Machine learning plays a crucial role in modern technology by analyzing massive datasets and creating models that solve real-world problems in major sectors such as health, finance, and marketing. From self-driving cars to breast cancer detection, machine learning automates complex tasks and improves efficiency. To solve these problems, it is essential to choose a proficient programming language so that the models can be trained efficiently.
Python and C serve different roles in ML; C plays a crucial role in accelerating machine learning computation, but Python is the go-to choice of every programmer to build AI models. According to a survey, around 75% of developers use Python for AI/ML. So the quest is, is Python the best, or does C have its place?
Let’s have a comprehensive comparison
| Feature | Python | C |
| Ease of Use | Simple, readable, beginner-friendly | Complex, requires manual memory management |
| Performance | Slower, relies on optimized libraries | Faster, closer to hardware |
| Libraries & Ecosystem | Extensive (TensorFlow, PyTorch, Scikit-learn) | Few direct ML libraries, mostly used in backends |
| Development Speed | Rapid prototyping, high-level API support | Slower development requires more coding |
| Community Support | Large, active AI/ML community | Smaller ML community, mainly system-level developers |
| Use Cases | Research, prototyping, data science, deep learning | Performance-critical tasks, embedded ML, optimizing ML frameworks |
| Memory Management | Automatic (garbage collection) | Manual (developer must handle allocation) |
| Industry Adoption | Widely used in AI/ML applications | Used in backend optimization (e.g., TensorFlow core) |
| Best For | AI research, NLP, data science, rapid development | High-performance computing, embedded ML, edge AI |
Python dominates machine learning and AI due to its simplicity, rich ML ecosystem, and faster prototyping. It allows researchers and developers to experiment with high-level frameworks and libraries like TensorFlow, PyTorch, and Scikit-learn, making it ideal for AI research, deep learning, and data engineering.
On the other hand, C is faster and more efficient, making it valuable for performance-critical applications, embedded AI, and optimizing ML frameworks. However, its complexity and manual memory management make it less practical for everyday ML development.
So here we can conclude that Python is the best choice for most ML applications, while C is useful when speed and hardware-level optimization are crucial.