We are living in a digital era where the term “Artificial Intelligence (AI)” is both interesting and inevitable. Uncovering the secret of artificial intelligence (AI) has the potential to drastically transform society, just as discovering fire or electricity changed our lives.

Thus, it is now essential to learn about AI because it is now influencing every industry. AI-based technologies are getting used everywhere.

If you dream to become an AI engineer, then this blog explains AI from the beginning to the end, offering the steps you need to take to successfully navigate this exciting yet challenging field.

What is Artificial Intelligence?

Artificial intelligence focuses on the cognitive issues that are typically linked to human intelligence. This indicates that it performs operations like pattern recognition, learning, and solving problems with a machine efficiency that occasionally surpasses human capability.

Almost every aspect of our lives is now impacted by AI. For example, it is the brain behind Google Maps’ route recommendations or the voice of our virtual assistants, such as Siri and Alexa.

Types of Artificial Intelligence

For aspiring AI professionals, it is important to know that AI is categorized into three types:

  • Artificial Narrow Intelligence (ANI):This is the most prevalent type of AI we deal with today. ANI is designed to do a single task, such as voice recognition.
  • Artificial General Intelligence (AGI):A human-level AI with artificial general intelligence (AGI) can understand, gain insight, adapt, and apply knowledge to a variety of tasks. Even though extensive language models and tools like ChatGPT have shown the ability to generalize several tasks.
  • Artificial Super Intelligence (ASI):ASI refers to a future scenario where AI goes beyond human intelligence in almost every economically significant task. This concept, while intriguing, is still highly speculative.

Although we have come a long way with narrow AI, general AI is still mostly unexplored.

Why Should You Learn AI?

Let’s first learn why understanding AI is so important to building a successful AI career before delving into its specifics:

  1. Global Impact:

AI has the ability to completely change the way we live, and industries like education, healthcare, and finance are already implementing AI techniques. It is a major factor in the shift towards a highly digitized future.

  1. Job Opportunities

There are many different job roles available for AI specialists, such as machine learning engineers, data scientists, and AI architects. The demand for these professionals is rising quickly.

  1. Competitive Benefits:

Gaining proficiency in AI not only provides you with a competitive advantage in your current role but also offers a wide range of possibilities.

  1. Emerging Field

With an average yearly compensation of $120,273, AI specialists are among the top-paid individuals in the tech sector. Depending on experience and expertise, compensation may vary even further in different AI jobs.

How to Learn AI from Scratch in 2024?

Learning AI can be a fascinating endeavor, but it’s not without its challenges. It’s a vast field with several subtopics. However, with a clear roadmap, the right resources, and a strategic approach, you can navigate this landscape successfully. Here’s how to learn AI in 2024:

  1. Master the AI skills

Before entering the AI world, you’ll have to gain knowledge in certain areas. The following fundamental abilities will serve as the cornerstone of your AI knowledge and skills:

  • Programming languages like Python and Java
  • Extensive knowledge of machine learning
  • Understanding of Database Modeling, Data Warehousing, and Data Processing
  • Knowledge of Intelligent User Interfaces (IUI)
  • Basic mathematics and statistics
  1. Learn AI tools and packages

The key to success in AI is having the appropriate tools and packages at your disposal. Python and R, in particular, have become the most popular languages in the AI field because of their ease of use, adaptability, and availability of robust libraries and frameworks.

Although mastering both is not necessary for success in AI, the following essential libraries and frameworks should be familiarized with, based on the tool you prefer:

  • pandas
  • NumPy
  • Scikit-Learn
  • PyCaret
  • PyTorch
  • Keras
  • Hugging Face


  1. Earn AI Certifications

The simplest and most effective way to gain AI skills is through a good AI course that covers a lot of concepts related to this field. Such courses provide classes on machine learning, AI basics, and deep learning that are specifically designed for novices.

There are several online educational bodies that are providing globally recognized AI certifications, such as Stanford University, Google, Harvard University, and USAII®, to name a few.

Whether you are a beginner in this field or you’re planning to switch to this domain from your existing one, USAII® has plenty of AI certification courses available for you.

Their Certified Artificial Intelligence Engineer (CAIE™) certification program is an excellent career-starter certification in Artificial Intelligence Engineering. You can enroll in this one of the best AI certification programs with confidence and study at your own pace.

If you are already in this field and want to achieve a higher position, then the Certified AI Transformation Leader (CAITL™) is the most preferred one. Being an excellence program, this course is aimed at business leaders, senior executives, and C-suite professionals to encourage AI transformation for business growth.

  1. Apply your Skills to Projects

Engage in hands-on projects to put your knowledge to use and get real-world experience. Start with simple projects and gradually increase complexity as you become more proficient.


Since the field of AI is rapidly expanding, make a commitment to continuous learning and regular practice. Continue to be curious, explore latest concepts, and consistently push yourself with coding tasks and projects.


In 2024, you may efficiently learn AI from beginning and lay a strong foundation for additional research and specialisation in the field by combining these learning strategies and tools.