ML Engineer Job Prep

Last updated Jun 23, 2022

# Computer Science Fundamentals and Programming

Data 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.

Basic 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 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.

Models: 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 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.