Machine Learning Introduction Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.
History of Machine Learning <1950s- Statistical methods are discovered and refined. 1950s- Pioneering machine learning research is conducted using simple algorithms. 1960s- Bayesian methods are introduced for probabilistic inference in machine learning. 1970s- AI Winter caused by pessimism about machine learning effectiveness. 1980s- The discovery of backpropagation causes a resurgence in machine learning research.
1990s- Work on Machine learning shifts from a knowledge-driven approach to a data-driven approach. Scientists begin creating programs for computers to analyse large amounts of data and draw conclusions – or "learn" – from the results.[ Support vector machines (SVMs) and Recurrent Neutral Networks (RNNs) become popular. The fields of computational complexity via neural networks and super-Turing computation started. 2000s- Support Vector Clustering and other Kernel methods and unsupervised machine learning methods become widespread. 2010s- Deep learning becomes feasible, which leads to machine learning becoming integral to many widely used software services and applications.
Importance of Machine Learning Data is very important for all the businesses. Data-driven decisions increasingly make the difference between keeping up with competition or falling further behind. Machine learning can be the key to unlock the value of corporate and customer data and enacting decisions that keep a company ahead of the competition. The machine learning field is continuously evolving. And along with evolution comes a rise in demand and importance.
Types of Machine Learning • Supervised ML- Past data is used to make predictions in supervised machine learning. • Unsupervised ML- Unsupervised machine learning finds hidden patterns. • Reinforcement ML- Reinforcement machine learning is used for improving or increasing efficiency.
Applications of Machine Learning Machine Learning is applicable in many fields such as: • Manufacturing- Predictive maintenance and condition monitoring • Retail- Upselling and cross-channel marketing • Healthcare and life sciences- Disease identification and risk satisfaction • Travel and hospitality- Dynamic pricing • Financial services- Risk analytics and regulation • Energy- Energy demand and supply optimization
Popular Machine Learning Tools The most used Machine Learning tools are: Knime, Accord.net, ScikitLearn, TensorFlow, Weka, Pytorch, RapidMiner, Google Cloud AutoML, Jupyter Notebook, Apache Mahout, Azure machine learning studio, MLLIB, Orange3, IBM Watson, Pylearn2 etc.
Popular Machine Learning Software • Python: a popular language with high-quality machine learning and data analysis libraries • C++: a middle-level language used for parallel computing on CUDA • R: a language for statistical computing and graphics
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