Have you a Mathematics love? Do you have a passion for Advanced Computer Science skills? Do you like to make machines that work with intelligence? Then Machine Learning is a perfect career for you. As a subpart of Artificial Intelligence(AI) and having a connection with Data Science, Machine Learning is a perfect combination of Data and Algorithms. Here is an ideal roadmap that will guide you to become an excellent Machine Learning(ML) engineer quickly. Let’s have a clear vision about how to learn ML by yourself.
1. What is Machine Learning?
Machine learning is the usage and development of computer systems that can learn and adapt without being given explicit instructions utilizing algorithms and statistical models to analyze data patterns and derive conclusions.
2. What is the salary of an ML engineer?
As it is clear that AI/ML is the future, the pay will be obviously good. According to data from various sources, the average salary of an ML engineer in India is 7.2 lacks per annum (60k per month). In the USA the wage is $122673 per annum.
3. What are the essential skills an ML engineer needs?
Think that can you get paid 1 million dollars per month without doing anything? No, it’s not possible. You have to have a vast amount of knowledge and sharp skills. They include Mathematics, Python, Data Structures and Algorithms, Database manipulation, Programming libraries, and knowledge of working with software tools and machine learning itself.
4. What to learn to become a Machine Learning engineer? (Roadmap to ML)
The following are the things you have to cover to become a capable machine learning developer.
1) Computer Science basics
If you are a non-CS/IT person then you have to learn about Computer parts, Bits, and bytes, knowledge of basics of programming, and concepts of Object Oriented Programming Structure.
2) Level 1 coding tutorials
A good ML engineer must have to be a good Software engineer first. So you have to learn to code. The most popular and effective language for ML/AI/DS is Python. Learn Python as much as possible. Now some companies also prefer the C++ language as it is faster. So you should also be ready for C++ programming.
3) Data Structures and Algorithms
It is the soul of Computer Science and Programming. Especially in Machine Learning where an algorithm is king. You have to learn stack, queue, various sorts, binary search, time and space complexity, Prims and Krushkal’s algorithms, Greedy and Dynamic methods, etc.
4) SQL basics
A good software engineer should know how to create and manipulate a database. So as an ML cum SW engineer you should know the basics of Structure Query Language(SQL) and how to create and manipulate databases using it.
5) Python Libraries
ML works with different types of processes to make the machines(computers) decision-makers. To do this process you have to learn Python libraries like Numpy, Pandas, Matplotlib, OpenCV, & Seaborn These Libraries are the base of your ML career so don’t take them liberally.
Research says that you can learn computer skills without mathematical skills. In short, Maths is a base of all engineering and CS as well. For ML you have to learn statistics, calculus, and linear algebra.
7) Machine Learning itself
Now you have to give maximum time to this section in which you have to learn statistical ML using sci-kit Learn, Classification and Regression models, classification Random forest, Decision tree, and all that you have to prepare.
8) Deep Learning
As a subdomain of ML, DL comes in many concepts of ML. In DL you have to prepare Different neural networks like artificial neural networks, convolutional neural networks, and recurrent neural networks.
9) ML Ops
You have to learn at least one Machine Learning life cycle tool. ML-Flow is an ideal one.