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.

Interview was basic and easy. You can clear it with aptitude, reasoning, and programming skills. Practicing more problems on HackerRank will help you solve challenges effectively with consistent practice daily.