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What do you do if you want to learn Algorithms at your own pace?

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When embarking on the journey to learn algorithms at your own pace, it's important to have a structured approach that aligns with your learning style and schedule. Algorithms are step-by-step procedures or formulas for solving problems, and understanding them is crucial for anyone looking to improve their problem-solving skills in computer science and programming. Whether you're a student, a professional, or simply curious about computer science, grasping the intricacies of algorithms can be both challenging and rewarding.

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Muhammad Yaman Ghaibeh

Senior AI Engineer @ Telecel Global

1 Start Simple

Begin with the basics by familiarizing yourself with simple algorithms that are foundational to computer science. Start with sorting algorithms like Bubble Sort, Selection Sort, and Insertion Sort, which are relatively straightforward to understand. Grasping these will help you comprehend the principles of algorithm efficiency and data manipulation. Use pseudocode, a high-level description of an algorithm that uses the structural conventions of programming languages, to outline these simple algorithms. This will allow you to focus on the logic without getting bogged down by syntax.

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Muhammad Yaman Ghaibeh

Senior AI Engineer @ Telecel Global
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Don't be afraid of pen and paper! Sometimes the best way to grasp a new algorithm is to step away from the code and sketch out the steps by hand with a simple example.

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Ajeet Singh

SDE-3 @ PayPal | Ex - Walmart
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Set Goals -> Define your objectives, whether it's mastering problem-solving or preparing for coding interviews. Learn Basics -> Start with fundamental data structures (arrays, linked lists) and algorithms (sorting, searching). Resources -> Utilize online courses, textbooks (e.g., CLRS), and coding platforms (LeetCode, HackerRank). Practice: Solve a variety of algorithmic problems to reinforce understanding. Implementation -> Code algorithms in your preferred language to deepen comprehension. Review -> Regularly revisit learned concepts and analyze their time and space complexity. Consistency -> Dedicate consistent time for learning and practice to make steady progress.

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2 Online Resources

Utilize online resources such as free courses, tutorials, and forums. There are numerous platforms that offer structured learning paths for algorithms and data structures. These resources often include interactive exercises that allow you to apply what you've learned immediately. Engaging with online communities can also provide support and additional perspectives as you work through challenging concepts. Remember, the goal is to understand the 'why' behind each algorithm, not just the 'how'.

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Muhammad Yaman Ghaibeh

Senior AI Engineer @ Telecel Global
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Don't just watch videos, do the exercises! The hands-on coding is where the real learning happens, even with beginner-level algorithms.

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3 Practice Coding

Dive into coding by implementing algorithms in a programming language you are comfortable with. Writing code helps solidify your understanding and gives you hands-on experience with algorithmic thinking. Start with simple problems and gradually increase the complexity as you become more confident. Use an Integrated Development Environment (IDE) to write, debug, and test your code. Practice is key; the more problems you solve, the better you'll understand the nuances of different algorithms.

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Muhammad Yaman Ghaibeh

Senior AI Engineer @ Telecel Global
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Remember, there's a certain joy in seeing your algorithm actually work! That moment the output is correct, even after hours of tinkering, is worth all the struggle. That feeling is what keeps you hooked on coding.

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4 Study Theory

While practice is essential, don't neglect the theoretical underpinnings of algorithms. Study Big O notation, which is used to classify algorithms according to how their running time or space requirements grow as the input size grows. Understanding this concept is critical for analyzing the efficiency of algorithms. Read about different algorithm design paradigms such as divide and conquer, dynamic programming, and greedy algorithms. A solid theoretical foundation will enable you to approach new problems methodically.

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Muhammad Yaman Ghaibeh

Senior AI Engineer @ Telecel Global
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Sometimes theory can feel dry, but remember: those concepts are what let you predict if your algorithm will take seconds or hours to run on a massive dataset. That's the difference between a cool idea and a solution that actually works in the real world.

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5 Challenge Yourself

As you gain confidence, challenge yourself with more complex problems. Websites that offer coding challenges can help you apply your knowledge in a variety of contexts and against a community of peers. These platforms often have problems categorized by difficulty and algorithm type, allowing you to focus on specific areas for improvement. Solving these problems can be incredibly satisfying and is a testament to your growing skills.

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6 Reflect Regularly

Lastly, make time for reflection and review. After solving a problem or learning a new concept, take a moment to reflect on what worked well and what could be improved. This could involve revisiting your code to optimize it or reviewing the theory to ensure you've understood it fully. Reflection is a powerful tool for learning, as it helps consolidate knowledge and prepares you for future challenges.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What do you do if you want to learn Algorithms at your own pace?

1

2

3

4

5

6

7

1 Start Simple

2 Online Resources

3 Practice Coding

4 Study Theory

5 Challenge Yourself

6 Reflect Regularly

7 Here’s what else to consider

Algorithms

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What do you do if you want to learn Algorithms at your own pace?

1

2

3

4

5

6

7

1 Start Simple

2 Online Resources

3 Practice Coding

4 Study Theory

5 Challenge Yourself

6 Reflect Regularly

7 Here’s what else to consider

Algorithms

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