The processing of coding manually became time-consuming, tedious, and inefficient. However, with multiple python libraries, frameworks, and modules, it has become much easier and more efficient than the yesteryear days.
Machine Learning is the science of programming a computer such that it can learn from many types of data. They are commonly used to solve a variety of life issues.
People used to execute machine learning jobs by manually coding all of the algorithms, mathematical, and statistical calculations in the past. The processing became time-consuming, tedious, and inefficient as a result. However, with multiple python libraries, frameworks, and modules, it has become much easier and more efficient than the yesteryear days.
Here are some of the best Python libraries for machine learning:
- SciPy
SciPy is a popular Python library for machine learning enthusiasts since it includes modules for optimisation, linear algebra, integration, and statistics. The SciPy library and the SciPy stack are two different things. SciPy is one of the SciPy stack’s most essential packages. SciPy may also be used to manipulate images.
2. Theano
Theano is a well-known Python library for efficiently defining, evaluating, and optimising mathematical statements using multi-dimensional arrays. It is accomplished by maximising CPU and GPU use. It is widely used to discover and diagnose defects during unit testing and self-verification. Theano is a powerful library that has long been used in large-scale computationally intensive scientific research. Still, it’s also accessible and approachable enough for amateurs to utilise for their projects.
3. NumPy
NumPy is a popular Python library for processing massive multi-dimensional arrays and matrices using many high-level mathematical operations. It comes in handy for basic scientific computations in Machine Learning. Its linear algebra, Fourier transform, and random number skills are particularly valuable. NumPy is used internally by high-end libraries like TensorFlow to manipulate Tensors.
4. Keras
Keras is a well-known Python Machine Learning library. It’s a high-level neural network API that works with TensorFlow, CNTK, and Theano. It can run on both the CPU and the GPU. Keras makes building and designing a Neural Network a breeze for ML newbies. One of the best things about Keras is that it makes prototyping simple and quick.
5. Scikit-learn
Scikit-learn is a popular machine learning library for traditional machine learning methods. It is based on two fundamental Python libraries, NumPy and SciPy. Scikit-learn supports most supervised and unsupervised learning algorithms. Scikit-learn may also be used for data mining and analysis, making it an excellent tool for those new to machine learning.
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