Programming, AI, Machine Learning & Software Development Tutorials
After interviewing 50+ AI Engineer candidates, I noticed a pattern: impressive GenAI projects can get you through the first 10 minutes—but strong fundamentals are what separate candidates who build AI systems from those who truly understand them. I’ve interviewed 50+ AI Engineer candidates over the past few months. Almost every resume looked impressive. RAG. AI … Read more
In Part 1, we covered the fundamentals of Spark, PySpark, Databricks, DAGs, lazy evaluation, partitioning, data skew, salting, AQE, migration validation, and common PySpark coding questions. But experienced Data Engineer interviews usually go one level deeper. Interviewers want to know: Can you actually design, optimize, and troubleshoot a production data platform? In this article, we’ll cover … Read more
The last Ensemble method we will discuss in this series is called stacking (short for stacked generalization). It is based on a simple idea: instead of using trivial functions (such as hard voting) to aggregate the predictions of all predictors in an ensemble, why don’t we train a model to perform this aggregation? Figure below … Read more
In many coding interviews and real-world applications, finding the top ( k ) largest elements in an array is a common problem. This tutorial will guide you through three popular methods to solve this problem: Sorting, Min-Heap, and the Quick select algorithm. We’ll focus on the Min-Heap approach due to its efficiency and practical use … Read more
Introduction: In the world of string manipulation, unraveling patterns and matching sequences play a pivotal role in various applications. In this tutorial, we’ll embark on a journey to decode word patterns, where we’ll determine if two strings follow the same pattern. Through the lens of Java programming, we’ll explore an efficient approach to tackle this … Read more
The text discusses the curse of dimensionality in machine learning, highlighting challenges in high-dimensional spaces. It suggests reducing features to improve training efficiency and visualization, while addressing potential information loss and risks of overfitting with increased dimensions. Dimensionality reduction techniques will be explored further.
As we have discussed, a Random Forest is an ensemble of Decision Trees, generally trained via the bagging method (or sometimes pasting), typically with max_samples set to the size of the training set. Instead of building a BaggingClassifier and passing it a DecisionTreeClassifier, you can instead use the RandomForestClassifier class, which is more convenient and … Read more