...

Full Bio

Which One Programming Language Is More Secure Than The Rest Programming Language?

yesterday

Why Deep Learning Needs A New Programming Language?

yesterday

Machine Learning Engineer Is In High Demand Job In The USA According To Indeed

4 days ago

How To Prepare For A Career In Data Science? Every Student Should Know

4 days ago

How To Use Augmented Data Science & Why Is It Important For Business?

4 days ago

Highest Paying Programming Language, Skills: Here Are The Top Earners

585807 views

Which Programming Languages in Demand & Earn The Highest Salaries?

419361 views

Top 10 Best Countries for Software Engineers to Work & High in-Demand Programming Languages

353037 views

50+ Data Structure, Algorithms & Programming Languages Interview Questions for Programmers

247509 views

100+ Data Structure, Algorithms & Programming Language Interview Questions Answers for Programmers - Part 1

211137 views

### How I Became A Machine Learning Engineer & Got Job Soon: A Cheat Note

- 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?