is that it can run on GPUs and CPUs as well as on mobile computing platforms.

  1. Amazon Machine Learning (AML)

Amazon Machine Learning tool is a cloud-based, Machine Learning software application. It is primarily used by developers all over the world to build machine learning models and for generating predictions. The best part about it is that it can be used by web/mobile app developers of all skill levels. 

AML supports three types of ML models, including regression, multi-class classification, and binary classification. It can integrate data from multiple sources like Redshift, Amazon S3, and RDS. Also, it allows you to create data source objects from the MySQL database.

  1. Auto-WEKA

Auto-WEKA is a data-mining tool designed to perform combined algorithm selection and hyper-parameter optimization over the classification and regression algorithms that WEKA implements. 

So, when a dataset is fed in WEKA, it explores the hyperparameter settings for several algorithms and recommends the most preferred one to the user – the one that offers a reliable generalization performance. The tool uses a fully-automated approach and by leveraging the recent innovations in Bayesian optimization.

  1. BigML

When talking about Machine Learning tools, we cannot afford to miss out on BigML. It is a comprehensive ML platform that offers a host of ML algorithms to solve complex, real-world problems through a unified and integrated framework. Explicitly designed for Machine Learning, BigML comes with an extensive range of features well-integrated within a convenient Web UI. It allows you to load your dataset, build and share your ML models, train and evaluate your models, and generate new predictions, either singularly or in a batch.

BigML encompasses various useful ML aspects, including classification, regression, time-series forecasting, cluster analysis, anomaly detection, topic modeling, etc., all of which apply to a wide range of predictive applications.

  1. Google Cloud AutoML

Google Cloud AutoML is a collection of ML products that allows developers with limited ML expertise to train high-quality models according to the unique business requirements. It is based on Google’s state-of-the-art transfer learning and neural architecture search technology. 

The tool offers a neat and simple GUI for training, evaluating, enhancing, and deploying models based on a given dataset. You can generate a prediction on your trained model through the existing Vision API by adding a custom model. You can store the data in the cloud storage securely.

  1. MLflow

MLflow is an open-source platform designed to manage the complete ML lifecycle (including experimentation, reproducibility, and deployment of ML models). It has three core components – Tracking, Projects, and Models, each performing unique functions.

MLflow has numerous built-in integrations like TensorFlow, PyTorch Keras, Spark,, Python, Java, R, Kubernetes, Docker, Azure ML, and Google Cloud. These can be very handy for building ML models to suit specific needs. 

  1. Scikit-Learn

Scikit-Learn is one of the most useful libraries for ML in Python. Built on NumPy, SciPy, and Matplotlib, this Python-based library contains an array of efficient tools for Machine Learning and statistical modeling. These include classification, regression, clustering and dimensionality reduction, model selection, and pre-processing.

Since it is an open-source library with an active community, it is always being improved. And rest assured, if you ever get stuck in a dead-end problem, you can always reach out to the Scikit-Learn community for help. 

  1. Apache Mahout

Apache Mahout is an open-source, distributed linear algebra framework and mathematically expressive Scala DSL designed for developing scalable ML applications. It is mainly used by Data Scientists, Mathematicians, and Statisticians for speedy implementation of ML algorithms.

Apart from offering an extensible platform for building scalable algorithms, Apache Mahout also includes matrix and vector libraries. It can run on top of Apache Hadoop using the MapReduce paradigm. 

  1. IBM Watson Studio

IBM Watson Studio is a platform that allows you to build and train scalable ML models with faster optimization. It offers you with all the tools required to solve business problems through a collaborative data experience. There are tools for data analysis and visualization, for cleansing and shaping the data, for data ingestion, and of course, for creating and training ML models. 

IBM Watson Studio accelerates the Machine Learning and Deep Learning workflows essential for the integration of AI into your business infrastructure, thereby fostering innovation. 

  1. Microsoft Azure ML Studio

Microsoft Azure Machine Learning Studio is a fully-managed cloud service that allows you to build, deploy, and share predictive analytics solutions in a hassle-free manner. It is a collaborative, drag-and-drop tool that enables you to build, test, and deploy predictive analytics solutions on your data. 

Azure ML Studio publishes models as web services to facilitate easy consumption by custom apps or BI tools. It offers an interactive, visual workspace for developing, testing, and iterating on a predictive analysis model. The tool demands no programming – it visually connects the datasets and modules to build your predictive analysis model.

 11. Apache Spark MLib

Apache Spark MLib is a scalable ML library that runs on Apache Mesos, Hadoop, Kubernetes, either standalone or in the cloud. It consists of all the standard ML algorithms and utilities such as classification, regression, clustering, collaborative filtering, dimensionality reduction. The main aim of this tool is to make practical Machine Learning scalable and easy. 

Spark MLlib offers various tools like ML algorithms, Featurization (for feature extraction, transformation, dimensionality reduction, and selection), Pipelines (for constructing, evaluating, and tuning ML pipelines), Persistence (for saving and loading algorithms, models, and pipelines), and Utilities (for linear algebra, statistics, data handling). 

  1. Accord.NET

Accord.NET is an ML framework for scientific computing in .NET. It comprises of multiple image and audio processing libraries written in C# programming language. The libraries are available both in source code and can be accessed through executable installers and NuGet packages. The core areas of focus are statistics, machine learning, artificial neural networks, numerical linear algebra, numerical optimization, signal and image processing, and support libraries (for example, graph plotting and visualization). 

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The Accord.NET framework includes the Accord.Statistics, Accord.Math, and Accord.MachineLearning. You can create and test new ML algorithms without diving too deep in code. Also, it comes with a suite of sample applications to help you write your applications quickly.

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When it comes to Data Science (AI, ML, Deep Learning), tools allow you to explore the depths of Data Science domains, experiment with them, and innovate fully-functional AI/ML solutions. Different tools are designed for different needs. So, the choice of Machine Learning tools will largely depend on the project at hand, the expected outcome, and, sometimes, your level of expertise.

However, the goal is to keep learning and acquiring new skills. So, don’t be afraid to play with new ML tools and software – who knows you might be able to create something fantastic someday! 

If you’re interested in working on things like training an agent to play tic tac toe, train a chatbot, etc. you should check our Master of Science in Machine Learning & AI.



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Frequently Asked Questions (FAQs)

1Are there any disadvantages to artificial intelligence?

One of artificial intelligence's most critical flaws is that it replaces humans with machines for a range of basic tasks. As the need for human involvement has lessened, many professional opportunities have vanished. Another of AI's key flaws is that it cannot learn to think creatively. AI can learn over time by utilizing pre-loaded data and previous experiences, but it can't be creative in its approach. It takes a great deal of expertise to create a machine that can mimic human intellect. It takes a lot of time and effort, and as a result, it may be rather costly.

2Is getting a data analyst job easy?

It is not a tedious task to gain the skills required to become a data analyst. The job opportunities of data analysts are enormous. Although entering the field without years of extensive study can be hard, you can gain the skills needed to work as a data analyst in a few months, even if you have no technical experience or do not know about coding concepts. As a result, it is not difficult to get a job as a data analyst.

3What are the limitations of using TensorFlow?

Although TensorFlow reduces the length of the code, it also makes it more complicated. TensorFlow is slower than its competitors and also less user-friendly. When it comes to delivering symbolic loops for indefinite sequences, TensorFlow is behind the times. TensorFlow only supports NVIDIA GPUs and Python GPU programming. It doesn't have any other means of support. It also does not offer many benefits to users of the Windows Operating System.

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