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Type of Machine Learning Machine Learning Deep Learning Underlying Technique Statistical Methods & Math Models Artificial Neural Networks Data Requirements Smaller Datasets Large Datasets Human Intervention More Human Feature Engineering Automatic Feature Extraction (often) Applications Spam Filtering, Stock Prediction Image Recognition, Natural Language Processing

AI includes everything from smart assistants like Alexa to robotic vacuum cleaners and self-driving cars. Machine learning (ML) is one among many other branches of AI. ML is the science of developing algorithms and statistical models that computer systems use to perform complex tasks without explicit instructions.

Machine learning is important because it gives enterprises a view of trends in customer behavior and operational business patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google, and Uber, make machine learning a central part of their

Here are examples of machine learning at work in our daily life that provide value in many ways—some large and some small.1.Facial recognition. 2. Product recommendations. 3. Email automation and spam filtering. 4. Financial accuracy. 5. Social media optimization. 6. Healthcare advancement. 7. Mobile voice to text and predictive text.

Regularization is a set of methods for reducing overfitting in machine learning models. Typically, regularization trades a marginal decrease in training accuracy for an increase in generalizability. Regularization encompasses a range of techniques to correct for overfitting in machine learning models.