What is Machine Learning?
Answer: Machine learning is a branch of computer science which deals with system programming in order to automatically learn and improve with experience. For example: Robots are programed so that they can perform the task based on data they gather from sensors. It automatically learns programs from data.
The simplest way to answer this question is - we give the data and equation to the machine. Ask the machine to look at the data and identify the coefficient values in an equation.
For example for the linear regression y=mx+c, we give the data for the variable x, y and the machine learns about the values of m and c from the data.
Modern formal definition of machine learning according to Tom Mitchell.
A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
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Machine Learning
- Give a popular application of machine learning that you see on a day-to-day basis?
- What is Genetic Programming?
- In what areas is pattern recognition used?
- What are the advantages of Naive Bayes?
- What is a classifier in machine learning?
- What is the difference between artificial learning and machine learning?
- What is algorithm independent machine learning?
- Explain what is the function of 'Supervised Learning'?
- What is the function of unsupervised learning?
- What is 'Training set' and 'Test set'?
- What is the standard approach to supervised learning?
- What are the three stages to build the hypotheses or model in machine learning?
- What are the different Algorithm techniques in Machine Learning?
- What are the five popular algorithms of Machine Learning?
- What is inductive Machine Learning?
- How can you avoid overfitting?
- Why does overfitting happen?
- What is 'Overfitting' in Machine learning?
- Mention the difference between Data Mining and Machine learning?
- Give a derivation of for a single example in batch gradient descent? (Gradient Descent For Linear Regression)
- What is the algorithm for implementing gradient descent for linear regression?
- How does gradient descent converge with a fixed step size alpha?
- Why should we adjust the parameter alpha when using gradient descent?
- Why does gradient descent, regardless of the slope's sign, eventually converge to its minimum value?