Vector and Matrices
This section covers concepts related to Vector and Matrices
Interview Preparation Corner
Unsupervised Learning
This section covers concepts from Dimensionality Reduction, Autoencoders, PCA, Clustering, KMeans, Recommendation System topics
Tree Based Methods
This section contains questions from Decision Trees, Bagging, Boosting, Random Forest, XGBoost, Adaboost, Gradient Boosting
Support Vector Machines
This Q&A covers fundamental concepts of Support Vector Machines (SVM) and Support Vector Regression (SVR). It includes topics such as the maximal margin classifier, hyperplanes, margin optimization, support vectors, and kernel functions. Additionally, it explores the differences between SVM and SVR, including their applications, loss functions, hyperparameters, and how they handle non-linear boundaries and outliers.
SQL
This section covers key SQL topics like data retrieval, string manipulation, joins, subqueries, sorting, filtering, and aggregation using functions like COUNT and GROUP BY. It also explores advanced operations like ranking, conditional logic, and handling duplicates, essential for data analysis.
Probabilistic Modeling
Questions from Naive Bayes
Probability
Questions from probability, conditional probability, bayes theorem, total probability etc
Performance Metrics
Covers questions from Regression Metrics, Classification Metrics, Loss Functions, Objective Functions, Clustering Metrics
Natural Language Processing
Contains questions from Tokenization, Tf-Idf, Embeddings, Other NLP concepts
Linear Methods For Regression
Covers questions from Linear Regression, Bias and Variance, Regularization, Feature Selection etc.
Linear Methods For Classification
Logistic Regression
Information Theory
This section covers KL-Divergence Theorem, Entropy, Information Gain, Gini Impurity etc.
Inferential Statistics
This section covers Point Estimation, Interval Estimation, Hypothesis Testing and Inference for Relationship
General Concepts In Neural Networks
This section covers Neural Networks, Activation Function, Dropout, Regularization and other crucial topics related to neural nets
General Concepts In Machine Learning
Contains questions from Bias and Variance, Cross Validation, Sampling Techniques etc
Descriptive Statistics
Mean, Median, Variance, Data distribution etc.
Convolution Neural Networks
This is a comprehensive set of questions related to Convolutional Neural Networks (CNNs), covering various aspects from convolution operations to fine-tuning pre-trained models and neural style transfer. Each question is well-defined, and the use of images and code snippets adds depth to the understanding of the concepts.
Computer Science Questions
This section covers key computer science concepts, including recursive functions, sorting algorithms, and subsequence problems. It also explores matrix multiplication optimization, numerical stability in deep learning, and the benefits of GPUs versus TPUs. Additionally, it addresses practical challenges like dynamic memory management and file similarity filtering.
Calculus And Algorithmic Differentiation
Contains questions from calculus and differentiation