# Placement papers

## Python for Beginners Video Tutorial

Python for Beginners Video Tutorial

## Big Data Analytics Video Tutorial

Big Data Analytics Video Tutorial

## Placement Preparation Video Tutorial

Placement Preparation Video Tutorial

## Wipro ELITE National Talent Hunt Hiring 2021

Wipro ELITE National Talent Hunt Hiring 2021 Opening for BE/B.Tech/5 Years Integrated M.Tech CS/ IT/ Circuital Branches 2021 Passing out only. ACADEMIC ELIGIBILITY CRITERIA Key Features & Eligibility criteria of the program are listed below: Qualification/ Degree: B.E./B.Tech / 5 years Integrated – M.Tech Branch of Study: CS/IT/Circuital Education: 10th Standard: 60% or above 12th

## 18ME33 Basic Thermodynamics – BTD – Notes

18ME33 Basic Thermodynamics ( BTD ) Notes Here you can download the VTU CBCS 2018 Scheme notes, Question papers, and Study materials of 18ME33 Basic Thermodynamics. University Name Visvesvaraya Technological University (VTU), Belagavi Branch Name Mechanical Engineering Semester 3rd Semester Subject Name 18ME33 Basic Thermodynamics – BTD Subject Code 18ME33 – BTD Scheme of Examination

## Machine Learning Question With Answers Module 5

17CS73 Machine Learning Question With Answers Module 5 MODULE 5 – EVALUATING HYPOTHESIS, INSTANCE BASED LEARNING, REINFORCEMENT LEARNING 1. Explain the two key difficulties that arise while estimating the Accuracy of Hypothesis. 2. Define the following terms a. Sample error b. True error c. Random Variable d. Expected value e. Variance f. standard Deviation 3.

## Machine Learning Question With Answers Module 4

17CS73 Machine Learning Question With Answers Module 4 MODULE 4 – BAYESIAN LEARNING 1. Define the Bayesian theorem? What is the relevance and features of the Bayesian theorem? Explain the practical difficulties of the Bayesian theorem. 2. Define is Maximum a Posteriori (MAP) Maximum Likelihood (ML) Hypothesis. Derive the relation for hMAP and hML using

## Machine Learning Question With Answers Module 3

17CS73 Machine Learning Question With Answers Module 3 MODULE 3 – ARTIFICIAL NEURAL NETWORKS 1. What is Artificial Neural Network? 2. Explain appropriate problem for Neural Network Learning with its characteristics. 3. Explain the concept of a Perceptron with a neat diagram. 4. Explain the single perceptron with its learning algorithm. 5. How a single

## Machine Learning Question With Answers Module 2

17CS73 Machine Learning Question With Answers Module 2 MODULE 2 – DECISION TREE LEARNING 1. What is decision tree and decision tree learning? 2. Explain representation of decision tree with example. 3. What are appropriate problems for Decision tree learning? 4. Explain the concepts of Entropy and Information gain. 5. Describe the ID3 algorithm for

## Machine Learning Question With Answers Module 1

17CS73 Machine Learning Question With Answers Module-1 MODULE 1 – INTRODUCTION AND CONCEPT LEARNING 1. Define Machine Learning. Explain with examples why machine learning is important. 2. Discuss some applications of machine learning with examples. 3. Explain how some disciplines have influenced machine learning. 4. What is well-posed learning problems. 5. Describe the following problems