20 Essential Python Libraries for Machine Learning
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Machine learning projects hardly rely on a single Python library. NumPy and pandas handle much of the data preparation, scikit-learn provides tools for traditional ML models, while PyTorch and TensorFlow are usually used for deep learning. Just like XGBoost is used for gradient boosting, JAX is used for high-performance numerical computing and machine learning, OpenCV for image processing, and Matplotlib and Seaborn for data visualisation.
The right library depends on what you are trying to build and the stage of the project you are working on. This article covers 20 Python libraries for machine learning, their main features, and where they fit into an ML workflow.
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Which Python Libraries are Used For Machine Learning?
The best Python libraries for machine learning to start with are NumPy, pandas, Matplotlib, scikit-learn, and PyTorch. NumPy handles numerical operations, pandas handles data, Matplotlib is used for visualisation, scikit-learn covers classical machine learning, and PyTorch is used for deep learning. And as you take on different projects, you can check out more libraries to work with.
The Five Libraries You'll Actually Use Most Often
If you’re starting with machine learning, five Python libraries will cover most of the work you’ll do early on: NumPy, pandas, Matplotlib, scikit-learn, and PyTorch.
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NumPy is used for numerical operations and array-based computing.
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pandas is used to load, clean, filter, and work with datasets.
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Matplotlib is used to create charts and plots for data visualisation.
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scikit-learn covers classical machine learning, preprocessing, and model evaluation.
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PyTorch is used for neural networks and deep learning.
You don’t need to master all five before working with the others. The libraries you use next will depend on the kind of project you’re building. XGBoost, LightGBM, and CatBoost are used for tabular machine learning, while Hugging Face Transformers are used for pretrained transformer models. MLflow, ONNX, and FastAPI are used when you’re working with experiment tracking, model formats, or deployment.
The sections below cover each library in more detail, including what it is used for and where it fits into a machine learning project.
Want to plan your Python learning journey? Check out: Python Roadmap 2026
Python libraries for data handling and numerical work
1. NumPy
NumPy works with numerical data stored in arrays. You can use it to perform operations on entire sets of values at once, reshape data into the dimensions required by a model, select specific values with indexing and slicing, and apply mathematical operations across rows, columns, or entire arrays.
And so, a dataset containing numerical features can be stored as a NumPy array and transformed before it is passed to a machine learning model. Operations such as normalization, matrix multiplication, statistical calculations, and element-wise arithmetic can also be performed directly on these arrays.
2. pandas
pandas works with structured datasets through Series and DataFrame objects, where data is organised into labelled rows and columns. It supports reading data from formats such as CSV, selecting rows and columns, filtering records, sorting values, combining datasets, and calculating statistics.
In a machine learning project, pandas handles much of the data preparation before modelling. Missing values can be identified and handled, data types can be changed, new columns can be created from existing data, and features can be separated from the target variable. The resulting dataset can then be passed to the ML library used for modelling.
3. Polars
Polars takes the DataFrame approach you may already know from pandas, but it is designed with speed and larger datasets in mind. Written in Rust, it uses multiple CPU cores to process operations in parallel, which can make data loading, filtering, joining, and transformations faster on larger workloads.
If you start working with datasets where pandas operations take noticeable time or consume a lot of memory, Polars is worth looking at. Its lazy API also plans and optimises a sequence of operations before running them, so unnecessary work can be removed before the data is processed.
For a machine learning project, you can use Polars for the same kind of data preparation you would handle with pandas: cleaning data, filtering rows, joining datasets, creating columns, and preparing the final dataset for your model.
4. SciPy
SciPy provides functions for scientific and mathematical computing, including optimisation, statistics, linear algebra, interpolation, and signal processing.
In machine learning, you may use SciPy for statistical tests, probability distributions, distance calculations, numerical optimisation, or sparse matrix operations. For example, scipy.stats contains statistical distributions and tests, while scipyoptimize includes algorithms for finding optimal values.
You can work on many standard ML projects without using SciPy directly. When your project requires one of these mathematical or statistical operations, the relevant SciPy modules can handle that part of the work.
Learn more about preparing data for machine learning: Data Cleaning in Machine Learning - Scaler.
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In many business problems where data is in tabular format, classical machine learning may be the right choice over deep learning. Decision tree algorithms, gradient boosting, and others could perform such tasks as churn prediction, fraud detection, and demand forecasting without the need for a neural network.
Understand the different approaches to machine learning with Types of Machine Learning.
5. scikit-learn
scikit-learn is where you’ll find algorithms for tasks such as classification, regression, clustering, and dimensionality reduction. It also handles several steps around the model itself, including splitting your dataset, scaling features, encoding data, selecting features, and evaluating predictions.
For example, you might have a dataset in pandas and want to predict whether a customer will leave a service. You can split the data into training and test sets, preprocess the columns needed, train a classification model, and evaluate its predictions using scikit-learn. If you want to try a different algorithm afterwards, you can follow the same basic fit() and predict() workflow.
6. XGBoost
XGBoost is a gradient boosting library you’ll often come across when working with tabular data. If your dataset contains rows of customers, transactions, products, or sales records, XGBoost can be used for predictions such as whether a customer will churn, whether a transaction is fraudulent, or how much demand to expect for a product.
The model builds decision trees sequentially. Each new tree works on the prediction errors left by the earlier trees, gradually improving the final result. XGBoost also includes regularisation and controls for tree depth, learning rate, and other parameters that help you tune how the model learns from your data.
7. LightGBM
LightGBM is a gradient boosting framework designed for tabular machine learning. It uses histogram-based learning and leaf-wise tree growth to reduce the time and memory needed to train models on large datasets.
If you’re working with hundreds of thousands of rows and your model training is taking too long or using more memory than your setup can handle, LightGBM gives you another way to approach the same classification, regression, or ranking problem. You can also adjust parameters such as the number of leaves, maximum depth, and learning rate to control how the model grows and learns from your data.
If your dataset is small enough to train quickly, these performance considerations may not affect your workflow much. In that case, you can concentrate on the model and the quality of your data instead of choosing a library based on processing speed.
8. CatBoost
CatBoost is a gradient boosting library that handles categorical features directly. If your dataset contains columns such as city, product type, customer segment, or subscription plan, CatBoost can work with those values without requiring you to manually encode every categorical column first.
For example, a customer dataset might contain city, plan_type, and customer_segment alongside numerical features such as age and monthly spending. CatBoost can use these categorical columns while building the model, which can simplify the preprocessing required before training.
CatBoost supports classification and regression, so you can use it for problems such as predicting customer churn, estimating prices, or classifying transactions. If you're comparing it with XGBoost or LightGBM, the type of data you have and how you want to handle categorical features can help determine which one fits your project.
Also Read: Performance Metrics in Machine Learning Explained
Deep Learning Frameworks
Deep learning involves a different set of frameworks from classical machine learning. These tools handle tasks such as building neural networks, training models, and working with GPUs. The Python libraries for deep learning covered here are PyTorch, TensorFlow, Keras, and JAX.
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9. PyTorch
PyTorch is used to build and train neural networks. Its tools cover tensors, automatic differentiation, neural network architectures, and GPU computation. If you’re working with deep learning, computer vision, natural language processing, or generative AI, these are the building blocks you’ll work with when developing and training models.
You can also get started with PyTorch with this Free PyTorch Course.
10. TensorFlow
TensorFlow is Google's open-source machine learning framework and has a strong production ecosystem. It supports model training across CPUs, GPUs, and TPUs, with tools for deploying models across different environments.
It also has close ties with Keras, which allows for a high-level API in order to build deep learning models. It fits well when you have an existing TensorFlow-based development process, whether it be web, mobile, or otherwise.
11. Keras
Keras is a high-level deep learning API for building and training neural networks. Keras 3 supports JAX, TensorFlow, and PyTorch as backends, so the same Keras interface can be used with different deep learning frameworks.
Older tutorials and TensorFlow projects may use tf.keras, but Keras 3 is no longer limited to TensorFlow. When working with current Keras, you can choose JAX, TensorFlow, or PyTorch as the backend for your project.
Learn more for free with: Deep Learning Online Certification Course with TensorFlow and Keras
12. JAX
JAX is designed for fast numerical computations and machine learning, with capabilities for automatic differentiation, compilation, batching, and parallelization on CPUs, GPUs, and TPUs. It is more advanced than what most beginners need. It is preferred when you're working with large models, accelerators, or research-heavy ML systems.
PyTorch vs TensorFlow: Which Should You Learn?
You should begin with PyTorch if you’re getting started with deep learning. Build a few neural networks with it, work with tensors, define layers, and train your models. That gives you a practical understanding of the main pieces involved in deep learning without having to learn two frameworks at once.
Once you’re able to use PyTorch, you can pick up TensorFlow when you need it for a project, course, or job. The underlying concepts stay the same even though the APIs and workflows are different.
For classical machine learning, you’ll usually work with scikit-learn instead. Regression, classification, clustering, and similar ML tasks fall within its scope, while PyTorch and TensorFlow handle neural networks and deep learning.
NLP and pretrained model libraries
When you’re preparing NLP projects, you end up requiring a different set of tools altogether. Some help you work with pretrained models and datasets, while others are designed for building and running complete language-processing pipelines.
Learn the fundamentals with NLP Roadmap
13. Hugging Face Transformers
Hugging Face Transformers gives you access to pretrained models for tasks such as text classification, summarisation, text generation, question answering, and more. Instead of training a model from scratch, you can start with an existing model and adapt it to your task.
The library also includes tokenizers that convert text into the inputs a model can process, while the wider Hugging Face platform provides models and datasets for training, evaluation, and experimentation. You can download a pretrained model, run it on your own data, and fine-tune it when the task requires more specialised results.
Once you move from traditional machine learning into NLP, large language models, or generative AI, Hugging Face becomes a library you’re likely to work with alongside your deep learning framework.
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spaCy is designed for building production NLP pipelines. It provides tools for tasks such as tokenisation, part-of-speech tagging, named entity recognition, and dependency parsing, with an emphasis on efficient text processing. You can use these tools with trained models and other components to build pipelines for specific NLP tasks. NLTK is another well-known NLP library, mainly used for learning and teaching NLP concepts.
Visualisation Libraries
Once you start working with machine learning datasets, you’ll need to see what the numbers are telling you. A plot can show whether a feature has extreme values, whether two variables have a relationship, or whether your model’s predictions are consistently off for certain data points. That makes visualisation part of both data exploration and model evaluation.
Matplotlib, Seaborn, and Plotly cover these tasks in different ways. Matplotlib gives you the basic building blocks for creating plots, Seaborn makes statistical charts easier to create, and Plotly adds interactive charts. You can start with Matplotlib and pick up the others as your projects require them.
15. Matplotlib
The main tool for visualisation in Python is Matplotlib. It provides control over various types of charts like line graphs, bar charts, scatter plots, and histograms, making it a good starting point for machine learning data.
It's used frequently to examine distributions, compare variables, to look for trends, or to plot model output. There are quite a few Python visualization tools that are built on, or complement, Matplotlib, so having a basic knowledge of the approach to plotting in Matplotlib will help you in picking up the rest of the visualization area.
16. Seaborn
Seaborn is an extension of Matplotlib with a simpler interface to create statistical graphs, requiring less code to write. It is helpful for studying relationships and distributions in tabular datasets.
Once you're used to making basic plots with Matplotlib, Seaborn can help you create more polished statistical charts without configuring every detail yourself.
17. Plotly
Plotly is designed for interactive visualisations. You can use it to create charts where users can hover over points, zoom in, filter the data, and explore the chart in more detail.
It's a strong alternative when you're building interactive reports, dashboards, or web-based data applications. For basic ML exploration, Matplotlib is enough. Pick up Plotly when your project needs interaction or a presentation that readers can explore themselves.
Experiment tracking and deployment libraries
As you train different versions of a model, you’ll need to keep track of which dataset, parameters, and results produced each version. Once you’re ready to use the model outside your notebook, you’ll also need to save it, validate the data it receives, and connect it to an application or API. The libraries in this section help you handle those tasks.
Read more: What is a Machine Learning Pipeline?
18. MLflow
MLflow helps you keep track of machine learning experiments and manage trained models. You can record things such as model parameters, metrics, and results across different runs, making it easier to compare what you tried and what worked.
Its model registry also gives teams a central place to manage model versions as they move through development and deployment. You don't need MLflow for your first few experiments. It becomes worth adding when you're running multiple experiments, comparing results, managing model versions, or collaborating with a team.
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19. ONNX
ONNX stores a trained machine learning model as a file containing the model’s operations, inputs, outputs, and learned parameters. Frameworks such as PyTorch and TensorFlow can export trained models to this format, and ONNX-compatible runtimes can then load the file and run the model. For example, you can train an image classification model in PyTorch, export it as an ONNX file, and use ONNX Runtime to generate predictions. The production system can run the exported model without setting up the original PyTorch training environment.
You’ll encounter ONNX when you need to take a model trained in one framework and run it in a different inference environment.
20. FastAPI
FastAPI is a Python framework for building APIs. In machine learning applications, you can use it to create an endpoint that accepts input, passes it to a trained model, and returns the model's prediction.
This is the step that turns a model sitting in a notebook into something another application can actually call. You don't need FastAPI while you're still learning how models work, but you'll want to know it when you're ready to put a model behind an application or service.
Supporting Libraries for Data Validation and Experiment Tracking
Before you send data to a model, you can validate its structure and check whether it meets the conditions your project expects. Pydantic can validate incoming data against defined types and structures, while Great Expectations can check datasets against rules such as expected data types, ranges, or missing-value limits.
For experiment tracking, Weights & Biases provides another option alongside MLflow for recording runs, parameters, metrics, and other experiment details. These tools handle specific parts of an ML project, so you can add them when your project needs data validation or more detailed experiment tracking.
These tools don't add to the 20-library count as they're supporting tools you can bring in when a project needs them.
Python machine learning libraries compared
The 20 machine learning libraries in Python covered here don't have equal learning priority. Some form the foundation of your ML workflow, while others solve specific problems you may not encounter for months.
| Library | Category | What it's for | Learn-first priority | Maturity |
|---|---|---|---|---|
| NumPy | Data & numerical | Arrays and numerical operations | 1 · Essential | Established |
| pandas | Data handling | Loading and cleaning tabular data | 2 · Essential | Established |
| Polars | Data handling | Fast dataframe workflows | Later · Situational | Newer |
| SciPy | Numerical & scientific | Statistics and optimisation | Later · Situational | Established |
| scikit-learn | Classical ML | Training and evaluating ML models | 4 · Essential | Established |
| XGBoost | Classical ML | Gradient boosting for tabular data | Later · High value | Established |
| LightGBM | Classical ML | Gradient boosting at scale | Later · Situational | Established |
| CatBoost | Classical ML | Boosting with categorical data | Later · Situational | Established |
| PyTorch | Deep learning | Neural networks and model training | 5 · Essential | Established |
| TensorFlow | Deep learning | Model training and deployment | Later · Situational | Established |
| Keras | Deep learning | High-level model development | Later · Situational | Established |
| JAX | Deep learning | High-performance numerical ML | Later · Niche | Specialised |
| Hugging Face Transformers | NLP & pretrained models | Pretrained models and NLP | Later · High value | Fast-moving |
| spaCy | NLP | Production NLP pipelines | Later · Situational | Established |
| Matplotlib | Visualisation | Charts and data exploration | 3 · Essential | Established |
| Seaborn | Visualisation | Statistical charts | Later · Helpful | Established |
| Plotly | Visualisation | Interactive charts and dashboards | Later · Situational | Established |
| MLflow | Tracking & deployment | Experiment tracking and model registry | Later · Situational | Established |
| ONNX | Deployment | Moving models across runtimes | Later · Situational | Established |
| FastAPI | Deployment | Serving models through APIs | Later · Situational | Established |
The first five libraries give you a foundation for machine learning. After that, what you learn depends on what you're building. XGBoost makes sense for a tabular modelling project, while Hugging Face is mainly used for NLP and pretrained models.
Note: “Maturity” indicates how long a library has been around, how widely it is used, and how established it is in the machine learning community. It does not mean that a library with a higher maturity level is better than one with a lower level.
Also check out: Top 29 Python Libraries for Data Science
Common Mistakes When Choosing Machine Learning Libraries
Choosing a library based on how impressive it sounds can send your learning in the wrong direction. A few common mistakes can leave you with a crowded environment and very little understanding of what to use.
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Installing everything at once: Having 20 libraries installed doesn't make you ready for machine learning. Start with the core five and add tools when a project calls for them.
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Reaching for deep learning on a 2,000-row spreadsheet: A small tabular dataset doesn't automatically need a neural network. Start with classical ML and see what the data and problem call for.
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Learning a framework before understanding the maths: PyTorch or TensorFlow can run a model for you, but you still need to understand concepts such as loss, optimisation, and evaluation to know what the model is doing.
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Copying a library list from a 2019 tutorial: Python's ML ecosystem changes quickly. Older lists may recommend tools that have been replaced, overlook newer options, or describe libraries in ways that no longer reflect how they're used today.
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FAQs
Which Python library is best for machine learning?
Scikit-learn is one of the best Python libraries to start with for classical machine learning. It supports classification, regression, clustering, preprocessing, feature selection, and model evaluation. For deep learning, PyTorch and TensorFlow are the main choices, while libraries such as XGBoost, LightGBM, and Hugging Face Transformers are suited to specific ML tasks.
Do I need to learn all 20 libraries?
No. You can begin with NumPy, pandas, Matplotlib, scikit-learn, and PyTorch. These five cover numerical work, data handling, visualisation, classical machine learning, and deep learning. The remaining libraries solve more specific problems. Learn them when a project calls for them, not because they're on a list of 20.
Should I learn PyTorch or TensorFlow?
Start with PyTorch if you're learning deep learning from scratch. Its design gives you a clearer view of how models are built and trained. TensorFlow is worth learning when you come across a project or job that uses it.
Is pandas still worth learning in 2026, given Polars?
Yes. pandas is a key library in Python for working with tabular data. It makes it easy to load, clean, and transform datasets using dataframes. Polars is another option for working with tabular data, and you can pick it up when a project calls for it.
Which Python libraries do I need before starting machine learning?
Start off by using NumPy, pandas, and Matplotlib. NumPy is your source of numerical functions, pandas is used to import and clean your datasets, while Matplotlib is used for visualisation prior to modelling. When you have mastered all of these, then move on to scikit-learn for classical machine learning.
Are these libraries free?
Yes. The libraries themselves are open-source and can be installed and used without paying for a licence. Some ecosystems also offer paid hosted services, compute, storage, or premium features. For example, Hugging Face offers paid Pro, Team, and Enterprise plans alongside its open-source ecosystem.
Which library is used to deploy a machine learning model?
FastAPI can expose a trained model through an API so another application can send input and receive predictions. ONNX helps move trained models between supported frameworks and inference runtimes. They solve different parts of deployment, so the right choice depends on how your model needs to run.
Want to build these skills through projects? Our AI & Machine Learning program includes hands-on projects where you can work with the tools used across machine learning workflows, from data preparation and model building to deployment.
You can also continue with a Machine Learning Roadmap or read How to Become a Machine Learning Engineer for the next steps in your learning journey.
