nterview Preparation Guide
1. Core ML & Data Skills

Data Preprocessing

Be ready to talk about handling missing values, categorical encoding (one-hot, label), feature scaling, and outlier detection.

Example: “How would you handle an imbalanced dataset?”

EDA (Exploratory Data Analysis)

Be able to describe how you would summarize a dataset using Pandas/Matplotlib/Seaborn.

Practice explaining insights with visuals (e.g., correlation heatmaps, distribution plots).

2. Algorithms & ML Models

Be comfortable with basics of:

Supervised Learning → Linear/Logistic Regression, Decision Trees, Random Forest, SVM.

Unsupervised Learning → K-Means, Hierarchical Clustering, PCA.

Deep Learning → CNNs (for images), RNN/LSTMs (for sequences), Transformers (for NLP).

Practice Questions:

“When would you prefer Random Forest over Logistic Regression?”

“How do you prevent overfitting in deep learning?”

3. Frameworks & Tools

Scikit-learn → preprocessing, pipelines, model training, evaluation.

TensorFlow / PyTorch → writing and training deep learning models.

MLOps (basic awareness) → version control for models/data, deployment concepts, Docker, CI/CD.

4. Trending AI (Good to Know)

OCR (Optical Character Recognition) → Applications: document processing, invoice extraction.

Mention Tesseract, EasyOCR, or deep learning-based OCR models.

LLMs (Large Language Models) → GPT, BERT, LLaMA.

Know fine-tuning concepts and embeddings.

SLMs (Small Language Models) → lightweight models optimized for edge devices.

5. Soft Skills & Documentation

They emphasize documentation & reproducibility → mention Jupyter Notebooks, GitHub, README, code comments.

Show team collaboration → highlight group projects or Git-based contributions.

6. What You Can Say in Interview

When they ask “Why Archlynk / Why this role?”, you can say:

You want to gain hands-on exposure to the full ML lifecycle (EDA → modeling → deployment).

You are excited about working on real-world datasets and scalable ML solutions.

You want mentorship and industry experience with trending AI areas like LLMs and OCR.

Question

nterview Preparation Guide
1. Core ML & Data Skills

Data Preprocessing

Be ready to talk about handling missing values, categorical encoding (one-hot, label), feature scaling, and outlier detection.

Example: “How would you handle an imbalanced dataset?”

EDA (Exploratory Data Analysis)

Be able to describe how you would summarize a dataset using Pandas/Matplotlib/Seaborn.

Practice explaining insights with visuals (e.g., correlation heatmaps, distribution plots).

2. Algorithms & ML Models

Be comfortable with basics of:

Supervised Learning → Linear/Logistic Regression, Decision Trees, Random Forest, SVM.

Unsupervised Learning → K-Means, Hierarchical Clustering, PCA.

Deep Learning → CNNs (for images), RNN/LSTMs (for sequences), Transformers (for NLP).

Practice Questions:

“When would you prefer Random Forest over Logistic Regression?”

“How do you prevent overfitting in deep learning?”

3. Frameworks & Tools

Scikit-learn → preprocessing, pipelines, model training, evaluation.

TensorFlow / PyTorch → writing and training deep learning models.

MLOps (basic awareness) → version control for models/data, deployment concepts, Docker, CI/CD.

4. Trending AI (Good to Know)

OCR (Optical Character Recognition) → Applications: document processing, invoice extraction.

Mention Tesseract, EasyOCR, or deep learning-based OCR models.

LLMs (Large Language Models) → GPT, BERT, LLaMA.

Know fine-tuning concepts and embeddings.

SLMs (Small Language Models) → lightweight models optimized for edge devices.

5. Soft Skills & Documentation

They emphasize documentation & reproducibility → mention Jupyter Notebooks, GitHub, README, code comments.

Show team collaboration → highlight group projects or Git-based contributions.

6. What You Can Say in Interview

When they ask “Why Archlynk / Why this role?”, you can say:

You want to gain hands-on exposure to the full ML lifecycle (EDA → modeling → deployment).

You are excited about working on real-world datasets and scalable ML solutions.

You want mentorship and industry experience with trending AI areas like LLMs and OCR.

Wipro