7 Free Machine Learning Tools Every Beginner Should Master in 2024

As a beginner in machine learning, you should not only understand algorithms but also the broader ecosystem of tools that help in building, tracking, and deploying models efficiently.

Remember, the machine learning lifecycle includes everything from model development to version control, and deployment. In this guide, we’ll walk through several tools—libraries and frameworks—that every aspiring machine learning practitioner should familiarize themselves with.

These tools will help you manage data, track experiments, explain models, and deploy solutions in production, ensuring a smooth workflow from start to finish. Let’s go over them.

1. Scikit-learn

What it is for: Machine Learning Development

Why it is important: Scikit-learn is the most popular library for machine learning in Python. It offers simple yet effective tools for data preprocessing, model training, evaluation, and model selection. It has ready-to-use implementations of supervised and unsupervised algorithms makes it the go-to library for beginners and experts alike.

Key Features

• Easy-to-use interface for ML algorithms
• Extensive support for data preprocessing and creating pipelines
• Built-in support for cross-validation, hyperparameter tuning, and evaluation

So scikit-learn is an excellent starting point to familiarize yourself with core algorithms and machine learning workflows. To get started, check out the Scikit-learn Crash Course – Machine Learning Library for Python.

2. Great Expectations

What it is for: Data validation and quality assessment

Why it is important: Machine learning models rely on high-quality data. Great Expectations automates the process of validating data by allowing you to set up expectations for your data’s structure, quality, and values. This ensures that you catch data issues early in the pipeline, preventing poor-quality data from negatively affecting model performance.

Key Features

• Automatically generate and validate expectations for datasets
• Integration with popular data storage and workflow tools
• Detailed reports for identifying and resolving data quality issues

By using Great Expectations early in your projects, you can focus more on modeling while reducing the risk of data-related issues. To learn more, watch Great Expectations Data Quality Testing.

3. MLflow

What it is for: Experiment tracking and model management

Why it is important: Experiment tracking is important for managing machine learning projects. MLflow helps track experiments, manage models, and streamline the machine learning workflow. With MLflow, you can log parameters and metrics, making it easier to reproduce and compare results.

Key Features

• Experiment tracking and logging
• Model versioning and lifecycle management
• Easy integration with many popular machine learning libraries such as scikit-learn

So tools like MLflow are important for keeping track of experiments in the iterative process of model development. Check out Getting Started with MLflow is a helpful resource to learn more.

4. DVC (Data Version Control)

What it is for: Data & Model Version Control

Why it is important: DVC is like a version control system for data science and machine learning projects. It helps track not only code but also datasets, model weights, and other large files. This makes your experiments reproducible and ensures that data and model versioning is handled efficiently across teams.

Key Features

• Version control for data and models
• Efficient management of large files and pipelines
• Easy integration with Git.

Using DVC helps you to track datasets and models just as you would track code, offering full transparency and reproducibility. To get familiar with DVC, check out the Data and Model Versioning tutorial.

5. SHAP (SHapley Additive exPlanations)

What it is for: Model explainability

Why it is important: It’s often helpful to understand how machine learning models make decisions. As machine learning models become more complex, it’s important to explain model predictions in a transparent and interpretable way. SHAP helps with model explainability by using Shapley values to quantify the contribution of each feature to the model’s output.

Key Features

• Feature importance based on Shapley values
• Provides useful visualizations, such as summary and dependence plots
• Works with many popular machine learning models

SHAP is a simple and effective tool to understand complex models and the importance of each feature, making it easier for both beginners and experts to interpret results.Check out this SHAP Values tutorial on Kaggle. You can then explore other explainability models as well.

6. FastAPI

What it is for: API development and model deployment

Why it is important: Once you have a trained model, FastAPI is an excellent tool for serving it via an API. FastAPI is a modern web framework that enables you to build fast, production-ready APIs with minimal code. It’s perfect for deploying machine learning models and making them accessible to users or other systems via RESTful endpoints.

Key Features

• Simple and fast API development
• Asynchronous capabilities for high-performance APIs
• Built-in support for model inference endpoints

FastAPI is, therefore, a useful tool when you need to create a scalable, production-ready API for your machine learning models. Follow along to FastAPI Tutorial: Build APIs with Python in Minute to get started with building APIs.

7. Docker

What it is for: Containerization and deployment

Why it is important: Docker simplifies the deployment process by packaging applications and their dependencies into containers. For machine learning, Docker ensures that your model will run consistently across different environments, making it easier to scale and deploy your solution.

Key Features

• Ensures reproducibility across different environments
• Lightweight containers for deploying ML models
• Easy integration with CI/CD pipelines and cloud platforms

Docker is, therefore, a must-have tool when you’re ready to move your machine learning models into production. It ensures consistent performance by containerizing your code, dependencies, and environment, making the deployment process smooth and reliable. Get started with this Docker Tutorial for Beginners.

Conclusion

Learning to work with these tools will help you level up as you progress in machine learning. We discussed a suite of tools: from building ML models with scikit-learn to ensuring data quality with Great Expectations and managing experiments with MLflow and DVC.

Docker and FastAPI enable smooth deployment in real-world environments. With these tools, you’ll have a complete toolkit for building robust, reproducible models.

Happy machine learning!