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Six Steps to Mastering Machine Learning Engineering and Becoming a Successful Machine Learning Engineer | Technology and Education

Six Steps to Mastering Machine Learning Engineering and Becoming a Successful Machine Learning Engineer | Technology and Education
Six Steps to Mastering Machine Learning Engineering and Becoming a Successful Machine Learning Engineer 

How to Master Machine Learning Engineering and Become a Successful Engineer in Six Easy Steps

There is a vast amount of information accessible about machine learning today.  The most important requirement, however, is a reliable learning path.  Anyone interested in ML engineering careers should take a look at this six-step guide.

Are you a software engineer, programmer, developer, data scientist, or computer engineer looking to enter the lucrative field of machine learning?  If you are successful, you will be in good company.  The report indicated that the demand for AI and machine learning 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. by year.

But before embarking on a career transition to ML engineering, there are a few things to keep in mind.  First, ML engineering is not an entry level position.  Requires a bachelor's degree in mathematics, data science, computer science, computer programming, or a related field.  Many ML engineering positions also require a master's or Ph.D.  in one of those disciplines.  After obtaining the appropriate degree, it usually takes several months or even years before one is competent in the field.  So where should you start?  When learning any new field, start with the basics.

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Machine Learning Engineer Knowledge Map: Six Steps to Master the Basics of Machine Learning 

Those who work as ML engineers recommend that one should follow an established learning path to master the basics of machine learning before seeking the top ML engineering position.  On average, if one spends four to five hours a day mastering the steps along the ML learning path, it should take six months to a year to complete. Here are six steps to master the basics of ML:

Step 1. Learn Advanced Math

Machine learning and related algorithms require a deep understanding of advanced mathematics, in particular linear algebra, calculus, probability, and statistics.  The goal of this first step in the ML learning path is to gain an understanding of advanced mathematical concepts that apply to ML.  Here are some resources to get you started:

Khan Academy

This website provides videos, examples, and practice problems for learning linear algebra, calculus, statistics, and probability.

Coursera Mathematics for Machine Learning Specialization

This program teaches math concepts as they are used in machine learning, including linear algebra and multivariate calculus.

An Introduction to Statistical Learning with Applications in R by James, Witti, Hastie, and Tikshirani

This book will help you understand the math of machine learning, especially as it relates to the R programming language. 

Linear Algebra and Optimization for Machine Learning by Aggarwal

This graduate textbook introduces linear algebra and optimization in the context of machine learning.

Data science and machine learning: mathematical and statistical methods of Kroese, Botev, Taimre and Vaisman 

This graduate textbook introduces the mathematics behind machine learning techniques, especially probability/statistics.

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Step 2. Gain proficiency in ML programming

To get a machine learning engineering job, computer programming experience 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:

Coursera Python specialization for everyone

This module introduces basic programming concepts such as data structures, network application programming, and databases using Python.  Students build and create data retrieval, processing, and visualization applications using the technologies studied during this specialization.

Coursera R Programming

This course will teach you how to code in R and how to use R for data analysis.  The course covers practical problems in statistical computing, including programming in R, reading data in R, accessing R packages, writing R functions, debugging, profiling R code, and organizing and commenting on R code. R code. The statistical data analysis topics will provide practical examples.

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Step 3. Master 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 the best way to produce models that will generate the information an organization needs to make better business decisions.  Therefore, the ML engineer must master 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 you started:

W3Schools SQL Tutorial

This online SQL tutorial teaches how to use SQL in MySQL, SQL Server, MS Access, Oracle, Sybase, Informix, Postgres, and other database systems.

Coursera Learn SQL Basics for Data Science Specialization

Through four progressively more difficult SQL projects with data science applications, this course covers topics such as SQL basics, data management, SQL analysis, AB testing, and distributed computing using Apache Spark and Delta Lake.  Covers the application of SQL to analyze and explore data, write efficient queries;  Create data analytics datasets, perform feature engineering, and use SQL with unstructured datasets and other machine learning and data analytics toolsets.

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Step 4: Learn to use algorithms to build ML models

With a background in programming, advanced math, and SQL used in machine learning, you're ready to move on to 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 provided by the ML engineer.  After training, a machine learning model is produced.  Therefore, 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:

Coursera Machine Learning

This introductory course covers machine learning, data mining, and statistical pattern recognition in depth.

Coursera Machine Learning with IBM Python

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.

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Step 5: Learn 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 you started, here are two online courses to master TensorFlow:

Coursera TensorFlow in Practice Specialization Program

Covers best practices for TensorFlow, building NLP systems with TensorFlow, and handling image data.

Udacity's introduction to machine learning with TensorFlow

This program covers basic machine learning algorithms, from data cleaning and supervised models to deep learning and unsupervised models.  Furthermore, it allows students to apply their skills to projects relevant to key industries.

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Step 6: Practice

Once you've mastered the materials covered in the previous steps, you're ready to build your machine learning model by working on a few machine learning projects.  The more projects you do, the more you can add to your resume to show potential employers that you have a working knowledge of machine learning.  Projects can range from image/voice recognition, classification and disease prediction to sentiment analysis or stock price prediction.  But what projects?  Here are some resources that can give you some project ideas and data sets to work on:

Kaggle

It is a popular machine learning contest platform where you can practice your ML skills on real data.  Kaggle provides over 68,000 public data sets for free download.

Data Camp

This website offers several real-world projects that you can choose from to apply your new ML skills.

This article describes eight projects for beginners and provides links to relevant tutorials and where to find free datasets.

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Beyond the Basics of Machine Learning 

This learning path should prepare you for your first ML engineering role.  However, you are not done learning.  Here are two areas for advanced students to consider.

Get proficient with deep learning algorithms

When working with unstructured data and very large data sets, such as those found in voice and facial recognition ML applications, 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 mastering deep learning algorithms:

Coursera Deep Learning Specialization

This advanced course series covers the use of Python and TensorFlow for deep learning and neural networks.

Udacity Introduction to Tensorflow for Deep Learning

This program uses TensorFlow to teach how to build deep learning applications that can be used on mobile devices, in the cloud, and in browsers.  It also covers advanced techniques and algorithms for working with large data sets.

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Learn Visualization Tools 

Knowledge of visualization tools like Tableau and PowerBi is essential to demonstrate the findings, patterns, and predictions of a model.  Here are two resources to get you started:

Coursera Data Visualization with the Tableau Specialization

Using Tableau, this beginner's data visualization course teaches how to build reports and dashboards that can help users make decisions based on their business data.

CodeAcademy Visualize data with Python

This course covers how to create data visualizations using Python, MatPlotLib, Seaborn, Pandas, and Jupyter Notebooks.

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Where to go from here

The learning of a machine learning engineer 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 obtaining a professional certification.  One of the most popular certifications for machine learning engineers is AWS Certified Machine Learning - Specialty.

Source: Toolbox, Direct News 99