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Introduction
Message from the Writer
Basic Introduction
- Why should we learn Machine Learning?
Answer
Machine Learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
- How to learn Machine Learning?
Answer
You can learn Machine Learning from CodeXam. CodeXam is a platform where you can learn Machine Learning from the basics. You can also contribute to the Machine Learning Tutorial.
- Definition of Machine Learning:
Answer
Definition: A computer program which learns from experience is called a machine learning program or simply a learning program .
- Definition of learning:
Answer
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 T, as measured by P , improves with experience E.
Examples :
- Handwriting recognition learning problem :
→ T(task) = Recognizing and classifying handwritten words within images ,
→ P(performance measure) = Fraction of words correctly classified ,
→ E(training experience) = A set of images of handwritten words and their correct classifications.
- Machine driving learning problem :
→ T(task) = Driving a tesla car using vision sensors ,
→ P(performance measure) = Driving the car without hitting any obstacles ,
→ E(training experience) = A set of images of the road and the correct steering angles.
Classification of Machine Learning Algorithms
Machine learning algorithms are classified into four main categories:
- Supervised Learning - In simple words it is a learning in which we have a dataset and we have to predict the output for a new data.
- Unsupervised Learning - In simple words it is a learning in which we have a dataset and we have to find the hidden patterns in the dataset.
- Reinforcement Learning - In simple words it is a learning in which we have a dataset and we have to find the best possible action for a given state.
- Semi-supervised Learning - In simple words it is a learning in which we have a dataset and we have to predict the output for a new data.
We will discuss each of these categories in detail later in this tutorial. Now let's turn on the chapter 2.