Artificial intelligence (AI) is a broad field that refers to the use of technologies to build machines and computers that have the ability to mimic cognitive functions associated with human intelligence, such as the abilities to see, understand, and respond to spoken or written language, and to analyse data, make recommendations, and more ¹⁴. Machine learning (ML) is a subset of AI that automatically enables a machine or system to learn and improve from experience. Instead of explicit programming, machine learning uses algorithms to process large amounts of data, learn from the insights, and then make informed decisions. Machine learning algorithms improve in performance over time as they are trained – exposed to more data. Machine learning models are the output, or what the program learns, from running an algorithm on training data. The more data used, the better the model will become.
Machine Learning Frameworks Available
There are many machine learning frameworks available, each with its own strengths and weaknesses. Here are some of the most popular:
- TensorFlow: TensorFlow is an open source machine learning framework that was developed by Google. It is widely used for building deep learning models and has a large community of developers.
- PyTorch: PyTorch is another popular open source machine learning framework, which is widely used for the construction of deep learning models. It is known for its ease of use and flexibility.
- scikit-learn: scikit-learn is a popular machine learning library for Python that provides a wide range of algorithms for classification, regression, clustering, and more. It is known for its ease of use and is widely used in industry and academia ¹⁴.
- Keras: Built on top of TensorFlow, Keras is a high-level neural network API. It provides a simple and intuitive interface for building deep learning models ¹³.
- MXNet: Known for its scalability and speed, MXNet is an open source deep learning framework. It supports multiple programming languages and provides a wide range of tools for building deep learning models.
- Caffe: Caffe is a deep learning framework widely used in computer vision applications. It provides a simple and efficient interface to build convolutional neural nets (CNNs).
- **Theano: Theano is a Python library that allows you to efficiently define, optimise and evaluate mathematical expressions involving multi-dimensional arrays. It is widely used for building deep learning models.
- Torch: Torch is an open source machine learning library that has a wide range of algorithms for deep learning model building. It is known for its ease of use and flexibility.
- **CNTK: CNTK (Microsoft Cognitive Toolkit) is an open source framework for deep learning that was developed by Microsoft. It provides a wide range of tools for building deep learning models, and supports multiple languages.



