Overview of Machine Learning for Business Leaders
Computers & Technology → Technology
- Author Chris Kambala
- Published July 31, 2019
- Word count 520
Introduction
Machine Learning has garnered a significant share of recent press coverage in both tech and main street media. It is inextricably intertwined with, and central to, discussion and dialogue on topics ranging from big data in general to Facebook’s threat to privacy, Boston Dynamics creepy robotics, and Google’s exploitation of artificial intelligence for good and ill. As such, it is easy to view machine learning as either sinister or magical — neither of which is true. For today’s business leader, an objective and actionable understanding of machine learning is as important as an actionable understanding of finance and financial management.
In this article, we provide an overview of machine learning for business leaders: what it is and how to think about its applicability to your business.
What machine learning is
Machine learning (ML) is a data-driven system development paradigm. ML systems leverage data models, data analysis and feedback to define and refine algorithms to improve model accuracy and system results.
ML systems work by analyzing data to detect patterns or by applying predefined rules to:
Categorize or catalog like objects
Predict likely outcomes or actions based on identified patterns
Identify unknown patterns and relationships
Detect anomalous or unexpected behaviors
Different algorithms learn in different ways. But in general, as new data are provided to the ML system the system "learns" and the algorithm’s performance improves over time.
Problems suited to machine learning
ML, like other software development paradigms, is not one-size-fits-all — some approaches are better suited to particular classes of problems and not suitable for others.
Machine learning is particularly suited to problems where:
Logical rules are unavailable or insufficient to describe the environment — but actionable rules can be intuited
Next actions are varied and the best action depends on conditions that cannot be identified in advance
Understanding why an outcome is suggested is not as important as the accuracy of the outcome
The data is problematic for traditional analytic methods
Now that you know what machine learning is and how to identify problems that lend themselves to ML solutions, let’s explore the steps to define and conduct an ML project.
How to plan and execute a machine learning project
Well executed ML systems follow these recommended steps:
Define Problem
Prepare Data
Evaluate Algorithms
Improve Results
Present Results
These steps, while seemingly generic and common to traditional software system development, require the perspective and attention gained from experience with ML system development.
The best way to approach machine learning system development is to work through an ML project end-to-end and cover the key steps with an experienced guide or team. Every step, from loading data, summarizing data, evaluating algorithms, making initial predictions, refining and presenting results is improved by experience — much like an ML system.
Accordingly, your first project should be viewed as a learning process to understand the mechanics of machine learning, calibrate your expectations and provide a perspective for setting expectations, interpreting and presenting results from dynamic, learning systems. After tackling your first project with the expert assistance you will be prepared to spot and sponsor the next, more consequential machine learning opportunity.
We provide a full range of custom software development services to assist Clients with solutions to their most important business and operational challenges. Our solution architects, developers, designers and engineers work with you to define, design and deliver exactly what your business needs.
Article source: https://articlebiz.comRate article
Article comments
There are no posted comments.
Related articles
- 10 Ways Business Central’s Quality Inspector App Streamlines Quality Assurance
- How EasyPDF™ Forms Save Time & Money at Home and in the Workplace
- The One and Only 15-Second Digital Lien Waiver to Complete and Submit in Record Time Using the Free Adobe Reader
- Augmented Reality (AR) in Business: Why Your Company Needs It
- Top 10 Reasons to Use Business Central’s License Plating App
- App Development: Transforming Ideas into Reality
- Eight Free Business Central Apps That You’ll Wish You Had
- How Artificial Intelligence (AI) and Machine Learning (ML) Are Transforming Computer-Based Trading Platforms
- The Role of Gas Engineers in Modern Energy Systems: Linking to Sustainability and Innovation
- The Significance of Stars in the Universe and Their Impact on Human Culture Throughout Evolution
- Exploiting Artificial Intelligence for Urban Mobility Transformation: A Case Study of Guatemala City
- Top 10 Ways Business Central Users Streamline Shipping
- The Impact of AI on Job Security and Availability in Africa: A Future at a Crossroads
- CNC Machining Vs 3D Printing: Which Technology Is Right For Your Project?
- The Future of Search: Embracing AI-Powered Search Solutions
- Low-Fidelity Vs High-Fidelity Prototypes: When To Use Each In Product Design
- MARKET SEGMENTATION
- Securing Data in the Cloud: Best Practices for the Oil and Gas Industry
- Key DevOps Practices: CI, CD, IaC, and Monitoring
- 10 Tips to Streamline Warehouse Operations with Business Central
- AI Admissions: Fair Selection or Digital Bias?
- How to Select the Best IT Recruitment Agency from Europe to Build Your Tech Team
- Evolution of the translation profession in the 21st century
- The Benefits of Open Source in Gaming and the Games It Made Possible
- Business Central Data Transfer: 10 Tips
- What Is a DC Contactor? Definition and Working Principle Explained
- Is an iPhone Worth Buying in 2024: A Comprehensive Guide
- Digital Advocacy: Myth or Future
- Best Tips for Manual Mobile App Testing to Quality App Development
- Web Developer Jobs: How to Find and Key Competencies in 2024