Ritika Securities Head Learning & Development Interview Questions and Answers

Q1. What is evaluation Matrix for classification
Ans. 
Evaluation metrics for classification are used to assess the performance of a classification model.
• Common evaluation metrics include accuracy, precision, recall, F1 score, and ROC-AUC.

• Accuracy measures the proportion of correctly classified instances out of the total instances.

• Precision measures the proportion of true positive predictions out of all positive predictions.

• Recall measures the proportion of true positive p...read more
Answered by AI
Add your answer
Q2. What L1 and L2 regression
Ans. 
L1 and L2 regression are regularization techniques used in machine learning to prevent overfitting.
• L1 regression adds a penalty equivalent to the absolute value of the magnitude of coefficients.

• L2 regression adds a penalty equivalent to the square of the magnitude of coefficients.

• L1 regularization can lead to sparse models, while L2 regularization tends to shrink coefficients towards zero.

• L1 regularization is also know...read more
Answered by AI
Add your answer
Q3. Explain random forest algorithm
Ans. 
Random forest is an ensemble learning algorithm that builds multiple decision trees and combines their predictions.
• Random forest creates multiple decision trees using bootstrapping and feature randomization.

• Each tree in the random forest is trained on a subset of the data and features.

• The final prediction is made by averaging the predictions of all the trees (regression) or taking a majority vote (classification).
Answered by AI
Add your answer

Q1. Tell me about self
Ans. 
I am a dedicated and passionate Machine Learning Engineer with a strong background in computer science and data analysis.
• Experienced in developing machine learning models for various applications

• Proficient in programming languages such as Python, R, and Java

• Skilled in data preprocessing, feature engineering, and model evaluation

• Strong understanding of algorithms and statistical concepts

• Excellent problem-solving and ana
Answered by AI
Add your answer
Q2. Questions about salary discuss
Add your answer

Q1. Screening round: Tell me about your experiences?
Add your answer
Q2. Tell me Learning and development challenges you faced and improvements you have done
Add your answer

Genral and technical aptitude test

Q1. Overall experience?
Add your answer
Q2. What was challenging role in previous org?
Add your answer
Q3. How you see L&D from your perspective?
Add your answer
Q4. How you tackle problems in your role?
Add your answer
Q5. How will you onboard 500 candidates every month?
Ans. 
By creating a structured onboarding process, utilizing technology for efficiency, and leveraging a team of trainers.
• Develop a comprehensive onboarding program with clear objectives and timelines.

• Utilize technology such as online training modules and virtual onboarding sessions.

• Assign a team of trainers to handle different aspects of the onboarding process.

• Implement a buddy system where existing employees mentor new hir...read more
Answered by AI
Add your answer

Q1. Basic Stats questions
Add your answer
Q2. Basic ML questions
Add your answer

Q1. Describe about yourself
Ans. 
I am a passionate and experienced Learning & Development Specialist with a strong background in designing and delivering effective training programs.
• Over 5 years of experience in creating engaging learning materials

• Skilled in conducting needs assessments and developing training plans

• Proficient in utilizing various instructional design methodologies

• Strong communication and presentation skills

• Proven track record of impro...read more
Answered by AI
Add your answer
Q2. Why is this job
Add your answer

Q1. Started with basic os or network fundamentals like analyzing a core dump, handling tcp packet loss, difference between rest api and grpc etc. Then, from ML started with basic questions like bias variance t...read more
Add your answer

Q1. Projects we have done earlier
Ans. 
We have worked on various projects involving image recognition, natural language processing, and predictive analytics.
• Image recognition: Developed a model to classify different types of fruits based on images.

• Natural language processing: Created a sentiment analysis tool for customer reviews.

• Predictive analytics: Built a model to forecast sales based on historical data.
Answered by AI
Add your answer
Q2. Aptitude questions
Add your answer

Q1. Shift timings and salary discussion
Add your answer

Q1. Details explain about project
Add your answer
Q2. Explain Module in ml
Ans. 
A module in machine learning is a self-contained unit that performs a specific task or function.
• Modules can include algorithms, data preprocessing techniques, evaluation metrics, etc.

• Modules can be combined to create a machine learning pipeline.

• Examples of modules include decision trees, support vector machines, and k-means clustering.
Answered by AI
Add your answer

Q1. Questions from the resume
Add your answer