Machine learning, or ML, is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. It is based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention.
ML algorithms and models are used in a wide variety of applications such as email filtering, computer visions, selfdriving cars and others. The types of ML algorithms depend on the task or problem intended to be solved, approached, and the type of input and output. Four popular types of ML algorithms are described below.
This algorithm involves direct supervision of the operation. The most common application of this algorithm is price prediction and trend forecasting in sales, retail commerce and stock trading. The algorithm uses incoming data to assess the possibilities and calculate possible outcomes.
This algorithm does not involve direct control of the programmer. It is used in digital marketing and ad technology to identify target audience based on certain credentials including behavioural data, personal data, customised settings in the software and so on.
This algorithm represents the middle ground between supervised and unsupervised algorithms. Use-cases may be found in legal and healthcare industries, among others, to manage Web content classification, image and speech analysis.
This learning represents the actual ML AI. Applications of reinforced learning algorithms are found in self-driving cars and AlphaGo programs.
Python is one of the most popular ML programming languages. It is suitable for ML algorithms because it has an easy syntax. Python has many libraries for various applications including ML. For example, SciPy and Numpy are great for linear algebra and getting to know kernel methods of ML. Other common libraries are Scikit-learn, Theano, TensorFlow, Keras, PyTorch, Pandas and Matplotlib.