The road to become a machine learning engineer is long yet rewarding. As the need for ML expertise grows, more engineers are pursuing certifications in this discipline. However, the intimidating bottom-up curriculum proposed by most ML specialists is enough to deter many newbies. This article helps to create an easy and efficient learning path for anyone interested in pursuing a career as an ML engineer.
Are you a software engineer, programmer, developer, data scientist or a computer engineer looking to enter the lucrative field of machine learning? If you succeed, you will be in great company. The Top 10 Tech Job Skills Predicted to Grow the Fastest in 2021 report stated that the demand for AI and ML skills will grow at a compounded rate of 71% through 2025. According to Glassdoor, Machine Learning engineers with two to four years of experience in the field earn an average salary of $124,422 per year.Â Â
But before embarking on a career transition to ML engineering, certain things must be taken into consideration. First, ML engineering is not an entry-level position. It requires an undergraduate degree in mathematics, data science, computer science, computer programming or a related field. Many ML engineering positions also require a master’s degree or Ph.D. in one of those disciplines. After attaining the appropriate degree, it typically takes several months or even years before one becomes proficient in the field. So, where should you begin? When learning any new field, begin with the basics.
Machine Learning Engineer Knowledge Map
Those who work as ML engineers recommend that one should follow a set learning path for mastering the basics of machine learning before setting out to find one’s first ML Engineering position. On average, if one spends four or five hours a day mastering the steps along the ML learning path, it should take six months to a year to complete.Â
Here are six steps toward mastering ML basics:
Step 1. Learn advanced mathematicsÂ
Machine learning and related algorithms need a thorough understanding of advanced mathematics, notably linear algebra, calculus, probability, and statistics. The goal of this first step in the ML learning path is to gain knowledge of advanced mathematics concepts as it applies to ML. Here are some resources to get started:
This website provides videos, examples and practice problems for learning linear algebra, calculus, statistics and probability.Â
This program teaches mathematics concepts as used in machine learning, including linear algebra and multivariate calculus.Â
- An Introduction to Statistical Learning with Applications in ROpens a new window by James, Witti, Hastie, and Tikshirani
This book will help one understand the mathematics of machine learning, especially as it pertains to the R programming language.Â
This graduate-level textbook introduces linear algebra and optimization in the context of machine learning.Â
- Data Science and Machine Learning: Mathematical and Statistical Methods Opens a new window by Kroese, Botev, Taimre, and Vaisman
This graduate-level textbook presents the mathematics behind machine learning techniques, especially probability/statistics.
Step 2. Obtain proficiency in ML programming
To obtain a machine learning engineering job, experience in computer programming is a must. Python is the most widely used programming language among data scientists; R is a close second, especially for ML projects that involve statistical operations. Here are two resources for mastering ML programming:Â
This module introduces basic programming concepts such as data structures, networked application programming, and databases using Python. Learners construct and create data retrieval, processing, and visualization applications using the technologies studied during this specialization.
This course will teach you how to code in R and how to use R for data analysis. The course covers practical issues in statistical computing, including programming in R, reading data into R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. Topics in statistical data analysis will provide working examples.Â Â Â Â Â
Step 3. Become proficient in data engineering techniques
To establish an ML model, data for training and testing is a prerequisite. ML engineers must be able to evaluate data streams to determine how to best produce models that will output the information an organization needs to make better business decisions. Thus, the ML engineer must be proficient in ETL (Extract, Transform and Load) tools, database management systems (such as SQL, Oracle and NoSQL) and the SQL programming language. Here are two resources to get started:
This online SQL tutorial teaches how to use SQL in MySQL, SQL Server, MS Access, Oracle, Sybase, Informix, Postgres, and other database systems.
Through four progressively more difficult SQL projects with data science applications, this course covers topics such as SQL basics, data wrangling, SQL analysis, AB testing, and distributed computing using Apache Spark and Delta Lake. It covers applying SQL to analyze and explore data, write efficient queries; create data analysis datasets, conduct feature engineering, and use SQL with unstructured data sets and other data analysis and machine learning toolsets.
Step 4: Learn how to use Algorithms to build ML models
With a background in programming, advanced math and SQL used in machine learning, you are ready to pursue step 4 of the ML learning path. ML programs must be trained before they can be put to use. ML programs are trained through algorithms with data given to them by the ML engineer. After training, a machine learning model is produced. Thus, machine learning engineers must be well-versed in the standard modeling algorithms used in supervised, unsupervised, reinforcement and deep learning. Here are three resources for learning the basics of machine learning modeling:
This introductory course covers machine learning, data mining, and statistical pattern recognition in depth.
This entry level course covers the fundamentals of machine learning and how to develop algorithms using Python.
This book demonstrates how to use Python to create machine learning algorithms.
Step 5: Learn how to work with ML Frameworks
Machine learning frameworks are an interface, library or tool for building ML models., such as PyTorch, Scikit-learn, Theano, and TensorFlow. The best way to learn these frameworks is through their official websites or an online course. To get started, here are two online courses for mastering TensorFlow:
It covers best practices for TensorFlow, building NLP systems with TensorFlow, and handling image data.Â Â
This program covers the foundational machine learning algorithms, from data cleaning and supervised models to deep learning and unsupervised models. In addition, it allows students to apply their skills to projects relevant to key industries.
Step 6: Practice
Once you have mastered the materials covered in the steps above, you are ready to build your machine learning model by working through some machine learning projects. The more projects you do, the more you can add to your resume to show prospective employers that you have a working knowledge of machine learning. Projects can range from image/speech recognition, classification, and disease prediction, to sentiment analysis or stock price prediction. But which projects? Here are some resources that can provide you with some projects ideas and datasets to work on them:
It’s a popular machine learning contest platform where you can practice your ML skills with actual data. Kaggle provides over 68,000 public datasets for free download.Â Â
This website provides several real-world projects from which you can choose to apply your newfound ML skills.
This article outlines eight projects for beginners and gives links to relevant tutorials and where to find datasets for free.Â
Beyond ML Basics
This learning path should prepare you for your first ML engineering position. Nevertheless, you are not finished learning. Here are two areas for advanced students that you should look into.
Become proficient with Deep Learning algorithms
When working with unstructured data and very large datasets, such as those found in speech and facial recognition ML apps, ML engineers will need a strong working knowledge of Deep Learning algorithms. Deep Learning incorporates neural networks that iteratively learn from data and is much better at working with large data sets than ML. Here are two resources for becoming proficient with deep learning algorithms:
This advanced course series covers the use of Python and TensorFlow for deep learning and neural networks.
This program uses TensorFlow to teach how to build Deep Learning applications that can be used on mobile devices, in the cloud, and browsers. It also covers advanced techniques and algorithms for working with large datasets.
Learn visualization tools
Knowledge of visualization tools such as Tableau and PowerBi is essential for demonstrating a model’s findings, patterns, and predictions. Here are two resources to start with:Â
Using Tableau, this beginning data visualization course teaches how to generate reports and dashboards that can help users make decisions based on their business data.Â
This course covers how to create data visualizations using Python, MatPlotLib, Seaborn, Pandas, and Jupyter Notebooks,Â
Where to go from here
A machine learning engineer’s learning never ends. There are always new algorithms, machine learning platforms, programming languages, and ML libraries to learn. Three resources for keeping up with new ML technologies are taking advantage of continuing education courses, belonging to professional organizations and earning a professional certification. One of the most popular certifications for machine learning engineers is AWS Certified Machine Learning â€“ Specialty.Â Opens a new window