AI and Machine Learning courses

Artificial intelligence (AI) is the process of programming a computer to make decisions for itself. This could be anything from recognising objects in pictures, to chatbots responding to customer queries based on frequently asked questions, to recommending shows on streaming services.

Machine learning (ML) is a subset of AI that teaches computers how to learn from data. This is achieved by providing the computer with many examples from which it can identify patterns and trends. These insights will then inform the computer how to behave in the future.

As the use of AI and ML expands, training and upskilling become essential. Resume Builder reports that 96% of companies hiring say candidates with AI skills will be at an advantage. According to Edvancer, 82% of organisations now require machine learning skills.

As more companies continue to adopt AI technology and tools, developing employees’ AI literacy should be a priority for leaders. It's necessary to stay competitive, improve efficiency and drive innovation. According to members of the Forbes Business Council, business leaders should work to level up their teams’ ability to read, understand, use and communicate with data and AI. Some steps they can take include determining where AI will be most effective, examining current IT policies, prioritising ethics oversight, and developing AI training programs.

To successfully integrate AI into their business, organisations should first look to provide their staff with the necessary training to understand how it could benefit their workflows. Knowledge of the capabilities and limitations of AI will help your staff design and implement AI-assisted workflows that teams are proficient in and comfortable with using on a daily basis.

The coverage of artificial intelligence courses, including the fundamentals and tools used, varies in terms of level (e.g. beginner, intermediate, advanced).

For intermediate and advanced AI and Machine Learning courses in Australia, New Zealand and the Philippines, training usually covers strategic thinking and communication and how to leverage advanced features of the tools.

AI and Machine Learning courses cover a mix of theoretical foundations and practical applications. Students will be introduced to key mathematical concepts and essential frameworks for working with AI on a daily basis. They will explore ML algorithms; deep learning techniques including CNNs, RNNs and transformers; as well as applications of natural language processing (NLP) and computer vision. Additionally, students will gain hands-on experience with commonly used tools, including the Python and R programming languages, and the TensorFlow, PyTorch and Scikit-learn software libraries.

Artificial Intelligence and Machine Learning training courses can be challenging. This is also because AI and ML are very fast-paced. New problems and solutions get developed and become mainstream in a few months.

You can attend an AI or ML training course in Australia, New Zealand and the Philippines without deep coding knowledge. Despite the common opinion that AI is for all, the basic or intermediate ability to write code is necessary to control AI and ensure that its activities align with your end goal. Access to it in the form of Netflix recommendations based on previous activity is one thing, but a working knowledge of the algorithms used behind the scenes is another.

Those in the field say it takes six months to gain an understanding and confidence in tackling the basic concepts of AI and ML. This requires participation in AI and ML courses and undertaking simple and practical projects. After that, it's a matter of taking advanced courses and solving more complex problems to learn intermediate concepts and deepen your knowledge.

Intermediate learners can spend further time applying algorithms to real-world data, understanding evaluation models and working with other issues of greater complexity. These skills can take months or years to build, culminating in advanced applications of AI and ML training, which often involves postgraduate study or industry experience.

It is important to note that both AI and ML require continuous upskilling to keep pace with the development of new models and techniques. Courses, projects, and independent research will help ensure a comprehensive knowledge of AI and ML for those working in the industry.

Are data analytics courses difficult?

This a question we often get asked along with "Can I become a data analyst in 3 months?", "Do data analytics courses require coding?" and "It is math-heavy?"

Based on the experiences of our trainers and our students, we would say that data analytics courses are not particularly tough. This is primarily because they are not highly academic. They cover a combination of learning about the tools, learning about practical communication and how to apply both to different use cases within organisations. And this is where the courses come in - they provide you with instructor-led training, hands on experience, practical exercises and additional resources.

When it comes to whether it's possible to learn data analytics in three months, we agree that it is possible but that honing your skills by continuously working on projects and interacting with a community of data analytics experts is key. Our data analytics courses in New Zealand can be taken online or in person in our campus in BGC, Metro Manila, Philippines. During the course, you can expand your network and get insights from other students.

On the question about whether data analytics courses require coding, our short answer is "Not really." It is not a barrier if you don't know how to code. Having a basic knowledge of programming languages will help. But in-depth knowledge is not a prerequisite for attending a data analytics course.

The answer is similar to whether data analytics courses are math-heavy. Data analysts are often good with numbers and they have a foundational knowledge of mathematics and statistics. But people have successfully attended data analytics courses and gained certifications without in-depth mathematical knowledge. What is required is a passion for analysis and logic. Much of data analysis and using data analytics tools is about following a series of logical steps to collect, process, present and monitor data.