How to Become a Machine Learning Engineer?
Machine Learning is an amazing area of Science and Technology, you knowingly or unknowingly use in your day-to-day life. From Weather forecasting, Self-driving cars, Autopilots, and even Cancer cell recognition systems, Machine Learning is everywhere. It is a branch of Artificial Intelligence that helps computer systems to be improved by analyzing data. Machine Learning is one of the high-demand industries in the 21st Century. Machine Learning Algorithms, designed by Machine Learning Engineers, help Computer Systems to learn from provided data by analyzing that and making predictions. I will be sharing a roadmap to becoming a Machine Learning Engineer for beginners, in this article. This is not a complete roadmap, none can be complete, but this is a great one for getting started with Machine Learning Engineering.
What is Machine Learning?
According to Wikipedia, “Machine learning (ML) is a field of inquiry devoted to understanding and building methods that ‘learn’, that is, methods that leverage data to improve performance on some set of tasks. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as training data, in order to make predictions or decisions without being explicitly programmed to do so.” Machine Learning is a sub-division or a branch of Artificial Intelligence. Machine Learning Engineers design and develop Machine Learning algorithms, which are able to analyze data and make predictions according to the data.
What are the Prerequisites for Machine Learning Engineering?
There are some prerequisites you should know before getting started with Machine Learning actually. You should know some basic and advanced mathematics, at least one programming language that has machine learning frameworks, basic concepts of Data Structures and Algorithms, Object Oriented Programming and some basic concepts of Data Science.
(1) Mathematics : You must know the Elementary Mathematics for Machine Learning Engineering. If you don’t know mathematics, you cannot understand the concepts of Machine Learning. Linear Algebra, Calculus, Statistics and Probability are very important as data analysis and processing are two of the most important works of machine learning algorithms. Besides these elementary and advanced concepts of mathematics, Discrete Mathematics is very helpful and important as well.
(2) Programming Language : Machine Learning Algorithms are developed using programming languages. Developers or Engineers write codes or programs to build Algorithms. There are a lot of amazing programming languages for Machine Learning. Among these, Python, R, Java, JavaScript, C++, C# and Go are the most popular. Whatever the programming language is, you have to know the fundamentals of the language clearly and perfectly.
(3) Data Structures and Algorithms : Besides the fundamental concepts of computer programming, you should know the concepts of object-oriented programming and some data structures and algorithms. You may apply object-oriented concepts using one of the seven programming languages mentioned above, Python, R, Java, JavaScript, C++, C# and Go. Data structures and algorithms are also important as they help you to save both your time and your computer’s memory.
(4) Data Science Frameworks : You should know some frameworks or libraries of the programming language you have learned, for processing and analyzing data. The data science libraries and frameworks are used a lot in machine learning projects. So, data science is very beneficial and important for machine learning engineers to develop algorithms that work with data, and process and analyze them to make predictions and decisions.
How to become a Machine Learning Engineer?
If you know the concepts mentioned above, you're ready to learn the concepts of machine learning. To become a machine learning expert, you should follow the following steps mentioned below —
(1) Theory of Machine Learning : First of all, learn the theories of Machine Learning and Machine Learning Algorithms. Learn the major steps of machine learning like Collecting and Evaluating Data, Choosing, Training and Evaluating Models, Parameters Tuning and Making Predictions. You should have a clear understanding of Introduction to Machine Learning, How Machine Learning Works, Types of Machine Learning, Supervised and Unsupervised Learning, Linear and Logistic Regression, Difference between Logistics and Linear Regressions, Confusion Matrix, Principal Component Analysis, Reinforcement Learning, Regularization and some other important concepts of Machine Learning.
[Note: You should not skip this step, the theoretical part is very important, even it is more important than the application. So, don’t skip the theoretical part.]
(2) Machine Learning Libraries and Frameworks : There are a lot of frameworks and libraries created for machine learning engineers to develop machine learning algorithms. Python, R, Java, JavaScript, C++, C# and Go have some great frameworks for machine learning. A list of some frameworks is provided at the end of the article. You just have to explore some popular frameworks and use the best for you on your projects.
(3) Computer Vision : According to IBM, “Computer vision is a field of artificial intelligence (AI) that enables computers and systems to derive meaningful information from digital images, videos and other visual inputs — and take actions or make recommendations based on that information. If AI enables computers to think, computer vision enables them to see, observe and understand.” I think you have already understood the importance of Computer Vision, it’s very important.
(4) Machine Learning Projects : You cannot become a poet without writing poems. You cannot become a mathematician without solving mathematical problems. So, how can you become a Machine Learning Engineer without creating and working on Machine Learning Projects? If you work on projects, your concepts will be clearer and your skills will be more enriched. If you don’t work on or create projects, you cannot be a Machine Learning Engineer ever, whatever you know, whatever your skill is. So, creating or working on projects is very important.
What Next? (Deep Learning)
Now you know the concepts of Machine Learning and Machine Learning Engineering. But as I have mentioned earlier that this roadmap is not a complete roadmap, but a great one to get started, you may have already understood that this is the start only. You are just introduced to Artificial Intelligence. You should learn more and more and enrich your knowledge.
Deep learning is a sub-field or sub-branch of Machine Learning. According to Wikipedia, “Deep learning (also known as deep structured learning) is part of a broader family of machine learning methods based on artificial neural networks with representation learning.” You may get started with Deep Learning Engineering after learning Machine Learning Engineering.
Machine Learning Libraries and Frameworks
Here is a list of some famous and popular machine learning frameworks, that you should try or explore, given below —
- TensorFlow is a free and open-source software library for machine learning and artificial intelligence, developed by Google. TensorFlow is written in Python and C++.
- OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library. OpenCV is written in C, C++, Python, Java, and Assembly language (JavaScript is also supported).
- dplyr is primarily a set of functions designed to enable data frame manipulation in an intuitive, user-friendly way (- Wikipedia). dplyr is written in the R programming language.
- Neuroph is a lightweight Java neural network framework to develop common neural network architectures, contains well designed, open-source Java library, that has GUI neural network editor to quickly create Java neural network components.
- Caffe is a deep learning framework made with expression, speed, and modularity in mind, that was developed by Berkeley AI Research and community contributors at the University of California, Berkeley. Caffe is written in C++, with a Python interface.
- GoLearn is a ‘batteries included’ machine learning library for Go. It is written in the Go programming language.
- ML.NET is a free, open-source, cross-platform machine learning framework, extended with TensorFlow. ML.NET supports the C# and the F# programming languages.
These are some of the most popular Machine Learning frameworks you may explore. There are a lot of good frameworks available for machine learning engineering. You should explore these and apply your favourite ones to your projects.
Machine Learning is a big picture, you may not learn everything and every concept of machine learning, no person can do this. But you should keep learning continuously, and create a lot of projects. And when you are comfortable with Machine Learning, have created a lot of projects, and have a mentality to learn continuously, you may be called a Machine Learning Engineer. At the end of this article, I will suggest you keep trying, keep learning and focus on fundamentals to make your fundamental knowledge strong. Thank you.