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Machine learning has infiltrated much of the technology consumers and businesses use on a regular basis. Widespread adoption of this subset of artificial intelligence may have seemed futuristic not that long ago, but machine learning now runs behind popular applications such as online shopping, music streaming and car navigation systems. In fact, according to the Gallup and Northeastern University survey, "Optimism and Anxiety: Views on the Impact of Artificial Intelligence and Higher Education's Response," 85% of Americans already use AI applications powered by machine learning every day.
It's no surprise, then, that career opportunities are multiplying for those with machine learning skills. Although some industries faced labor reductions due to the automation machine learning can enable, the number of jobs created by the technology tripled between 2015 and 2019, leading to a shortage of qualified candidates, according to the "Gartner AI and Machine Learning Development Strategies Study." In addition, those positions are not limited to the IT department; they span across business units including finance, customer service, marketing, sales and research and development.
Another study published by job market analytics firm Burning Glass identified artificial intelligence (AI) and machine learning as one of the "most disruptive skills in tech," predicting that jobs requiring these skills will grow 71% from 2021 to 2026.
Break into a machine learning career
For those hoping to take advantage of the growing job market, now is a good time to begin learning about machine learning. Job seekers looking to build machine learning or AI expertise can pursue certifications such as Arcitura Education's Certified Machine Learning Specialist and Certified AI Specialist accreditations. Arcitura is a global education provider offering vendor-neutral training and certifications across a wide range of technical topics, including big data, cloud, security and digital transformation.
The Certified Machine Learning Specialist program includes three modules designed to teach participants about machine learning practices, models and algorithms; how to use machine learning systems to perform a range of data analysis processing tasks; and how to apply these concepts to real business problems.
The lessons listed below are select excerpts from Arcitura's program and provide an overview for anyone interested in working with machine learning and how to use its practical applications to achieve business goals. The first three lessons provide introductory content. The subsequent lessons delve further into individual machine learning techniques and practices. Each lesson documents the business problem addressed by the technique, the solution provided by the technique and how the technique is applied.
Get started with the machine learning course
Read each lesson below. Additional topics will be added on a regular basis.
Lesson 1: Introduction to using machine learning
Get an introduction to machine learning that explains the basic concepts of algorithms, models and model training, in preparation for putting machine learning practices and patterns to work solving real problems.
Lesson 2: The "supervised" approach to machine learning
Supervised learning is one of the most common methods of machine learning, and is particularly useful for financial predictions, fraud detection and risk assessment, among other things. Learn how to use the supervised learning to produce the best predictions.
Lesson 3: Unsupervised machine learning: Dealing with unknown data
The unsupervised learning model of machine learning uses specific algorithms to deal with unclassified and unlabeled data. This lesson explains the model, dimension reduction algorithms, the concept of reinforcement learning and more.
Lesson 4: Common ML patterns: central tendency and variability
Four common patterns provide approaches to solving machine-learning problems. Learn how two -- central tendency computation and variability computation -- work.
Lesson 5: Associativity and graphical summary computations aid ML insights
Associativity computation and graphical summary computation allow for more complex insights, and in turn improve predictions. Explore how these ML techniques work in practice.
Lesson 6: How feature selection, extraction improve ML predictions
In this discussion of machine learning patterns, learn how feature selection and feature extraction help make data more useful and, thus, improve predictions.
Lesson 7: 2 data-wrangling techniques for better machine learning
Before data can be usefully inputted into algorithms, it must first be prepared. Learn two of the techniques -- feature imputation and feature encoding -- that do the job and make machine learning work.
Lesson 8: Wrangling data with feature discretization, standardization
A variety of techniques help make data useful in machine learning algorithms. This article looks into two such data-wrangling techniques: discretization and standardization.
Lesson 9: 2 supervised learning techniques that aid value predictions
Learn how two supervised machine learning techniques -- numerical prediction and category prediction -- work to predict values and, thus, can aid model training.
Lesson 10: Discover 2 unsupervised techniques that help categorize data
Two unsupervised techniques -- category discovery and pattern discovery -- solve ML problems by seeking similarities in data groups, rather than a specific value.
Lesson 11: Model evaluation techniques
Lesson 12: Model optimization techniques I
Lesson 13: Model optimization techniques II
After exploring these lessons, readers will have a solid understanding of machine learning that will help them succeed in a machine learning certification program. For more on Arcitura Education's Certified Machine Learning Specialist and additional training and certification options, visit Arcitura Education.