Top 20 Popular Machine Learning Tools for 2025

Machine learning sounds quite scary to many people, but at its core, it runs on tools that make everything possible, from data cleaning to building real-world AI applications. These tools help in AI development, helping engineers, researchers, and even beginners turn raw data into working models.

In 2025, the use of ML tools is expanding through various industries. Finance firms use them for fraud detection, hospitals rely on them for diagnosis, and retailers optimize customer experiences with them. Basically, wherever there’s data, machine learning tools are making an impact.

And the growth is massive. The ML tools market is projected to cross $60B by 2027, driven by a shift toward cloud-based platforms and AI-powered automation. That means learning these tools isn’t just useful, it’s essential if you want to stay ahead in the AI and data-driven world.

What are Machine Learning Tools?

Think of ML tools as the software, frameworks, and platforms that simplify the process of creating AI models.

• Cloud-based & open-source: Cloud platforms like AWS, GCP, Azure provide scalability, while open-source tools like TensorFlow, Scikit-learn are widely used for flexibility and cost-effectiveness.
• Low-code/no-code & coding-heavy: Tools like RapidMiner or Google AutoML make it easy for beginners, while coding frameworks give more control to engineers and researchers.
• Key use cases: From data preparation and model training to deployment and monitoring, ML tools cover the full machine learning pipeline.

For anyone looking to step into AI or data science, getting familiar with these tools is one of the smartest starting points.

Categories of Machine Learning Tools

There are various machine learning tools, and they certainly serve different purposes. Some help with model building, others with deployment or data visualization. To make things easier, here’s a list of ML tools grouped by their main functions.

ML Frameworks & Libraries

• TensorFlow: Widely used for deep learning projects.
• PyTorch: Favored by researchers for its flexibility.
• Keras: Beginner-friendly interface for neural networks.
• Scikit-learn: Great for traditional ML tasks like regression and classification.

Cloud Platforms & End-to-End Solutions

• AWS SageMaker: Train and deploy models directly on AWS.
• Microsoft Azure ML: Integrates well with Microsoft’s ecosystem.
• Google Vertex AI: Streamlines data-to-deployment workflows.
• IBM Watson: Known for enterprise-ready AI solutions.

No-Code / Low-Code ML Tools

• BigML: Simple interface for predictive modeling.
• KNIME: Drag-and-drop style workflows.
• RapidMiner: Popular in academic and research settings.
• Google AutoML: Automates model building with minimal coding.

Data Preparation & Visualization Tools

• Project Jupyter: Interactive notebooks for coding and visualization.
• Google Colab: Free, cloud-hosted notebooks.
• Apache Spark: Handles big datasets efficiently.
• Weka: Easy tool for exploring datasets and testing algorithms.

You can think of these categories as a toolkit; depending on your project, you can pick a mix of frameworks, cloud services, and visualization tools.

Top 20 Machine Learning Tools in 2025

Here is a well-rounded list of the top 20 machine learning tools that can help you with your upskilling journey:

1. TensorFlow

TensorFlow is developed by Google and is one of the most widely used open-source frameworks for deep learning and machine learning. It offers a flexible ecosystem with APIs for Python, C++, and JavaScript, making it suitable for research as well as production-level applications.

Pros: Highly scalable, strong community support, seamless integration with cloud platforms.

Cons: Can be a little difficult for beginners compared to simpler libraries.

Best Suited For: Students, researchers, and engineers building neural networks, computer vision models, and NLP applications. It’s particularly useful if you want to move from experimentation to real-world deployment at scale.

2. PyTorch

PyTorch is developed by Meta. It is an open-source deep learning framework known for its dynamic computation graph and Pythonic design. It helps developers to experiment quickly while still being powerful enough for large-scale model training.

Pros: Easy to learn, strong debugging capabilities, widely adopted in research.

Cons: Slightly less optimized for production deployment compared to TensorFlow, though this is improving with TorchServe.

Best Suited For: Students, researchers, and developers who want to prototype models quickly, especially in areas like computer vision, reinforcement learning, and NLP. It’s the go-to framework in academia and increasingly in industry.

3. Scikit-learn

Scikit-learn is a widely used open-source Python library, as it is focused on classical machine learning. It provides efficient implementations of algorithms like regression, classification, clustering, and dimensionality reduction, along with tools for data preprocessing and model evaluation.

Pros: Beginner-friendly, lightweight, integrates seamlessly with NumPy and pandas, excellent documentation.

Cons: Not designed for deep learning or very large-scale data; better suited for small to medium datasets.

Best Suited For: Students, beginners, and professionals who want to learn and apply machine learning fundamentals or work on structured data projects like predictive modeling, recommendation systems, and basic analytics.

4. Keras

Keras is an open-source, high-level neural networks API that runs on top of TensorFlow. Its interface is simple and intuitive, which makes it easier to design, train, and deploy deep learning models without needing to write complex code.

Pros: Beginner-friendly, modular, and supports quick prototyping. Works seamlessly with TensorFlow’s powerful backend for large-scale deep learning.

Cons: Limited flexibility compared to raw TensorFlow or PyTorch; mainly suited for prototyping rather than highly customized research.

Best Suited For: Students, researchers, and developers who want to quickly build and test deep learning models such as image recognition, NLP, and computer vision applications.

5. Apache Spark MLlib

Apache Spark MLlib is the machine learning library built on top of Apache Spark. It’s designed for big data processing, which enables distributed machine learning at scale. MLlib supports a wide range of algorithms for classification, regression, clustering, and collaborative filtering.

Pros: Handles massive datasets efficiently, integrates smoothly with big data tools, and supports both batch and streaming data.

Cons: Requires some knowledge of the Spark ecosystem; not as specialized for deep learning as TensorFlow or PyTorch.

Best Suited For: Data engineers and AI practitioners working with large-scale datasets in industries like finance, telecom, and e-commerce.

6. Weka

Weka is an open-source machine learning software with a graphical user interface and is known for being beginner-friendly. It comes with built-in tools for data preprocessing, classification, regression, clustering, and visualization, without requiring heavy coding.

Pros: Easy to use, great for education and prototyping, supports a variety of ML algorithms out of the box.

Cons: Not designed for large-scale data; limited compared to modern deep learning frameworks.

Best Suited For: Students, researchers, and newcomers to ML who want to experiment with algorithms quickly and learn the fundamentals before moving to advanced frameworks.

7. BigML

BigML is a cloud-based, no-code machine learning platform that makes building and deploying ML models accessible to non-programmers. It offers tools for classification, regression, clustering, anomaly detection, and time-series forecasting, all through an intuitive web interface.

Pros: User-friendly, no coding required, strong visualization features, easy integration with business workflows.

Cons: Less flexible for highly customized ML tasks; best suited for small to medium datasets rather than complex enterprise-scale projects.

Best Suited For: Business analysts, managers, and students who want to apply ML to real-world problems quickly without deep technical expertise.

8. KNIME

KNIME is a low-code, open-source data analytics platform that allows users to design workflows for machine learning, data mining, and ETL processes. Its drag-and-drop interface makes it easy to preprocess data, train models, and visualize results.

Pros: No heavy coding required, highly modular, integrates well with Python, R, and big data tools.

Cons: Can become resource-intensive for very large datasets; advanced customization may still require coding knowledge.

Best Suited For: Students, business analysts, and data enthusiasts who want to build end-to-end ML workflows efficiently without starting from scratch in code.

9. RapidMiner

RapidMiner is a low-code/no-code platform designed for data science and machine learning workflows. It supports data preparation, model training, validation, and deployment with a visual interface and pre-built templates.

Pros: Beginner-friendly, integrates with Python and R, strong automation features, good for rapid prototyping.

Cons: Can be slower with very large datasets; advanced users may find it limiting compared to full coding frameworks.

Best Suited For: Students, business analysts, and professionals who want to quickly build and deploy ML models without deep programming knowledge, especially for business analytics and predictive modeling.

10. Google Colab

Google Colab is a cloud-based notebook environment that allows users to write and execute Python code in the browser. It comes pre-installed with popular ML libraries like TensorFlow, PyTorch, and Keras, and offers free access to GPUs and TPUs for faster model training.

Pros: Free to use, easy collaboration with Google Drive, no setup required, supports GPU/TPU acceleration.

Cons: Limited runtime and storage; performance may vary for very large datasets or long-running tasks.

Best Suited For: Students, researchers, and ML enthusiasts who want to experiment with machine learning projects quickly, prototype models, or learn deep learning without investing in hardware.

11. Jupyter Notebook

Jupyter Notebook is an open-source web application that lets users create and share documents containing live code, equations, visualizations, and narrative text. It supports languages like Python, R, and Julia, making it a versatile tool for ML experimentation and teaching.

Pros: Interactive and highly flexible, excellent for data exploration and visualization, integrates with libraries like NumPy, pandas, and Matplotlib.

Cons: Not ideal for production-level deployment; can become unwieldy with very large projects.

Best Suited For: Students, researchers, and developers who want to prototype ML models, analyze datasets, and create educational or research notebooks in an interactive environment.

12. Amazon SageMaker

Amazon SageMaker is a fully managed cloud platform that enables developers and data scientists to build, train, and deploy machine learning models at scale. It provides tools for data labeling, model tuning, monitoring, and integration with other AWS services.

Pros: Scalable, production-ready, integrates seamlessly with the AWS ecosystem, supports automated ML (AutoML) workflows.

Cons: Can be expensive for small projects; requires familiarity with AWS services for full potential.

Best Suited For: AI engineers, ML practitioners, and enterprises looking to deploy production-grade machine learning models efficiently and leverage cloud infrastructure for scalability.

13. Microsoft Azure Machine Learning

Azure Machine Learning is a cloud-based platform that allows users to build, train, and deploy machine learning models at scale. It supports automated ML, MLOps, and integrates with popular frameworks like TensorFlow, PyTorch, and Scikit-learn.

Pros: Scalable, enterprise-ready, strong integration with the Microsoft ecosystem, supports both coding and no-code workflows.

Cons: Can be complex for beginners; pricing may be high for extensive usage.

Best Suited For: AI engineers, data scientists, and enterprises aiming to deploy end-to-end ML solutions on a cloud platform with robust enterprise features.

14. Google Vertex AI

Google Vertex AI is a managed machine learning platform that unifies Google Cloud’s AI tools. It allows users to build, deploy, and scale ML models with pre-built pipelines, AutoML capabilities, and seamless integration with BigQuery and other Google services.

Pros: Scalable, supports AutoML, integrates with the Google Cloud ecosystem, and reduces operational overhead for ML deployment.

Cons: Can be overwhelming for beginners; cost increases with heavy usage.

Best Suited For: AI engineers, data scientists, and businesses who want to develop and deploy ML models at scale while leveraging Google Cloud’s infrastructure and AI services.

15. IBM Watson Studio

IBM Watson Studio is a cloud-based platform that provides tools for building, training, and deploying machine learning and AI models. It supports Python, R, and SPSS, along with automated model building and data visualization capabilities.

Pros: Integrates well with IBM Cloud services, supports collaboration, and AutoAI features simplify model development.

Cons: Can be costly for small teams, steeper learning curve compared to simpler ML platforms.

Best Suited For: Data scientists, AI engineers, and enterprises aiming to deploy AI-driven applications in industries like healthcare, finance, and retail while leveraging robust cloud-based ML tools.

16. Apache Mahout

Apache Mahout is an open-source machine learning library designed for algorithms on big data platforms like Hadoop and Spark. It provides implementations for clustering, classification, and collaborative filtering, focusing on distributed processing.

Pros: Handles large datasets efficiently, integrates with Hadoop/Spark ecosystems, is open-source, and flexible.

Cons: Limited deep learning support; more suitable for classical ML and big data tasks.

Best Suited For: Data engineers and ML practitioners working on large-scale recommendation systems, clustering tasks, and analytics projects where scalability and distributed computing are key.

17. Shogun

Shogun is an open-source machine learning library that focuses on kernel methods and large-scale learning. It supports multiple programming languages, including C++, Python, Java, and R, and offers algorithms for classification, regression, clustering, and dimensionality reduction.

Pros: Efficient for large-scale datasets, supports multiple languages, strong for classical ML techniques like SVMs.

Cons: Limited support for deep learning and modern neural network architectures; smaller community compared to TensorFlow or PyTorch.

Best Suited For: Researchers, students, and practitioners working on large-scale supervised and unsupervised learning problems, especially when kernel-based methods are preferred.

18. H2O.ai

H2O.ai is an open-source, enterprise-ready platform for machine learning and AI. It provides tools for automated machine learning (AutoML), data preprocessing, and scalable ML algorithms, supporting Python, R, and Java.

Pros: Fast model training, supports AutoML, integrates well with big data platforms, suitable for both beginners and professionals.

Cons: GUI-based workflows may feel limiting for advanced custom models; enterprise features require a subscription.

Best Suited For: AI engineers, data scientists, and business analysts who want to quickly build predictive models and deploy ML solutions in sectors like finance, insurance, and healthcare.

19. DataRobot (AutoML)

DataRobot is a cloud-based AutoML platform that automates the end-to-end process of building, training, and deploying machine learning models. It supports various algorithms, feature engineering, and model evaluation, making ML accessible for non-experts.

Pros: Speeds up model development, no extensive coding required, provides explainable AI insights, suitable for business applications.

Cons: Subscription-based, less flexible for custom algorithm development compared to open-source frameworks.

Best Suited For: Business analysts, data scientists, and AI practitioners who want to quickly deploy predictive models for applications like forecasting, risk assessment, and customer analytics without deep programming expertise.

20. MATLAB ML Toolbox

MATLAB Machine Learning Toolbox can be used for designing, training, and deploying ML models. It supports classification, regression, clustering, and dimensionality reduction with easy-to-use functions and visualizations.

Pros: Excellent for prototyping, strong visualization capabilities, and integrates well with signal processing and control systems toolboxes.

Cons: Commercial software with licensing costs, less commonly used in modern deep learning compared to TensorFlow or PyTorch.

Best Suited For: Students, engineers, and researchers in academia, robotics, and engineering domains who want to experiment with ML algorithms in a visual, interactive environment.

Also, Check out: 25 Data Science Tools to Consider in 2025

How to Choose the Right Machine Learning Tool?

1. Experience Level: Beginners can start with Weka, KNIME, or Colab, while advanced users may prefer TensorFlow, PyTorch, or Scikit-learn.
2. Cloud / On-Premise: Cloud tools like SageMaker, Azure ML, Vertex AI scale easily, whereas on-premise tools like MATLAB or Mahout suit private or internal setups.
3. Industry Use Case: Healthcare - IBM Watson; Startups - Colab; Enterprises - SageMaker/Azure ML.
4. Budget: Open-source tools Scikit-learn, H2O.ai, are cost-effective; enterprise platforms may involve subscriptions or cloud fees.
5. To Practice: Try multiple tools to understand their strengths.

If you need guidance and a path, do check out Scaler’s Machine Learning course, where we provide structured learning, 1:1 mentorship, placement guidance, real-world projects, and exposure to these tools, helping you confidently choose and master the right ML tool for your career.

Future of Machine Learning Tools

Machine learning tools are significantly evolving, driven by advancements in AI, cloud computing, and automation. As we move into 2025 and beyond, ML tools are expected to become smarter, more accessible, and integrated across industries, enabling both developers and non-technical users to build powerful AI solutions.

Integration with Generative AI

One of the biggest trends is the integration of generative AI and large language models (LLMs) into ML tools. Platforms are starting to embed AI assistants, similar to Copilot for ML, which help users write code, suggest model improvements, and automate repetitive tasks. This reduces development time and allows engineers to focus on more strategic aspects of model design and deployment.

Automated ML (AutoML) Expansion

The rise of no-code and low-code ML platforms is making machine learning more approachable. AutoML tools automatically handle feature selection, model training, and hyperparameter tuning, enabling business analysts, product managers, and beginners to build effective models without deep technical expertise. This trend is expected to accelerate, further democratizing AI development.

Ethical & Responsible AI Tools

With AI becoming a central part of decision-making, responsible and ethical AI features are gaining importance. Tools now include bias detection, fairness metrics, and explainability dashboards, helping organizations deploy ML models that are transparent, accountable, and compliant with ethical standards. This trend ensures AI solutions are both effective and socially responsible.

Cross-Industry Adoption

Finally, ML tools are expanding across sectors like IoT, retail, healthcare, and finance. Edge ML allows real-time processing on devices, enabling smart sensors, personalized healthcare recommendations, and predictive analytics in retail. This cross-industry adoption highlights how integral ML tools are becoming in both strategic and operational workflows.

As ML tools continue to advance and integrate with emerging technologies, staying updated and gaining hands-on experience will be crucial for aspiring AI professionals and organizations looking to leverage AI efficiently.