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.