Data Science, Machine Learning and Artificial Intelligence using Python part-1

What is Machine Learning?

Ans: Machine Learning is the science of programming computers so they can learn from data.
Machine Learning is a field of computer science that uses statistical techniques to give computer systems the ability to learn(ie. progressively improve performance on a specific task) with data, without being explicitly programmed.

Example:
An E-mail spam filter is an ML program that can learn to flag spam given examples of spam emails and examples of non-spam/ham emails.

"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."

Types of Machine Learning

• Supervised learning
• Unsupervised Learning
• Semi-supervised Learning
• Reinforcement Learning

Supervised Learning: In this learning, the training data you feed to the algorithm includes the labels/outcome(dependent variable to predict). you show the model during the training phase what are the right examples.

The Dependent variable may be :

1)Categorical(Classification problem)

2)Continuous(Regression Problem)

Unsupervised learning: In this learning, the sample data is unlabelled ie. no outcome to predict. An example of this learning is - clustering, dimensionality reduction, etc. here, there is no right example against which the accuracy of the model can be judged. 

Training and Test sample

The total sample is randomly split into training and test samples.

The training sample provides the data from which the predictive model is generated.

The test sample provides us a generalization assessment of the performance of our predictive models to unseen data.

This is just beginning the topic will be continued.......