Data Science and Machine Learning Interview Handbook

This course equips you with practical skills to ace data science and machine learning interviews. You’ll begin with real-world datasets, including structured, unstructured, time series, text, and images, and learn key techniques for collecting and querying data using APIs, SQL, and web scraping. Next, you’ll cover data preprocessing workflows: cleaning, normalization, handling missing data, feature engineering, and managing outliers. You’ll then apply supervised learning methods like regression, decision trees, SVM, Naive Bayes, and unsupervised techniques such as k-means, hierarchical clustering, and PCA. The course also covers advanced topics, including ensemble methods, regularization, hyperparameter tuning, and the fundamentals of deep learning. You’ll explore real-world applications in health care, finance, and autonomous systems. Finally, you’ll practice with case studies, model deployment strategies, fairness and privacy in AI, and mock interview practice to make you industry-ready.