Online Training in Bangalore

WHAT IS ARTIFICIAL INTELLIGENCE? Artificial Intelligence is an area of computer science that emphasizes the creation of intelligent machine that work and reacts like humans. WHAT IS AN ARTIFICIAL INTELLIGENCE NEURAL NETWORKS? Artificial intelligence Neural Networks can model mathematically the way biological brain works, allowing the machine to think and learn the same way the humans do- making them capable of recognizing things like speech, objects and animals like we do. WHAT ARE THE VARIOUS AREAS WHERE AI (ARTIFICIAL INTELLIGENCE) CAN BE USED? Artificial Intelligence can be used in many areas like Computing, Speech recognition, Bio-informatics, Humanoid robot, Computer software, Space and Aeronautics’s etc. WHICH IS NOT COMMONLY USED PROGRAMMING LANGUAGE FOR AI? Perl language is not commonly used programming language for AI WHAT IS PROLOG IN AI? In AI, Prolog is a programming language based on logic. GIVE AN EXPLANATION ON THE DIFFERENCE B

WHAT IS PYTHON? Python is a high-level, interpreted, interactive and object-oriented scripting language. Python is designed to be highly readable. It uses English keywords frequently where as other languages use punctuation, and it h as fewer syntactical constructions than other languages. WHAT ARE THE SUPPORTED DATA TYPES IN PYTHON? Python has five standard data types: Numbers String List Tuple Dictionary WHAT IS TUPLES IN PYTHON? A tuple is another sequence data type that is similar to the list. A tuple consists of a number of values separated by commas. Unlike lists, however, tuples are enclosed within parentheses. WHAT IS THE DIFFERENCE BETWEEN TUPLES AND LISTS IN PYTHON? The main differences between lists and tuples are − Lists are enclosed in brackets ( [ ] ) and their elements and size can be changed, while tuples are enclosed in parentheses ( ( ) ) and cannot be updated. Tuples can be thought of as read-only lists. WHAT ARE PYTHON'

CHAPTER 1:INTRODUCTION In this course you will learn to implement mathematical ideas in machine learning. You will investigate the process of learning and understand the application of various learning algorithms. Prerequisites: The courses assignments and notes will use python programming language and expects a basic knowledge of python. We assume the student has completed the Machine Learning Foundations or has an equivalent fluency in mathematics and fundamentals. CHAPTER 2: LINEAR MODELS Understand linear approximation and modelling of problems and develop linear models CHAPTER 3: DIMENSIONALITY REDUCTION Use ideas from linear algebra to transform dimensions and warp space providing additional flexibility and functionality to linear models. CHAPTER 4:SVM Develop and implement kernel based methods to develop nonlinear models to solve few complex tasks. CHAPTER 5:NEAREST NEIGHBOURS, K-MEANS, AND GAUSSIAN MIXTURE MODELS Review pattern recognition ideas with

CHAPTER 1:INTRODUCTION An Introduction to Python CHAPTER 2:BEGINNING PYTHON BASICS The print statement Comments Python Data Structures & Data Types String Operations in Python Simple Input & Output Simple Output Formatting CHAPTER 3:PYTHON PROGRAM FLOW Indentation The If statement and its’ related statement An example with if and it’s related statement The while loop The for loop The range statement Break & Continue Assert Examples for looping CHAPTER 4:FUNCTIONS & MODULES Create your own functions Functions Parameters Variable Arguments Scope of aFunction FunctionDocumentation/Docstrings Lambda Functions & map An Exercise with functions Create a Module Standard Modules CHAPTER 5:EXCEPTIONS Errors Exception Handling with try Handling Multiple Exceptions Writing your own Exceptions >> Read more >>