How To Become A Successful Machine Learning Engineer ?
Who are machine learning engineers and why do employers need them?
A Machine learning engineer is anyone with the programming skills to develop or apply software algorithms that train computers to apply certain knowledge in different fields. People with this skill are in high demand in all fields of industry, from robotics and science to social media and retail.
Demand for machine learning engineers and other AI talent is rising. Indeed.com registered a 182% increase in searches related to AI and machine learning. More so, 94% of AI jobs were specifically looking for machine learning engineers and up to 41% of these job postings stayed open well after 60 days, a sign that there is room for a lot of talent.
What skills do you need to become a machine learning engineer and how do you develop those skills?
To become a machine learning engineer, you need the following skills:
Programming and Computer Science
A poll by KDnuggets found that Python and R are some of the most popular programming languages in the field in the field of machine learning.
In addition to programming, you should understand and be able to apply the basics of computer science such as data structures, computer architecture, and algorithms for searching, optimization, sorting etcetera
Understand Statistics and Probability
The Machine learning framework is based on statistical probabilities. The underlying principle of Probability is that events are unpredictable, hence rules are important to minimize error and increase the probability. As such, an understanding of probability concepts and techniques such as Bayes rule and Bayes Nets, Markov Models, Decision Processes, independence, and the likelihood is important.
Statistics will help you to build and validate data testing models and you must, therefore, know your way around distributions, analytical methods, and statistical measures such as median, mean and variance.
Know how to model and evaluate data
As a machine learning engineer, you should be able to work with data sets, to classify it, identify patterns such as correlations and to predict regressions or anomalies. You should also know how to continually evaluate and tweak your models to ensure the accuracy of your algorithms.
System Design and Software Engineering
Machine learning code is part of a larger software ecosystem. Software engineering skills will help you understand how the code fits and works together with other pieces in the system. The interfaces you build need to be dependable, otherwise, you risk introducing bottlenecks with algorithms that cannot scale with increasing data. To create quality algorithms that will boost productivity and enhance collaboration, you need to acquaint yourself with software engineering best practices.
Be familiar with machine learning frameworks
As a software engineer, you will be working with machine learning frameworks such as TensorFlow, PyTorch, scikit-learn, Google Cloud ML Engine, among others. Frameworks take care of the grunt work during testing and configuration. Each framework has its own priorities as well as strengths and weaknesses. While some frameworks prioritize ease of training, others emphasize parameter optimization or focus on rapid deployment.
Your choice of the framework should also be informed by:
Your programming language
For instance, TensorFlow is Python-based, so it would make sense for you to use tensorflow if you are familiar with python.
The type of data set you will be working with
Caffe, for instance, would be ideal if you will be working with a lot of images.
Other frameworks like The Microsoft Cognitive Toolkit (CNTK) have wider use, lots of advantages and are open source.
Which courses are the best machine learning course?
Whether you are a beginner or have intermediate or expert knowledge of AI and machine learning, you will benefit by taking these courses.
Machine learning offered by Stanford is one of the most comprehensive courses offered online. It was created by Andrew Ng, who is one of the founders of coursera and also an Adjunct professor at The Leland Stanford Junior University, California. It’s an 11-hour course, with a mix of supervised and unsupervised learning, all offered in the form of videos, readings and practice scenarios.
It is a great course for beginners as it teaches the core ideas behind machine learning application, by providing a better understanding of concepts such as Statistical modeling, data classification, neural networks, linear algebra, among others.
Cost: (you have the option to purchase a certificate or access the course for free)
If you have a background in R and statistics, this course is for you. The course is also structured to focus on specific skills or to advance key areas in a career. Some of the concepts covered throughout the course include:
Classification techniques such as decision trees and K-nearest neighbors
Principles of clustering, including use cases
Performance measures for your algorithms, including learning concepts such as variance and bias
Regression algorithms and their application to machine learning
Cost: Free subscription and paid subscription at $29 per month (the first chapter is free)
Created by Jerry Kurata, this course is a great introduction to Python, scikit-learn and Jupyter notebook. It’s a great course for you have if you are already familiar with statistics and software development (not necessarily python), and want to branch into machine learning. By the end of the course, you will know how to build and use predictive models and other machine learning solutions. Teams and Enterprise users can also access a host of courses tailored to expand specific skills.
personal plan: $29/month or $299 billed annually.
Premium plan: $449 billed annually
This course uses tensorflow API for model configuration, Pandas Library for data set manipulation and Python for coding. Before you take the course, you should be familiar with these areas. In addition, you should have a good understanding of linear algebra.
The course is delivered through a mix of video lectures, practice exercises, and case studies and Kaggle competitions, all geared to increase understanding of machine learning application in the real world.
Google also offers separate courses in data preparation and problem framing, from a machine learning point of view.
This course uses Naive Bayes with scikit learn in python. Udacity offers this free course as part of their nanodegree, to introduce anyone who wants to turn raw data into meaningful trends and predictions. By the end of the course, you will have learned how and where to apply machine learning, how to investigate data how to implement projects in real life, in addition to coding your own decision trees and choosing your algorithms.
Some key features of the course include interactive quizzes and self-paced learning, so it’s great for you if you are a beginner, prefer lots of hands-on experience and a relaxed pace of learning. Udacity offers many other free courses in other machine learning areas like model building and validation, unsupervised learning, classification models and inferential statistics.
If you want to advance your skills and get certified, you can pay for the machine learning nanodegree.
Cost for the nanodegree: $999
Machine learning from Simplilearn is an intermediate-level course suitable for developers, analysts, data science graduates, and information architects. It features lots of hands-on exercises and practice using data from real-life scenarios. With instructor-led and self-paced course models, this course is made to suit different study habits and styles; from those who want to accelerate their learning to those who prefer relaxed self-paced learning. Throughout the course, you will learn concepts and techniques such as supervised and unsupervised learning, practical use of algorithms, application of mathematical aspects to machine learning, use of vectors, decision trees, and many other concepts.
A key unique feature of this Simplilearn course (and other courses) is that students can receive dedicated mentoring from industry experts.
$299 for self-paced learning
$399 for instructor-led learning
The roles and responsibilities of a machine learning engineer
The roles and responsibilities of a machine learning engineer vary from one company to another, although the core function of an engineer in this field is to implement algorithms to the company’s data, in order to make predictions or solve problems.
The below screenshots of machine learning job postings on glassdoor should give you an idea of the responsibilities you should be prepared for.
Hourly jobs are also available across multiple job platforms, and these are ideal for freelancers or anyone who wants to work part-time.
Bottom line: Career opportunities for machine learning engineers
There is a general consensus among industry experts that Machine learning is growing, as shown in the below screen captures.
You can see more of the same here.
By 2025, it is projected that enterprises will have invested up to $232 billion in the field of AI and machine learning compared to the current $12.4 billion, according to a report by KPMG. With many companies racing to invest in machine learning, the future looks bright for machine learning engineers.
In fact, if you search on google for “companies looking for machine learning engineers”, the first 3 results on the search results page show the existing demand for machine learning jobs from industry giants such as Amazon and Facebook, and other large and mid-sized enterprises across the world.
This is an indication that an investment in a machine learning course is a step in the right direction for your career.