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Jun 23, 2022

# Computer Science Fundamentals and ProgrammingData structures : Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc.Algorithms : Recursion, searching, sorting, optimization, dynamic programming, etc.Computability and complexity : P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc.Computer architecture : Memory, cache, bandwidth, threads & processes, deadlocks, etc.# Probability and StatisticsBasic probability : Conditional probability, Bayes rule, likelihood, independence, etc.Probabilistic models : Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc.Statistical measures : Mean, median, mode, variance, population parameters vs. sample statistics etc.Proximity and error metrics : Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc.Distributions and random sampling : Uniform, normal, binomial, Poisson, etc.Analysis methods : ANOVA, hypothesis testing, factor analysis, etc.# Data Modeling and EvaluationData preprocessing : Munging/wrangling, transforming, aggregating, etc.Pattern recognition : Correlations, clusters, trends, outliers & anomalies, etc.Dimensionality reduction : Eigenvectors, Principal Component Analysis, etc.Prediction : Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics.Evaluation : Training-testing split, sequential vs. randomized cross-validation, etc.# Applying Machine Learning Algorithms and LibrariesModels : Parametric vs. non-parametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc.Learning procedure : Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc.Tradeoffs and gotchas : Relative advantages and disadvantages, bias and variance, overfitting and underfitting vanishing/exploding gradients, missing data, data leakage, etc.# Software Engineering and System DesignSoftware interface : Library calls, REST APIs data collection endpoints, database queries, etc.User interface : Capturing user inputs & application events, displaying results & visualization, etc.Scalability : Map-reduce, distributed processing, etc.Deployment : Cloud hosting, containers & instances, microservices etc.# Resources