ITI Intern Interview Questions and Answers

Easy to crack the interview DSA

Q1. What are ur skills and explain ur projects?
Ans. 
I have strong skills in programming, data analysis, and project management. I have completed projects in web development, machine learning, and database management.
• Programming: Proficient in languages such as Python, Java, and JavaScript.

• Data Analysis: Experience with tools like Excel, SQL, and Tableau.

• Project Management: Successfully led a team to develop a web application for a local business.

• Web Development: Created...read more
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1 hrs of round consisting of both output and programing writing questions ans dome of the general apptitude

Q1. Questions on C and Sql
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Some coding questions were asked

Q1. DSA, OS, OOP were asked
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Q1. Simple and calm round , it was through college
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Coding round - OS, bit manipulation etc

Q1. What is hyperparameter tuning?
Ans. 
Hyperparameter tuning is the process of selecting the best set of hyperparameters for a machine learning model.
• Hyperparameters are parameters that are set before the learning process begins.

• Hyperparameter tuning involves adjusting hyperparameters to optimize the model's performance.

• Common techniques for hyperparameter tuning include grid search, random search, and Bayesian optimization.
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Q2. How do we train neural networks?
Ans. 
Neural networks are trained using algorithms that adjust the weights and biases of the network based on the input data and desired output.
• Neural networks are trained using a process called backpropagation, where the error between the predicted output and the actual output is used to adjust the weights and biases of the network.

• Training data is fed into the neural network, and the network's output is compared to the des...read more
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Q3. What is overfitting?
Ans. 
Overfitting occurs when a machine learning model learns the training data too well, including noise and outliers, leading to poor generalization on new data.
• Overfitting happens when a model is too complex and captures noise in the training data.

• It can be identified when a model performs well on training data but poorly on unseen data.

• Techniques to prevent overfitting include cross-validation, regularization, and early ...read more
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