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### How Did I Become A Machine Learning Engineer: Follow Cheat Sheet

**If you are interested in pursuing a career in machine learning and don't know where to start, here's your go-to guide for the best programming languages and skills to learn, interview questions, salaries, and more.**

- Software engineer, machine learning: Computer science fundamentals and programming, and software engineering and system design
- Applied machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries
- Core machine learning engineer: Computer science fundamentals and programming, applying machine learning algorithms and libraries, data modeling, and evaluation

- Computer science fundamentals and programming: Data structures (stacks, queues, multi-dimensional arrays, trees, graphs), algorithms (searching, sorting, optimization, dynamic programming), computability and complexity (P vs. NP, NP-complete problems, big-O notation, approximate algorithms), and computer architecture (memory, cache, bandwidth, deadlocks, distributed processing).
- Probability and statistics: Formal characterization of probability (conditional probability, Bayes' rule, likelihood, independence) and techniques derived from it (Bayes Nets, Markov Decision Processes, Hidden Markov Models). Statistics measures (mean, median, variance), distributions (uniform, normal, binomial, Poisson), and analysis methods (ANOVA, hypothesis testing).
- Data modeling and evaluation: Finding patterns (correlations, clusters, eigenvectors), predicting properties of previously unseen instances (classification, regression, anomaly detection), and determining the right accuracy/error measure (e.g., log-loss for classification, or sum-of-squared-errors for regression) and an evaluation strategy (training-testing split, sequential vs. randomized cross-validation).
- Applying machine learning algorithms and libraries: Standard implementations of machine learning algorithms are available through libraries, packages, and APIs (such as scikit-learn, Theano, Spark MLlib, H2O, and TensorFlow). Applying them effectively means selecting the right model (decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models) and a learning procedure to fit the data (linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods), as well as understanding how hyperparameters affect learning.
- Software engineering and system design: Machine engineers are typically working on software that fits into a larger ecosystem of products and services. That means they need to understand how the different parts work together, communicate with the parts (using library calls, REST APIs, and database queries), and build interfaces for your piece that others can use. This involves knowing system design and software engineering best practices (including requirements analysis, system design, modularity, version control, testing, and documentation).

- What have you been working on for the past few years?
- What AI and machine learning tools are you familiar with, and how proficient are you in them?
- What do you do to stay on top of changing technologies?
- How do you clean and prepare data to ensure quality and relevance?
- How do you handle missing or corrupted data in a dataset?
- What are the ethical implications of using machine learning?