People seem to think there is some magical ingredient needed to become an expert at AI and machine learning. After all, AI promises to revolutionize entire industries and impact the lives of individuals and businesses around the world. But there is a path you can take to achieve expert status in these growing fields that will give you the important educational foundation and pragmatic best practices to apply AI and machine learning on the job to great promise.
Here are some key skills you’ll want to learn to master in the AI market that is expected to grow to $100 billion by 2025.
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Real-world AI Skills
To put AI skills to good use, it helps to work with models that you have confidence in and can make AI explainable to business teams that will use it. According to a Fortune study, AI technologies enable computers to mimic human intelligence, using logic, if-then rules, decision trees, and other intelligent functions to improve the efficiency of a wide range of business processes. A recent study from Forrester reports that 45 percent of AI decision-makers say trusting AI systems is either challenging or very challenging, and that there will be increasing demand for transparent and easily understandable AI models.
Core AI skills training shows AI engineers on how to create practical applications of AI using a wide range of intelligent agents. According to a recent poll, the three most popular uses for AI and machine learning are to increase efficiencies or worker productivity (51 percent), to inform future business decisions (41 percent), and to streamline processes (39 percent). AI skills encompass many key areas, including designing intelligent agents; machine learning algorithms and advanced artificial neural networks that use predictive analytics to solve real-time decision-making problems; neural networks and multi-layer data abstraction that help you analyze data effectively; and applying major applications of AI across various fields such as customer service, financial services, and healthcare.
Machine learning automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. Machine learning skills training dive deep into the operation of support vector machines, decision trees, and clustering, and how they relate to the practical aspects of machine learning. One of the most promising fields to leverage machine learning in data science. According to Indeed, the terms “AI” and “machine learning” were included in the job descriptions for about 75 percent of data scientist jobs. Data scientists enhance the value of data with machine learning and find patterns that can be directly applied to business operations.
According to a Fortune report, Deep learning has become known as the deepest subset within machine learning, composed of algorithms that permit software to train itself to perform tasks by exposing multi-layered neural networks to vast amounts of data. It’s also a field heavily used by data scientists, and in fact, 80 percent of data scientists are predicted to be using deep learning in their toolkits, according to Gartner. Deep learning skills training is structured around learning TensorFlow, the open-source software library designed to conduct deep neural network research. Deep learning aspirants learn to implement algorithms, build artificial neural networks, and traverse layers of data abstraction to understand the power of data. And from a practical standpoint, which you can see is paramount with today’s upskilling curriculum, learners can master using TensorFlow models and interpret results, troubleshoot, and use these models in a real-world industry project. Take a step further and incorporate Python into your learning path. Data scientists similarly use Python to perform data analytics, machine learning models, data visualization, web scraping, and natural language processing, among many other tasks.
Convergence with Robotic Process Automation (RPA)
RPA empowers companies to configure software bots to capture and interpret applications for processing a transaction, manipulating data, triggering responses, and communicating with other digital systems. The convergence of RPA with AI and machine learning technologies is creating a new era for digital transformation. Forrester reports that more than 40 percent of enterprises plan to create state-of-the-art digital workers by combining AI with RPA. Core RPA skills training provides a complete overview of tools and workflows used in intelligent automation and steps for implementing RPA across the organization.
Is the demand for workers with AI and related technologies high? You bet! Indeed Hiring Lab says demand for workers with AI talent has more than doubled over the past three years, with the number of AI-related job postings as a share of all job postings up about 119 percent. AI skills are becoming increasingly used in IT, marketing, sales, and financial operations. That should be sufficient reason to get your teams on the path to AI mastery as soon as possible.
As the demand for AI and machine learning has increased, organizations require professionals with in-and-out knowledge of these growing technologies and hands-on experience. Keeping the innate need in mind, Simplilearn has launched the AI and ML courses with Purdue University in collaboration with IBM that will help you gain expertise in various industry skills and technologies from Python, NLP, speech recognition, to advanced deep learning. This Post Graduate program will help you stand in the crowd and grow your career in thriving fields like AI, machine learning and deep learning.