4 answers
Advaith
Student
Temple Terrace, Florida
1
Question
Asked
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What skills should new computer science grads know, for machine learning roles?
What skills should I learn, as a new grad who wants to start as a machine learning developer? And for DSA interviews, what algorithms should I learn beforehand to have a good chance at passing it?
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4 answers
Doc Frick
Career Coach and Mentor
1942
Answers
California, CA
Updated
Doc’s Answer
So you’ve been hearing about machine learning and all its possibilities Advaith. Here are the actual skills you’ll need to learn before entering this field. Essential concepts in machine learning often involve statistical analysis and mathematical data manipulation. Machine learning professionals excel in technical skills such as software engineering and data science and non-technical competencies like communication and problem-solving proficiency.
PROGRAMMING EXPERIENCE
Here’s a list of some popular machine learning programming languages. It shouldn’t really come as a surprise that programming languages are one of the most common technical requirements of machine learning jobs out there.
• Python: As a leading programming language, Python boasts extensive libraries for machine learning, such as TensorFlow and scikit-learn.
• R: Predominantly used in statistics and visualizations, R is essential for data analysis and modeling.
• SQL: Command of SQL is vital for data extraction, manipulation, and querying from relational databases. Here’s a quick beginner’s guide to SQL to help you get started.
• Java: Java’s robustness and portability makes it suitable for high-performance machine learning applications.
• C/C++: For performance-critical applications, a thorough knowledge of these languages is indispensable.
MATHEMATICS AND STATISTICS
A solid foundation in linear algebra, calculus, and probability & statistics is vital for understanding how algorithms work and for interpreting results. This knowledge helps in feature engineering, model selection, and understanding model performance.
DATA PREPROCESSING
Real-world data is often messy and requires cleaning and transformation before it can be used by machine learning models. Data preprocessing involves techniques like handling missing values, dealing with outliers, and scaling data. Feature engineering is the process of creating new, informative features from existing data, which can significantly impact model accuracy.
ALGORITHMS AND MODEL EVALUATION
Knowing how to choose the right algorithm for a specific task and how to evaluate its performance is crucial. This involves understanding the strengths and weaknesses of various algorithms (e.g., regression, classification, clustering) and using appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score).
MODEL DEPLOYMENT AND SCALABILITY
The ability to deploy trained models into production environments and ensure they can handle large amounts of data is a key skill. This involves understanding cloud computing platforms, containerization technologies (like Docker), and model serving frameworks. Additionally, skills in DevOps and MLOps are becoming increasingly important for maintaining and updating models in production.
CONCUSSION
Machine learning is an incredibly dynamic and rapidly evolving field. It requires a diverse set of skills that go beyond just coding and data analysis. In addition to technical skills such as programming languages, statistics, and knowledge of algorithms, soft skills such as communication, problem-solving, and continual learning are also essential for success in this field.
Hope this was helpful Advaith.
PROGRAMMING EXPERIENCE
Here’s a list of some popular machine learning programming languages. It shouldn’t really come as a surprise that programming languages are one of the most common technical requirements of machine learning jobs out there.
• Python: As a leading programming language, Python boasts extensive libraries for machine learning, such as TensorFlow and scikit-learn.
• R: Predominantly used in statistics and visualizations, R is essential for data analysis and modeling.
• SQL: Command of SQL is vital for data extraction, manipulation, and querying from relational databases. Here’s a quick beginner’s guide to SQL to help you get started.
• Java: Java’s robustness and portability makes it suitable for high-performance machine learning applications.
• C/C++: For performance-critical applications, a thorough knowledge of these languages is indispensable.
MATHEMATICS AND STATISTICS
A solid foundation in linear algebra, calculus, and probability & statistics is vital for understanding how algorithms work and for interpreting results. This knowledge helps in feature engineering, model selection, and understanding model performance.
DATA PREPROCESSING
Real-world data is often messy and requires cleaning and transformation before it can be used by machine learning models. Data preprocessing involves techniques like handling missing values, dealing with outliers, and scaling data. Feature engineering is the process of creating new, informative features from existing data, which can significantly impact model accuracy.
ALGORITHMS AND MODEL EVALUATION
Knowing how to choose the right algorithm for a specific task and how to evaluate its performance is crucial. This involves understanding the strengths and weaknesses of various algorithms (e.g., regression, classification, clustering) and using appropriate evaluation metrics (e.g., accuracy, precision, recall, F1-score).
MODEL DEPLOYMENT AND SCALABILITY
The ability to deploy trained models into production environments and ensure they can handle large amounts of data is a key skill. This involves understanding cloud computing platforms, containerization technologies (like Docker), and model serving frameworks. Additionally, skills in DevOps and MLOps are becoming increasingly important for maintaining and updating models in production.
CONCUSSION
Machine learning is an incredibly dynamic and rapidly evolving field. It requires a diverse set of skills that go beyond just coding and data analysis. In addition to technical skills such as programming languages, statistics, and knowledge of algorithms, soft skills such as communication, problem-solving, and continual learning are also essential for success in this field.
Hope this was helpful Advaith.
Great advice, Doc!
Just a follow up: I am currently a new graduate, and I have been working with machine learning on two internships - and also served as a teaching assistant for an algorithms course. I also wanted to ask you how important it would be to learn how to use containerization frameworks like Docker and Kubernetes? They have been somewhat of a "buzzword" in many CVs that I have seen recently. Do you believe they are useful for the field of AI and ML as well?
Advaith
GREAT question Advaith. The field of machine learning is constantly evolving with new algorithms, techniques, and tools emerging regularly. Continuing education will ensure you're stay up-to-date with these advancements, preventing you from falling behind and becoming obsolete. Machine learning is not a static field. Professionals need to adapt to new data distributions, evolving challenges, and new applications. Continuous learning helps you develop the flexibility and problem-solving skills necessary to tackle future changes going forward.
Doc Frick
Thank You Tasneem. Volunteering is an act of heroism on a grand scale. And it matters profoundly. It does more than help people beat the odds; it changes the odds.
Doc Frick
Emily Anne
Manager, Technical Support
25
Answers
Manila, Metro Manila, Philippines
Updated
Emily’s Answer
From the top of my mind:
1. Core Skills for Machine Learning Roles:
Programming:
-Python is the industry standard. Be comfortable with libraries like NumPy, pandas, scikit-learn, and matplotlib.
-Knowing a bit of SQL for data querying is also helpful.
-Familiarity with Jupyter Notebooks for prototyping.
Math Fundamentals:
-Linear Algebra: Vectors, matrices, eigenvalues, etc.
-Probability & Statistics: Distributions, Bayes’ theorem, hypothesis testing.
-Calculus: Mainly derivatives and gradients (for understanding optimization).
Machine Learning Concepts:
-Supervised/Unsupervised Learning: Regression, classification, clustering.
-Model Evaluation: Cross-validation, confusion matrix, ROC/AUC, precision/recall.
-Feature Engineering: Data cleaning, normalization, encoding categorical variables.
-Overfitting/Underfitting: Regularization, bias-variance tradeoff.
Frameworks:
-scikit-learn for classical ML.
-TensorFlow or PyTorch for deep learning (start with one, then branch out).
Data Handling:
-Data preprocessing, handling missing values, outlier detection.
-Understanding of basic data pipelines.
Version Control:
-Git basics for collaboration.
2. For DSA Interviews:
Data Structures:
-Arrays, Strings, Linked Lists
-Stacks, Queues
-Hash Tables (Dictionaries/Maps)
-Trees (Binary Trees, BSTs, Trie)
-Graphs (basic representation and traversal)
Algorithms:
-Sorting (Quick, Merge, Bubble, Insertion)
-Searching (Binary Search, Linear Search)
-Recursion and Backtracking
-Dynamic Programming (knapsack, longest subsequence, etc.)
-Greedy Algorithms
-Graph Algorithms (BFS, DFS, Dijkstra’s, Topological Sort)
Other Tips:
-Practice coding on platforms like LeetCode, HackerRank, or CodeSignal.
-Focus on writing clean, readable code and explaining your thought process.
-Be ready for time/space complexity analysis (Big O notation).
If you have more time, work on projects like build a few ML projects (even small ones) and put them on GitHub. This shows initiative and practical skills. Practice explaining your solutions and ML concepts clearly—this is key in interviews and stay curious! The field moves fast! Follow blogs, papers, or YouTube channels to keep learning.
Good luck!
1. Core Skills for Machine Learning Roles:
Programming:
-Python is the industry standard. Be comfortable with libraries like NumPy, pandas, scikit-learn, and matplotlib.
-Knowing a bit of SQL for data querying is also helpful.
-Familiarity with Jupyter Notebooks for prototyping.
Math Fundamentals:
-Linear Algebra: Vectors, matrices, eigenvalues, etc.
-Probability & Statistics: Distributions, Bayes’ theorem, hypothesis testing.
-Calculus: Mainly derivatives and gradients (for understanding optimization).
Machine Learning Concepts:
-Supervised/Unsupervised Learning: Regression, classification, clustering.
-Model Evaluation: Cross-validation, confusion matrix, ROC/AUC, precision/recall.
-Feature Engineering: Data cleaning, normalization, encoding categorical variables.
-Overfitting/Underfitting: Regularization, bias-variance tradeoff.
Frameworks:
-scikit-learn for classical ML.
-TensorFlow or PyTorch for deep learning (start with one, then branch out).
Data Handling:
-Data preprocessing, handling missing values, outlier detection.
-Understanding of basic data pipelines.
Version Control:
-Git basics for collaboration.
2. For DSA Interviews:
Data Structures:
-Arrays, Strings, Linked Lists
-Stacks, Queues
-Hash Tables (Dictionaries/Maps)
-Trees (Binary Trees, BSTs, Trie)
-Graphs (basic representation and traversal)
Algorithms:
-Sorting (Quick, Merge, Bubble, Insertion)
-Searching (Binary Search, Linear Search)
-Recursion and Backtracking
-Dynamic Programming (knapsack, longest subsequence, etc.)
-Greedy Algorithms
-Graph Algorithms (BFS, DFS, Dijkstra’s, Topological Sort)
Other Tips:
-Practice coding on platforms like LeetCode, HackerRank, or CodeSignal.
-Focus on writing clean, readable code and explaining your thought process.
-Be ready for time/space complexity analysis (Big O notation).
If you have more time, work on projects like build a few ML projects (even small ones) and put them on GitHub. This shows initiative and practical skills. Practice explaining your solutions and ML concepts clearly—this is key in interviews and stay curious! The field moves fast! Follow blogs, papers, or YouTube channels to keep learning.
Good luck!
James Constantine Frangos
SOFTWARE ENGINEER SINCE 1972; NUTRITIONIST SINCE 1976.
7094
Answers
Gold Coast, Queensland, Australia
Updated
James Constantine’s Answer
Good Day Advaith!
Ride the Artificial Intelligence wave instead of being inundated by it! Go with the flow of the energy, making sure you can influence the change, rather than being victim to it. Redundancy-proof yourself. SEE https://www.coursera.org/specializations/machine-learning-introduction?
ALSO https://www.coursera.org/certificates/advance-your-career? ALSO https://www.coursera.org/professional-certificates/microsoft-ai-and-ml-engineering/paidmedia? Rather than being worried about new trends, prepare yourself for an altered employment marketplace.
Do two things. Specialize in machine learning and diversify into fields that Artificial Intelligence cannot touch! Be centripetal and centrifugal too. SEE https://jobright.ai/blog/jobs-ai-cant-replace/ ALSO https://www.forbes.com/sites/bryanrobinson/2025/03/13/the-10-careers-ai-cant-replace-in-2025-yet/
Ride the Artificial Intelligence wave instead of being inundated by it! Go with the flow of the energy, making sure you can influence the change, rather than being victim to it. Redundancy-proof yourself. SEE https://www.coursera.org/specializations/machine-learning-introduction?
ALSO https://www.coursera.org/certificates/advance-your-career? ALSO https://www.coursera.org/professional-certificates/microsoft-ai-and-ml-engineering/paidmedia? Rather than being worried about new trends, prepare yourself for an altered employment marketplace.
Do two things. Specialize in machine learning and diversify into fields that Artificial Intelligence cannot touch! Be centripetal and centrifugal too. SEE https://jobright.ai/blog/jobs-ai-cant-replace/ ALSO https://www.forbes.com/sites/bryanrobinson/2025/03/13/the-10-careers-ai-cant-replace-in-2025-yet/
Isabel Dominguez
Student
137
Answers
Corning, California
Updated
Isabel’s Answer
Hi Advaith,
If you're a new computer science graduate aiming for a machine learning job, it's important to get good at Python, NumPy, pandas, scikit-learn, and either TensorFlow or PyTorch. Understand concepts like supervised and unsupervised learning, overfitting, and how to evaluate models. Strong skills in linear algebra, probability, and calculus are crucial.
For data structures and algorithms (DSA) interviews, focus on sorting methods like merge and quick sort, searching techniques like binary search, recursion, breadth-first search (BFS), depth-first search (DFS), dynamic programming, heaps, hash tables, trees, and graphs. Practice problems on platforms like LeetCode or HackerRank. Also, be able to explain time and space complexity and learn to optimize your code.
Knowing version control with Git, some basic SQL, and maintaining good documentation are also helpful. Working on projects, participating in Kaggle competitions, or doing internships can really boost your portfolio and give you more practical machine learning experience.
If you're a new computer science graduate aiming for a machine learning job, it's important to get good at Python, NumPy, pandas, scikit-learn, and either TensorFlow or PyTorch. Understand concepts like supervised and unsupervised learning, overfitting, and how to evaluate models. Strong skills in linear algebra, probability, and calculus are crucial.
For data structures and algorithms (DSA) interviews, focus on sorting methods like merge and quick sort, searching techniques like binary search, recursion, breadth-first search (BFS), depth-first search (DFS), dynamic programming, heaps, hash tables, trees, and graphs. Practice problems on platforms like LeetCode or HackerRank. Also, be able to explain time and space complexity and learn to optimize your code.
Knowing version control with Git, some basic SQL, and maintaining good documentation are also helpful. Working on projects, participating in Kaggle competitions, or doing internships can really boost your portfolio and give you more practical machine learning experience.