Many of the programmers, beginners, or experienced ones aren’t aware of this, and many avoid learning it. We’re talking about Data Structure And Algorithm for Machine Learning. So is Data Structures And Algorithms important for Machine Learning?
People are still confused about this topic and are reaching out to Quora and Reddit platform to find answers. Hence, we’ve made a guide to give a clear picture for you.
If you’re on the path to learn the Machine Learning or already an ML practitioner, then this is for you. Learn Data Structure and Algorithms before it’s too late. A lot of programmers who skipped this step are finding ways to learn it now.
If you’re looking for the best resource to learn it then, we’ve outlined a list of 21 best courses to learn data structure and algorithm. Pick any of the courses to start your learning journey to write a better readability code.
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How Important are Data Structures and Algorithms for Machine Learning?
Did you know how important are Data Structures and Algorithms for Machine Learning in 2020? Every program or every line of coding that you do depends on algorithms and data structure.
But what really is the data structure and algorithm, and how important is it? For building simple Machine Learning models for small projects, it is not so important or required. This can be done with the Python programming language or Matlab environment.
But if your focus is to implement Machine Learning on a large dataset or for career, then yes. To manage these huge datasets, you should have an optimized code. This is done in the step of algorithms. To effectively initialize the data, you need to have data structure skills.
Now let us understand what they really are from the basics.
What is Data Structure?
In simple words, it is a container to store data in a certain layout. This layout lets a data structure to be efficient in some ways and not in some. By learning data structures, you can pick the right layout to solve a given problem. The good thing about the data structures is that in a programming language, you can effortlessly implement vectors and matrices.
What is Algorithms?
Algorithms are nothing but a set of instructions that are written in a sequence. By having the right sequential order, one can solve problems and get the expected results.
Now the big question on your mind.
Do I Need to Study Algorithms and Data Structures to Learn Machine Learning?
TL;DR, yes. Read on to know why?
Learning to build and train a neural network can be done with the current programming skills you have. In fact, there are several short 2 hour long projects on Machine Learning on Coursera. And in Amazon Sagemaker has many pre-built Machine Learning models available for everyone.
But if you want to design a neural network, then you need to implement algorithms and data structure in a programming language.
However, for the Data Scientist career path, you need not worry about data structures and algorithms. It is the same for software developers. To be successful in the Machine Learning carer, you need to learn algorithms and data structures to create your own algorithms.
It is not mandatory to learn data structure and algorithms before machine learning. You can get into the learning program even after finishing a Machine Learning course. Here is the course you can take to start learning Machine Learning algorithms with certification.
Machine Learning Algorithms Online Course
Machine Learning: Algorithms in the Real World Specialization
With this course, you will be able to implement an ML algorithm for real-world applications. The specialization course is designed by Alberta Machine Intelligence Institute and available in Coursera. The bonus thing is that if you’ve Coursera Plus subscription, then you can start learning right away.
The prerequisite to enroll in this course is background knowledge in linear algebra, matrix multiplication, analytics, statistics, and Python programming language. The specialization consists of 4 courses. It teaches you to apply ML to data analysis and automation.
Machine Learning Algorithms will teach you to define, train, and maintain an ML application in the real world. Students will understand and apply supervised learning techniques based on real-world case studies. You also build skills on data preparation steps to describe common production issues in applied ML.
Key takeaways of this course:
- Learn the essential skills to write Machine Learning algorithms.
- Learn about obtaining data and understand many sources of training data.
- Define a Machine Learning problem and prepare data for effective ML applications.
- Transform the business needs to Machine Learning application.
- Understand the critical elements of data in the learning, training, and operation phases.
- You will learn how to deal with the changing data.
- Learn all the necessary tools you need for an ML project and prepare to optimize it.
- Demonstrate how the accuracy of your ML model can be improved.
The course duration is 4 months, where the learners have to at lease dedicate 2 hours per week. The more time you dedicate to learn Data Structures and Algorithms for Machine Learning, the sooner you complete. So far, over 6k students have enrolled in this course, and over 600 students have given an average rating of 4.6 out of 5.
By the end of the course, you’ll have to apply all the skills learned in this course in the final capstone project. Also, you will be trained to expect common pitfalls and mitigate it in applied Machine Learning.
After completing the course, you’ll be awarded with a certificate from Coursera. You can share this with your new employer to have a better chance of getting hired.
Summary: Is Data Structures And Algorithms Important For Machine Learning?
Although Data Structures and Algorithms are not prerequisites for Machine Learning, it is indeed crucial for a successful Machine Learning Career. If you’re on this path, you shouldn’t be taking any chance of not learning it.
The more skill you show in your resume, the better chance you have to clear the interview round. So it is safe to say Data Structures and Algorithms are essential in Machine learning.
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