If you've been looking into the world of artificial intelligence (AI), you must have seen or heard these two terms - machine learning and deep learning. They are quite related to each other so people tend to use them interchangeably.
But machine learning is a subset of artificial intelligence and deep learning is a subset of machine learning. This means that each term has its unique definition in the field of AI. In this article, we'll learn the difference between them.
What is artificial intelligence and the latest trends taking place in AI?
Artificial intelligence (AI) is all about developing machines that can think and perform tasks like humans. Computers were typically created to listen to instructions before they execute tasks. But AI seeks to build smarter systems that can learn and improve from data and experience.
Artificial intelligence is quickly becoming a part of our everyday lives. From healthcare to tech to advertising, AI has the potential to improve every industry out there. According to a survey, 50% of respondents say their companies have adopted AI in at least one business function.
There are some artificial intelligence trends that are likely to continue in 2023 and beyond. First, AI is helping to tackle cybercrime and enhance cybersecurity strategies. Next, AI-based tools are using Natural Language Processing (NLP) to generate images and texts automatically.
Another noticeable trend is the awareness of the ethics of AI. These are a set of principles that govern data collection and manipulation as well as machine learning techniques. Other AI trends may include the increasing use of AI technologies in healthcare and businesses.
What are the differences between machine learning and deep learning?
Machine learning is a subset of AI that allows systems to learn and quickly adapt to changes without being programmed to that level. It uses datasets to train models or algorithms so they can make predictions or perform certain tasks.
Deep learning is a subset of machine learning that consists of many more layers of algorithms known as an artificial neural network. Just like the human brain, deep learning uses neural networks to process data and solve problems.
A machine learning algorithm can train on a CPU and mostly needs small datasets. It often takes a shorter time to train and provides lower accuracy. An ML algorithm makes simple, linear correlations and requires more human intervention to correct and learn.
A deep learning algorithm requires large amounts of data and needs a specialized GPU to train. It takes longer training and offers higher accuracy. It's also capable of making non-linear, complex correlations and learns on its own from environment and past mistakes.
A few practical examples of how machine learning and deep learning are applied in real life.
Machine learning and deep learning are under the umbrella of artificial intelligence. There are so many useful applications for them today. For example, machine learning is being used for image and speech recognition, social media, product recommendation, traffic prediction, etc.
Deep learning is relatively new but there are some common applications of it. They include:
Natural Language Processing (NLP), computer vision, customer relationship management systems (CRM), financial fraud detection, autonomous vehicles, virtual assistants, etc.
What is the demand for qualified machine learning specialists?
Machine learning is a rapidly growing field that has the potential to transform every industry. These models and systems are more complex than ever because of real-time applications. More machine learning tools are also available so companies can automate repetitive tasks.
The demand for machine learning engineers is growing as more companies get exposed to the capabilities of these technologies. According to a report by the World Economic Forum, AI and machine learning will automate 85 million jobs and introduce 97 million new jobs by 2025.
However, there's a limited supply of machine learning professionals. According to a Statista survey, 82% of organizations have a demand for machine learning skills but only 12% seem to have an adequate supply of these skills.
What does a machine learning specialist do?
A machine learning specialist is someone who develops AI and machine learning algorithms. Their responsibilities may include designing and developing machine learning systems, implementing machine learning algorithms, running AI systems experiments and tests, etc.
Machine learning specialists are often part of a data science team who develops the models for AI systems. They may need to combine software development and modeling skills to determine which model to use and the datasets that would be best for training each model.
What type of qualifications do you need to become a machine learning specialist?
Most machine learning jobs require applicants to have a bachelor's or masters in computer science or data science. You also need a good practical portfolio or some working experience as a data scientist or ML specialist.
Some crucial skills for machine learning include knowledge of math, statistics, and probability. You also need to understand coding languages such as Python, R, and SQL. Then, you need to master the software and tools required.
With a bachelor's degree in computer science or a related field, you can enroll in an online machine learning certificate program. It can help you build a graduate-level foundation in machine learning and predictive modeling.
For flexible class schedules, you should earn your machine learning certificate online. It's a project-driven certificate program that can be completed in about 5 courses. This is much faster than a traditional master's degree.
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