machine learning

Year Major SubjectCode Unit Chapter Section QuestionType BTLevel COs DifficultyLevel Question Mark 2021 BIT 19ITEN2007 1 1 A Descriptive Remember CO1 Easy Define Machine Learning and List the real-life applications of ML algorithms 2 2021 BIT 19ITEN2007 1 1 A Descriptive Understanding CO1 Moderate Mention two methods by which we can replace NaN values from the Dataframe in Pandas. 2 2021 BIT 19ITEN2007 1 1 A Descriptive Understanding CO1 Easy Differentiate between supervised and unsupervised learning with example. 2 2021 BIT 19ITEN2007 1 1 A Descriptive Understanding CO1 Difficult Justify on how semi-supervised learning use both labeled and unlabeled data for model training 2 2021 BIT 19ITEN2007 1 1 A Descriptive Remember CO1 Easy Define Reinforcement with real time examples. How is it different from other ML techniques. 2 2021 BIT 19ITEN2007 1 2 A Descriptive Remember CO1 Easy Mention the main categories of algorithms included in Scikit-Learn 2 2021 BIT 19ITEN2007 1 2 A Descriptive Remember CO1 Moderate Enumerate the concept of pipeline in Scikit-Learn 2 2021 BIT 19ITEN2007 1 2 A Descriptive Remember CO1 Moderate Mention the different types of Data Structures in Pandas. 2 2021 BIT 19ITEN2007 1 2 A Descriptive Apply CO1 Moderate What are the different ways in which a dataframe can be created? 2 2021 BIT 19ITEN2007 1 2 A Descriptive Apply CO1 Moderate How can you reverse a NumPy array? 2 2021 BIT 19ITEN2007 1 1 B Descriptive Understanding CO1 Easy Explain the different types of machine learning and its applications. 15 2021 BIT 19ITEN2007 1 2 B Descriptive Apply CO1 Moderate Describe the essential libraries and tools required for performing classification tasks in machine learning.Provide insights into how each component contributes to the classification workflow and its role in real-world scenarios. 15 2021 BIT 19ITEN2007 1 1 B Descriptive Apply CO1 Easy Explain pandas contribution in machine learning tasks. Discuss any five functions of pandas library. 8 2021 BIT 19ITEN2007 1 2 B Descriptive Apply CO1 Moderate Identify and explain the essential functions in NumPy required for running machine learning models. 7 2021 BIT 19ITEN2007 2 1 A Descriptive Understanding CO2 Easy Provide a concise definition of supervised learning. 2 2021 BIT 19ITEN2007 2 1 A Descriptive Understanding CO2 Easy Compare and contrast between overfitting and underfitting 2 2021 BIT 19ITEN2007 2 1 A Descriptive Understanding CO2 Easy Outline the three stages of building a Model in Machine Learning 2 2021 BIT 19ITEN2007 2 1 A Descriptive Understanding CO2 Easy Define Generalization with example 2 2021 BIT 19ITEN2007 2 1 A Descriptive Remember CO2 Easy Mention the applications of Supervised Machine Learning in Modern Businesses 2 2021 BIT 19ITEN2007 2 2 A Descriptive Understanding CO2 Easy Define the term 'naive' in the Naive Bayes Classifier. 2 2021 BIT 19ITEN2007 2 2 A Descriptive Understanding CO2 Easy Define pruning in Decision Trees, and how it works? 2 2021 BIT 19ITEN2007 2 2 A Descriptive Understanding CO2 Moderate Give a valid reason for the the Trade-off Between Bias and Variance 2 2021 BIT 19ITEN2007 2 2 A Descriptive Remember CO2 Moderate Outline the primary objective of Support Vector Machines (SVM) 2 2021 BIT 19ITEN2007 2 2 A Descriptive Understanding CO2 Moderate Is Naive Bayes is a discriminative classifier or generative classifier? Justfy your answer. 2 2021 BIT 19ITEN2007 2 1 B Descriptive Apply CO2 Moderate Illustrate in detail about the working of K-Nearest Neighbour(KNN) with its underlying mathematics. Also write a code snippet demonstrating the implementation of the k-Nearest Neighbors (KNN) algorithm for classification. 15 2021 BIT 19ITEN2007 2 2 B Descriptive Understanding CO2 Moderate Discuss the differences between the two approaches in terms of their construction, decision-making process, and performance of Decision Tree classifier and an ensemble of Decision Trees in detail 15 2021 BIT 19ITEN2007 2 2 B Descriptive Understanding CO2 Easy Explain the Naïve Bayes Classifier and its implementation using python 8 2021 BIT 19ITEN2007 2 1 B Descriptive Understanding CO2 Easy Differentiate between Classification and Regression. 7 2021 BIT 19ITEN2007 3 1 A Descriptive Remember CO3 Easy Mention few common machine learning problems that are solved using Unsupervised algorithm 2 2021 BIT 19ITEN2007 3 1 A Descriptive Remember CO3 Easy List the types of unsupervised learning. 2 2021 BIT 19ITEN2007 3 1 A Descriptive Understanding CO3 Moderate Outline the challenges of in unsupervised learning. 2 2021 BIT 19ITEN2007 3 1 A Descriptive Understanding CO3 Moderate How does data normalization contribute to improving the performance of machine learning models? 2 2021 BIT 19ITEN2007 3 1 A Descriptive Apply CO3 Difficult Enumerate on how to handle missing values in a dataset during the preprocessing stage of data analysis? 2 2021 BIT 19ITEN2007 3 1 B Descriptive Apply CO3 Easy Describe the various types of data preprocessing. Provide detailed explanations for each technique, including how they work, their advantages, and their limitations. 15 2021 BIT 19ITEN2007 3 1 B Descriptive Understanding CO3 Moderate Describe the fundamental concepts and applications of unsupervised learning algorithms in detail. 8 2021 BIT 19ITEN2007 3 1 B Descriptive Understanding CO3 Difficult Discuss the various types of scaling techniques used in data analysis and machine learning in detail. 7