In this tutorial we learn about python programming language.
What is Python
Python is an interpreted, interactive, object-oriented programming language. It incorporates modules, exceptions, dynamic typing, very high level dynamic data types, and classes.Python combines remarkable power with very clear syntax so it is easy to learn,read and maintain.
Python Example
Let’s have a quick look at python programming example.
print("Python World")
print("Python World")
Where it is used?
Python is used in many application domains.
Web and Internet Development
Python is widely used in scientific and numeric computing.
Machine learning is a subset of artificial intelligence and where we train and teach the machines (computers) by giving various algorithms and data to acquire some output.
“Machine learning is the science of getting computers to act without being explicitly programmed.” – Stanford.
Examples: Email spam filter and malware filters are powered by machine learning.
Types of Machine Learning there are three types of machine learning
1.Supervised
2.Unsupervised
3.Reinforcement
SupervisedLearning is method to enable machines to classify objects ,problem situation based on related data ( labeled data ) fed into the machines.It is the type of learning where we teach and train the machine using the data which is well-labeled.
Examples:
Classification: Machine is trained to classify something into some class.
classifying whether a patient has disease or not.
classifying whether an email is spam or not.
Regression: Machine is trained to predict some value like price, weight or height.
predicting house/property price.
predicting stock market price.
Unsupervised Learning is a method used to enable machines to classify both tangible and intangible objects without providing the machines any prior information about the objects. It is the type of learning where we teach and train the machine using the data which is not well-labeled.
Example:
Clustering: A clustering problem is where you want to discover the inherent groupings in the data.
such as grouping customers by purchasing behavior.
Association: An association rule learning problem is where you want to discover rules that describe large portions of your data.
such as people that buy X also tend to buy Y.
Reinforcement Learning is to develop a system (agent) that improves its performance based on interactions with the environment.
In reinforcement learning system(agent) perform some actions based on state of environment and get a reward(positive) or penalty(negative), and agent tries to maximize reward by series of interactions.
Examples: A popular example of reinforcement learning is chess game engine.
AI WALA 