Artificial Intelligence (AI) and Machine Learning (ML)
Machine learning (ML) and artificial intelligence (AI) are yet more technological advances that organizations need to implement in order to stay competitive in their industry.
Machine learning extracts meaningful insights from raw data to quickly solve complex, data-rich business problems.
The keys here are machine learning algorithms. ML algorithms learn from the data iteratively and allow computers to find different types of hidden insights without being explicitly programmed to do so.
Many companies are already using forms of AI or machine learning (ML) for everything from automation of manual processes to predicting and fulfilling customer demand.
Most companies no longer resist the use of AI/ML. But many organizations are still befuddled on the best way to utilize these technologies.
Experts believe the first step is for companies to identify and clarify the most profitable use cases in order to determine their future AI investment and development needs.
For example, take the financial sector. One widespread use of AI and ML is in fraud detection. Visa, Mastercard and PayPal (all US) are using machine-learning algorithms to analyze data on customer behavior captured over several decades.
Such analysis can detect oddities in account activities and identify fraudulent activity in mere milliseconds at any point in the transaction cycle. According to recent reports, AI and ML have been very successful in reducing fraud.
Another prominent use of AI is in the healthcare industry. AI is being used in healthcare facilities and pharmaceutical industries. Allocation of resources and drug discovery are two of the many ways AI is being used.
Diagnostics is another area with potential, with AI used to check patients’ symptoms against possible causes or to analyze scans. Early adopters include Chinese health apps such as Ping An’s Good Doctor and Chinese hospitals, particularly in Shanghai, which allegedly wants to become a base for healthcare AI.
Electric grids are also investing in AI.
In the US, the Department of Energy has put AI at the center of its smart grid strategy, while in the UK the National Grid has teamed up with IBM to develop cloud-based analytics. These initiatives allow for real-time monitoring of power grids and the ability to forecast and respond to surges in output or demand.
AI and Machine Learning Training Courses by Tonex
Our Machine Learning Training Courses covers a wide array of topics including:
- The Basics of Machine Learning
- Popular Machine Learning Methods
- Terminology and Principles
- Machine Learning Tools and Algorithms
- Applied Artificial Intelligence and Machine Learning
- Principles of Neural Networks
- Introduction to Deep Learning
Our Machine Learning Training Bootcamp is especially beneficial for busy professionals who want to stay current in their fields but have limited time to be away from the office.
Attendees learn, comprehend and master ideas on machine learning concepts, key principles, techniques including: supervised and unsupervised learning, mathematical and heuristic aspects, modeling to develop algorithms, prediction, linear regression, clustering, classification, and prediction.
Our Machine Learning for Control Training is a unique course that explores the fundamentals of control theory, an area of engineering related to control of continuously operating dynamical systems in engineered processes and machines.
Who Should Attend:
Professionals seeking to enhance their AI and Machine Learning expertise, including data scientists, engineers, analysts, and IT professionals. Ideal for organizations looking to upskill their teams and harness AI’s potential for innovation.
Machine learning in business helps in enhancing business scalability and improving business operations for companies across the globe. Artificial intelligence tools and numerous ML algorithms have gained tremendous popularity in the business analytics community.
Factors such as growing volumes, easy availability of data, cheaper and faster computational processing, and affordable data storage have led to a massive machine learning boom.
If done correctly, machine learning can serve as a solution to a variety of business complexities problems, and predict complex customer behaviors. Major technology giants figured this out some time ago.
Google, Amazon, Microsoft, etc., have in fact come up with their own Cloud Machine Learning platforms.
In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.
Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress toward human-level AI.
Machine learning is an important component of the growing field of data science. Through the use of statistical methods, algorithms are trained to make classifications or predictions, uncovering key insights within data mining projects.
These insights subsequently drive decision making within applications and businesses, ideally impacting key growth metrics. As big data continues to expand and grow, the market demand for data scientists will increase, requiring them to assist in the identification of the most relevant business questions and subsequently the data to answer them.
“Deep” machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesn’t necessarily require a labeled dataset.
It can ingest unstructured data in its raw form (e.g., text, images), and it can automatically determine the set of features which distinguish different categories of data from one another.
Unlike machine learning, it doesn’t require human intervention to process data, allowing us to scale machine learning in more interesting ways.
Remember, Tonex courses can be tailored to your needs.
Contact us for more information, questions, comments.
AI and Machine Learning
