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Machine learning is a subset of artificial intelligence that focuses on developing algorithms to make predictions based on data, while deep learning is a subset of machine learning that uses neural networks to learn from large amounts of data.
Machine learning is a broader concept that encompasses various techniques such as decision trees, support vector machines, and random forests.
Deep learning specifically refers to ...
Supervised machine learning in modern businesses includes applications in customer segmentation, fraud detection, recommendation systems, and predictive analytics.
Customer segmentation for targeted marketing campaigns
Fraud detection in financial transactions
Recommendation systems for personalized product suggestions
Predictive analytics for forecasting sales or demand
Sentiment analysis for understanding customer feedbac
Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.
Supervised learning requires a target variable to be predicted, while unsupervised learning does not.
In supervised learning, the model learns from labeled examples provided in the training data, while in unsupervised learning, the model finds patterns and relationships in the data without guidance.
Examples of super...
Reinforcement learning in chess involves training a system to make optimal moves based on rewards and penalties.
The system starts by randomly exploring different moves and receives rewards or penalties based on the outcome.
Over time, the system learns to make better moves by maximizing rewards and minimizing penalties.
Examples of reinforcement learning in chess include AlphaZero and Stockfish, which use neural networks
Decision Tree Classifier is a machine learning algorithm that creates a tree-like model of decisions based on features.
Uses tree-like structure of decisions to classify data
Easy to interpret and visualize
Can handle both numerical and categorical data
Can handle multi-output problems
Prone to overfitting if not pruned properly
Clustering in data mining is the process of grouping similar data points together based on certain criteria.
Clustering is an unsupervised learning technique used to discover hidden patterns or structures in data.
It helps in organizing data into meaningful groups without any prior knowledge of the groupings.
Examples of clustering algorithms include K-means, Hierarchical clustering, and DBSCAN.
Applications of clustering ...
Common tools used in Big Data include Hadoop, Spark, Kafka, and SQL databases.
Hadoop: Distributed storage and processing framework for large data sets.
Spark: In-memory data processing engine for speed and ease of use.
Kafka: Distributed streaming platform for handling real-time data feeds.
SQL databases: Traditional relational databases used for structured data storage and querying.
I appeared for an interview in Feb 2025.
Women Entrepreneurship development
Land is a tangible, non-depreciable asset that serves as a foundational resource for various economic activities.
Land is classified as a fixed asset in accounting, meaning it is not easily liquidated.
It is considered a non-depreciable asset because it does not lose value over time like buildings or machinery.
Land can be used for various purposes, such as agriculture, residential, commercial, or industrial development.
E...
Principles of accounting are fundamental guidelines that govern financial reporting and recording.
Accrual Principle: Revenue is recognized when earned, not when received. Example: A service performed in December is recorded in December, even if payment is received in January.
Consistency Principle: Businesses should use the same accounting methods over time. Example: If a company uses straight-line depreciation, it shou...
Bookkeeping is the systematic recording and organizing of financial transactions for a business or individual.
Involves tracking income and expenses to maintain accurate financial records.
Utilizes methods like double-entry bookkeeping, where each transaction affects two accounts.
Examples include recording sales, purchases, receipts, and payments.
Essential for preparing financial statements, tax returns, and budgeting.
Ca...
I appeared for an interview in Feb 2025.
Our commitment to excellence, innovation, and collaboration makes us the ideal choice for aspiring academics.
Strong research opportunities: We encourage faculty to pursue groundbreaking research, exemplified by recent grants awarded to our team.
Collaborative environment: Our department fosters teamwork, as seen in interdisciplinary projects that have led to significant advancements.
Commitment to student success: We pri...
I possess the necessary qualifications, experience, and passion for teaching and research in this academic role.
Strong academic background: I hold a Ph.D. in my field, which equips me with in-depth knowledge.
Teaching experience: I have taught undergraduate courses, receiving positive feedback from students for my engaging teaching style.
Research contributions: I have published several papers in reputable journals, demo...
I appeared for an interview in Jan 2025.
I have a basic aptitude test, and I also possess fundamental knowledge regarding the subject.
Echelon Institute of Technology interview questions for popular designations
I appeared for an interview in Sep 2024.
I appeared for an interview in Sep 2024.
I appeared for an interview in Jan 2025.
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