Machine Learning
What is Machine Learning?? 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.
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.
Popular Machine Learning Tools • Knime • Accord.net • Scikit-Learn • TensorFlow • Weka • Pytorch • RapidMiner, • Google Cloud AutoML • Jupyter Notebook • Apache Mahout 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
Applications of Machine Learning • 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
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