How Coders Can Survive—and Thrive—in a ChatGPT World
Artificial intelligence, particularly generative AI powered by large language models (LLMs), could upend many coders’ livelihoods. But some experts argue that AI won’t replace human programmers—not immediately, at least.
“You will have to worry about people who are using AI replacing you,” says Tanishq Mathew Abraham, a recent Ph.D. in biomedical engineering at the University of California, Davis and the CEO of medical AI research center MedARC.
Here are some tips and techniques for coders to survive and thrive in a generative AI world.
Stick to Basics and Best Practices
While the myriad AI-based coding assistants could help with code completion and code generation, the fundamentals of programming remain: the ability to read and reason about your own and others’ code, and understanding how the code you write fits into a larger system.
Find the Tool That Fits Your Needs
Finding the right AI-based tool is essential. Each tool has its own ways to interact with it, and there are different ways to incorporate each tool into your development workflow—whether that’s automating the creation of unit tests, generating test data, or writing documentation.
Clear and Precise Conversations Are Crucial
When using AI coding assistants, be detailed about what you need and view it as an iterative process. Abraham proposes writing a comment that explains the code you want so the assistant can generate relevant suggestions that meet your requirements.
Be Critical and Understand the Risks
Software engineers should be critical of the outputs of large language models, as they tend to hallucinate and produce inaccurate or incorrect code. “It’s easy to get stuck in a debugging rabbit hole when blindly using AI-generated code, and subtle bugs can be difficult to spot,” Vaithilingam says.
Artificial intelligence, particularly generative AI powered by large language models (LLMs), could upend many coders’ livelihoods. But some experts argue that AI won’t replace human programmers—not immediately, at least.
“You will have to worry about people who are using AI replacing you,” says Tanishq Mathew Abraham, a recent Ph.D. in biomedical engineering at the University of California, Davis and the CEO of medical AI research center MedARC.
Here are some tips and techniques for coders to survive and thrive in a generative AI world.
Stick to Basics and Best Practices
While the myriad AI-based coding assistants could help with code completion and code generation, the fundamentals of programming remain: the ability to read and reason about your own and others’ code, and understanding how the code you write fits into a larger system.
Find the Tool That Fits Your Needs
Finding the right AI-based tool is essential. Each tool has its own ways to interact with it, and there are different ways to incorporate each tool into your development workflow—whether that’s automating the creation of unit tests, generating test data, or writing documentation.
Clear and Precise Conversations Are Crucial
When using AI coding assistants, be detailed about what you need and view it as an iterative process. Abraham proposes writing a comment that explains the code you want so the assistant can generate relevant suggestions that meet your requirements.
Be Critical and Understand the Risks
Software engineers should be critical of the outputs of large language models, as they tend to hallucinate and produce inaccurate or incorrect code. “It’s easy to get stuck in a debugging rabbit hole when blindly using AI-generated code, and subtle bugs can be difficult to spot,” Vaithilingam says.
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Here are 40 most asked DSA questions to ace your next interview -
𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 (𝗗𝗣):
1. How do you find the nth Fibonacci number using dynamic programming?
2. Write a dynamic programming solution for the 0/1 knapsack problem.
3. Memoization to optimize recursive solutions in dynamic programming?
4. Implement a dynamic programming algorithm to find the longest common subsequence of two strings.
5. The coin change problem.
6. Tabulation approach in dynamic programming.
𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴:
7. Backtracking algorithm to solve the N-Queens problem.
8. Generate all permutations of a given set using backtracking?
9. Implement backtracking to solve the Sudoku puzzle.
10. Subset sum problem.
11. Graph coloring problem using backtracking.
12. Write a backtracking algorithm to find the Hamiltonian cycle in a graph.
𝗛𝗮𝘀𝗵𝗶𝗻𝗴:
13. Implement a hash table using separate chaining.
14. First non-repeating character in a string using hashing.
15. Collision resolution techniques in hashing.
16. Write a function to solve the two-sum problem using hashing.
17. How can you implement a hash set data structure?
18. Count the frequency of elements in an array using hashing.
𝗛𝗲𝗮𝗽:
19. Implement a priority queue using a min-heap.
20. How do you merge K sorted arrays using a min-heap?
21. Write a function to perform heap sort algorithm.
22. Find the kth largest element in an array using a min-heap.
23. Implement a priority queue using a min-heap.
24. How do you build a max heap from an array?
𝗧𝗿𝗶𝗲𝘀:
25. Implement a trie data structure.
26. Write a function to search for a word in a trie.
27. How can you implement autocomplete feature using a trie?
28. Deleting a word from a trie.
30. Write a function to find all words matching a pattern in a trie.
𝗚𝗿𝗲𝗲𝗱𝘆 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀:
31. Solve the activity selection problem using a greedy algorithm.
32. Implement Huffman coding using a greedy algorithm.
33. Write a function to find the minimum spanning tree using Prim's algorithm.
34. Coin change problem.
35. Dijkstra's algorithm using a greedy approach.
36. Implement the job sequencing problem using a greedy algorithm.
37. Stack Vs queue.
38. breadth-first search (BFS) and depth-first search (DFS) traversal
39. Concept of big O notation.
40. What is an AVL tree? Explain its properties and how it maintains balance during insertion and deletion operations.
React ❤️ for more
𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴 (𝗗𝗣):
1. How do you find the nth Fibonacci number using dynamic programming?
2. Write a dynamic programming solution for the 0/1 knapsack problem.
3. Memoization to optimize recursive solutions in dynamic programming?
4. Implement a dynamic programming algorithm to find the longest common subsequence of two strings.
5. The coin change problem.
6. Tabulation approach in dynamic programming.
𝗕𝗮𝗰𝗸𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴:
7. Backtracking algorithm to solve the N-Queens problem.
8. Generate all permutations of a given set using backtracking?
9. Implement backtracking to solve the Sudoku puzzle.
10. Subset sum problem.
11. Graph coloring problem using backtracking.
12. Write a backtracking algorithm to find the Hamiltonian cycle in a graph.
𝗛𝗮𝘀𝗵𝗶𝗻𝗴:
13. Implement a hash table using separate chaining.
14. First non-repeating character in a string using hashing.
15. Collision resolution techniques in hashing.
16. Write a function to solve the two-sum problem using hashing.
17. How can you implement a hash set data structure?
18. Count the frequency of elements in an array using hashing.
𝗛𝗲𝗮𝗽:
19. Implement a priority queue using a min-heap.
20. How do you merge K sorted arrays using a min-heap?
21. Write a function to perform heap sort algorithm.
22. Find the kth largest element in an array using a min-heap.
23. Implement a priority queue using a min-heap.
24. How do you build a max heap from an array?
𝗧𝗿𝗶𝗲𝘀:
25. Implement a trie data structure.
26. Write a function to search for a word in a trie.
27. How can you implement autocomplete feature using a trie?
28. Deleting a word from a trie.
30. Write a function to find all words matching a pattern in a trie.
𝗚𝗿𝗲𝗲𝗱𝘆 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀:
31. Solve the activity selection problem using a greedy algorithm.
32. Implement Huffman coding using a greedy algorithm.
33. Write a function to find the minimum spanning tree using Prim's algorithm.
34. Coin change problem.
35. Dijkstra's algorithm using a greedy approach.
36. Implement the job sequencing problem using a greedy algorithm.
37. Stack Vs queue.
38. breadth-first search (BFS) and depth-first search (DFS) traversal
39. Concept of big O notation.
40. What is an AVL tree? Explain its properties and how it maintains balance during insertion and deletion operations.
React ❤️ for more
❤6
You will not learn system design in a month.
You will not master DSA in a month.
You will not suddenly understand how to solve problems at scale in a month.
You won’t grasp scalability, databases, and caching overnight.
And you most definitely won’t internalize every distributed system pattern just by reading a few blogs.
Because software engineering is an ocean: deep, vast, and ever-expanding.
And you can’t cross an ocean in a single leap.
In a month, you’ll realize you’re only scratching the surface.
You’ll see more gaps than answers.
You’ll feel like there’s too much to learn and too little time.
But that’s where most people give up.
That’s where frustration makes them quit.
Don’t be one of them.
Take it one step at a time.
Real expertise doesn’t come from rushing. It comes from consistent, deliberate learning over years.
It comes from revisiting the same concepts and seeing them from new perspectives each time.
So trust your own pace.
Stay in the game long enough to connect the dots.
And one day, the same concepts that once seemed impossible will feel like second nature.
Just keep collecting buckets.
You will not master DSA in a month.
You will not suddenly understand how to solve problems at scale in a month.
You won’t grasp scalability, databases, and caching overnight.
And you most definitely won’t internalize every distributed system pattern just by reading a few blogs.
Because software engineering is an ocean: deep, vast, and ever-expanding.
And you can’t cross an ocean in a single leap.
In a month, you’ll realize you’re only scratching the surface.
You’ll see more gaps than answers.
You’ll feel like there’s too much to learn and too little time.
But that’s where most people give up.
That’s where frustration makes them quit.
Don’t be one of them.
Take it one step at a time.
Real expertise doesn’t come from rushing. It comes from consistent, deliberate learning over years.
It comes from revisiting the same concepts and seeing them from new perspectives each time.
So trust your own pace.
Stay in the game long enough to connect the dots.
And one day, the same concepts that once seemed impossible will feel like second nature.
Just keep collecting buckets.
❤4👌1
SQL Essentials for Quick Revision
🚀 SELECT
Retrieve data from one or more tables.
🎯 WHERE Clause
Filter records based on specific conditions.
🔄 ORDER BY
Sort query results in ascending (ASC) or descending (DESC) order.
📊 Aggregation Functions
MIN, MAX, AVG, COUNT: Summarize data.
Window Functions: Perform calculations across a dataset without grouping rows.
🔑 GROUP BY
Group data based on one or more columns and apply aggregate functions.
🔗 JOINS
INNER JOIN: Fetch matching rows from both tables.
LEFT JOIN: All rows from the left table and matching rows from the right.
RIGHT JOIN: All rows from the right table and matching rows from the left.
FULL JOIN: Combine rows when there is a match in either table.
SELF JOIN: Join a table with itself.
🧩 Common Table Expressions (CTE)
Simplify complex queries with temporary result sets.
Quick SQL Revision Notes 📌
Master these concepts for interviews and projects!
#SQL #DataAnalytics #QuickNotes
🚀 SELECT
Retrieve data from one or more tables.
🎯 WHERE Clause
Filter records based on specific conditions.
🔄 ORDER BY
Sort query results in ascending (ASC) or descending (DESC) order.
📊 Aggregation Functions
MIN, MAX, AVG, COUNT: Summarize data.
Window Functions: Perform calculations across a dataset without grouping rows.
🔑 GROUP BY
Group data based on one or more columns and apply aggregate functions.
🔗 JOINS
INNER JOIN: Fetch matching rows from both tables.
LEFT JOIN: All rows from the left table and matching rows from the right.
RIGHT JOIN: All rows from the right table and matching rows from the left.
FULL JOIN: Combine rows when there is a match in either table.
SELF JOIN: Join a table with itself.
🧩 Common Table Expressions (CTE)
Simplify complex queries with temporary result sets.
Quick SQL Revision Notes 📌
Master these concepts for interviews and projects!
#SQL #DataAnalytics #QuickNotes
❤2
Typical C++ interview questions sorted by experience
Junior:
- What are the key features of object-oriented programming in C++?
- Explain the differences between public, private, and protected access specifiers in C++.
- Distinguish between function overloading and overriding in C++.
- Compare and contrast abstract classes and interfaces in C++.
- Can an interface inherit from another interface in C++?
- Define the static keyword in C++ and its significance.
- Is it possible to override a static method in C++?
- Explain the concepts of polymorphism and inheritance in C++.
- Can constructors be inherited in C++?
- Discuss pass-by-reference and pass-by-value for objects in C++.
- Compare == and .equals for string comparison in C++.
- Explain the purposes of the hashCode() and equals() functions.
- What does the Serializable interface do? How is it related to Parcelable in Android?
- Differentiate between Array and ArrayList in C++. When would you use each?
- Explain the distinction between Integer and int in C++.
- Define ThreadPool and discuss its advantages over using simple threads.
- Differentiate between local, instance, and class variables in C++.
Mid:
- What is reflection in C++?
- Define dependency injection and name a few libraries. Have you used any?
- Explain strong, soft, and weak references in C++.
- Interpret the meaning of the synchronized keyword.
- Can memory leaks occur in C++?
- Is it necessary to set references to null in C++?
- Why is a String considered immutable?
- Discuss transient and volatile modifiers in C++.
- What is the purpose of the finalize() method?
- How does the try{} finally{} block work in C++?
- Explain the difference between object instantiation and initialization.
- Under what conditions is a static block executed in C++?
- Why are generics used in C++?
- Mention some design patterns you are familiar with. Which do you typically use?
- Name some types of testing methodologies in C++.
Senior:
- Explain how
- What is the "double-check locking" problem, and how can it be solved in C++?
- Differentiate between StringBuffer and StringBuilder in C++.
- How is StringBuilder implemented to avoid the immutable string allocation problem?
- Explain the purpose of the
- Define Autoboxing and Unboxing in C++.
- What's the difference between Enumeration and Iterator in C++?
- Explain the difference between fail-fast and fail-safe in C++.
- What is PermGen in C++?
- Describe a Java priority queue.
- How is performance influenced by using the same number in different types: Int, Double, and Float?
- Explain the concept of the Java Heap.
- What is a daemon thread?
- Can a dead thread be restarted in C++?
✅ Best Telegram channels to get free coding & data science resources
-> https://t.me/addlist/4q2PYC0pH_VjZDk5
ENJOY LEARNING 👍👍
Junior:
- What are the key features of object-oriented programming in C++?
- Explain the differences between public, private, and protected access specifiers in C++.
- Distinguish between function overloading and overriding in C++.
- Compare and contrast abstract classes and interfaces in C++.
- Can an interface inherit from another interface in C++?
- Define the static keyword in C++ and its significance.
- Is it possible to override a static method in C++?
- Explain the concepts of polymorphism and inheritance in C++.
- Can constructors be inherited in C++?
- Discuss pass-by-reference and pass-by-value for objects in C++.
- Compare == and .equals for string comparison in C++.
- Explain the purposes of the hashCode() and equals() functions.
- What does the Serializable interface do? How is it related to Parcelable in Android?
- Differentiate between Array and ArrayList in C++. When would you use each?
- Explain the distinction between Integer and int in C++.
- Define ThreadPool and discuss its advantages over using simple threads.
- Differentiate between local, instance, and class variables in C++.
Mid:
- What is reflection in C++?
- Define dependency injection and name a few libraries. Have you used any?
- Explain strong, soft, and weak references in C++.
- Interpret the meaning of the synchronized keyword.
- Can memory leaks occur in C++?
- Is it necessary to set references to null in C++?
- Why is a String considered immutable?
- Discuss transient and volatile modifiers in C++.
- What is the purpose of the finalize() method?
- How does the try{} finally{} block work in C++?
- Explain the difference between object instantiation and initialization.
- Under what conditions is a static block executed in C++?
- Why are generics used in C++?
- Mention some design patterns you are familiar with. Which do you typically use?
- Name some types of testing methodologies in C++.
Senior:
- Explain how
std::stoi (string to integer) works in C++.- What is the "double-check locking" problem, and how can it be solved in C++?
- Differentiate between StringBuffer and StringBuilder in C++.
- How is StringBuilder implemented to avoid the immutable string allocation problem?
- Explain the purpose of the
Class.forName method in C++.- Define Autoboxing and Unboxing in C++.
- What's the difference between Enumeration and Iterator in C++?
- Explain the difference between fail-fast and fail-safe in C++.
- What is PermGen in C++?
- Describe a Java priority queue.
- How is performance influenced by using the same number in different types: Int, Double, and Float?
- Explain the concept of the Java Heap.
- What is a daemon thread?
- Can a dead thread be restarted in C++?
✅ Best Telegram channels to get free coding & data science resources
-> https://t.me/addlist/4q2PYC0pH_VjZDk5
ENJOY LEARNING 👍👍
❤1
Learning CSS in 30 days👇
Day 1-5: Introduction to CSS⚡
Learn the basics of HTML (if you don't know already)
Understand what CSS is and how it is used to style web pages
Learn how to add CSS to an HTML page
Understand the different ways to add CSS to an HTML page (inline, internal, external)
Learn about selectors and how they are used to target HTML elements
Day 6-10: Box model and layout🎁
Understand the box model and how it affects the layout of HTML elements
Learn how to manipulate the box model using padding, margin, and border
Learn how to position elements using CSS (relative, absolute, fixed, static, sticky)
Understand the different display properties (block, inline, inline-block) and how they affect layout
Learn how to create responsive layouts using media queries
Day 11-15: Typography and colors ❤️🔥
Understand the CSS font properties (font-family, font-size, font-weight, etc.)
Learn how to style text using CSS (color, text-decoration, text-align, etc.)
Understand the CSS color property and how to use it to set colors
Learn about gradients, transparency, and opacity
Understand how to use CSS to create hover and active effects
Day 16-20: Advanced CSS concepts👩💻
Learn about CSS preprocessors like Sass and Less
Understand how to use CSS frameworks like Bootstrap and Foundation
Learn about CSS animations and transitions
Understand how to use flexbox for layout
Learn how to use CSS grid for layout
Day 21-25: CSS best practices and optimization💥
Learn about CSS code organization and best practices
Understand how to optimize CSS for performance
Learn how to use CSS vendor prefixes for cross-browser compatibility
Understand how to debug CSS using browser developer tools
Learn how to use CSS linting tools to catch errors
Day 26-30: Practice and projects👩🏫
Create simple projects to practice what you've learned
Work on more complex projects to challenge yourself
Join CSS communities to learn from others and get feedback on your work
Read articles and tutorials to stay up-to-date with the latest CSS trends and techniques
Reflect on what you've learned and identify areas for improvement
Remember, this is just a roadmap, and you can adjust it based on your learning style and pace.
The most important thing is to stay consistent and practice regularly. Good luck!
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING 👍👍
Day 1-5: Introduction to CSS⚡
Learn the basics of HTML (if you don't know already)
Understand what CSS is and how it is used to style web pages
Learn how to add CSS to an HTML page
Understand the different ways to add CSS to an HTML page (inline, internal, external)
Learn about selectors and how they are used to target HTML elements
Day 6-10: Box model and layout🎁
Understand the box model and how it affects the layout of HTML elements
Learn how to manipulate the box model using padding, margin, and border
Learn how to position elements using CSS (relative, absolute, fixed, static, sticky)
Understand the different display properties (block, inline, inline-block) and how they affect layout
Learn how to create responsive layouts using media queries
Day 11-15: Typography and colors ❤️🔥
Understand the CSS font properties (font-family, font-size, font-weight, etc.)
Learn how to style text using CSS (color, text-decoration, text-align, etc.)
Understand the CSS color property and how to use it to set colors
Learn about gradients, transparency, and opacity
Understand how to use CSS to create hover and active effects
Day 16-20: Advanced CSS concepts👩💻
Learn about CSS preprocessors like Sass and Less
Understand how to use CSS frameworks like Bootstrap and Foundation
Learn about CSS animations and transitions
Understand how to use flexbox for layout
Learn how to use CSS grid for layout
Day 21-25: CSS best practices and optimization💥
Learn about CSS code organization and best practices
Understand how to optimize CSS for performance
Learn how to use CSS vendor prefixes for cross-browser compatibility
Understand how to debug CSS using browser developer tools
Learn how to use CSS linting tools to catch errors
Day 26-30: Practice and projects👩🏫
Create simple projects to practice what you've learned
Work on more complex projects to challenge yourself
Join CSS communities to learn from others and get feedback on your work
Read articles and tutorials to stay up-to-date with the latest CSS trends and techniques
Reflect on what you've learned and identify areas for improvement
Remember, this is just a roadmap, and you can adjust it based on your learning style and pace.
The most important thing is to stay consistent and practice regularly. Good luck!
Web Development Best Resources: https://topmate.io/coding/930165
ENJOY LEARNING 👍👍
❤2
If you want to get a job as a machine learning engineer, don’t start by diving into the hottest libraries like PyTorch,TensorFlow, Langchain, etc.
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
Yes, you might hear a lot about them or some other trending technology of the year...but guess what!
Technologies evolve rapidly, especially in the age of AI, but core concepts are always seen as more valuable than expertise in any particular tool. Stop trying to perform a brain surgery without knowing anything about human anatomy.
Instead, here are basic skills that will get you further than mastering any framework:
𝐌𝐚𝐭𝐡𝐞𝐦𝐚𝐭𝐢𝐜𝐬 𝐚𝐧𝐝 𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 - My first exposure to probability and statistics was in college, and it felt abstract at the time, but these concepts are the backbone of ML.
You can start here: Khan Academy Statistics and Probability - https://www.khanacademy.org/math/statistics-probability
𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚 𝐚𝐧𝐝 𝐂𝐚𝐥𝐜𝐮𝐥𝐮𝐬 - Concepts like matrices, vectors, eigenvalues, and derivatives are fundamental to understanding how ml algorithms work. These are used in everything from simple regression to deep learning.
𝐏𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 - Should you learn Python, Rust, R, Julia, JavaScript, etc.? The best advice is to pick the language that is most frequently used for the type of work you want to do. I started with Python due to its simplicity and extensive library support, and it remains my go-to language for machine learning tasks.
You can start here: Automate the Boring Stuff with Python - https://automatetheboringstuff.com/
𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦 𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 - Understand the fundamental algorithms before jumping to deep learning. This includes linear regression, decision trees, SVMs, and clustering algorithms.
𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 𝐚𝐧𝐝 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧:
Knowing how to take a model from development to production is invaluable. This includes understanding APIs, model optimization, and monitoring. Tools like Docker and Flask are often used in this process.
𝐂𝐥𝐨𝐮𝐝 𝐂𝐨𝐦𝐩𝐮𝐭𝐢𝐧𝐠 𝐚𝐧𝐝 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚:
Familiarity with cloud platforms (AWS, Google Cloud, Azure) and big data tools (Spark) is increasingly important as datasets grow larger. These skills help you manage and process large-scale data efficiently.
You can start here: Google Cloud Machine Learning - https://cloud.google.com/learn/training/machinelearning-ai
I love frameworks and libraries, and they can make anyone's job easier.
But the more solid your foundation, the easier it will be to pick up any new technologies and actually validate whether they solve your problems.
Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624
All the best 👍👍
❤3