Since the digital computer was developed in the 1940s, it has been acknowledged that computers can be crafted to perform even the most complex task, such as playing chess or performing mathematical terms with high proficiency. Even though continuous advancements took place in computers' processing speed and memory capacity, no programs could match the flexibility of humans with extensive knowledge in performing day-to-day tasks. 

With the advancement of artificial intelligence, computers can now match the performance level of humans and professionals in functioning specific complex tasks. Hence, artificial intelligence, handwriting recognition, computer search engines, medical diagnoses, and much more can be performed without human investment.

What is Artificial Intelligence (AI)?

Artificial intelligence combines data sets and computer science to function in problem-solving. It also includes subfields of deep learning and machine learning, often mentioned in alignment with artificial intelligence. These disciplines consist of AI algorithms to create expert systems, making classifications or predictions based on the input data.

Over the years, artificial intelligence has gone through multiple revolutions and marked a turning point with the release of Chat GPT from Open AI. These generative AI models even learn molecules, natural images, grammar of software code, and multiple other types of data for performing the most complex tasks.

Is AI Hard to Learn?

Is AI learning hard? Artificial intelligence is quite challenging to understand as it is one of the significant technology fields today. Around 90% of automation technologists monitored the evidence that they are still determining several problems presented by intelligent machine development, and the major problem is the need for Expertise in engineers. Some of the significant complexities of AI faced by around 56% of businesses presently include:

  • Expertise in coding is vital to train computers to automate decision-making.
  • Currently, the methods and AI tools implemented are specifically for conventional software, so for a beginner, learning new tools and techniques requires ample time and effort.
  • Calculus, statistics, and computer science are the other most complicated areas of artificial intelligence.

Essential Skills for Easy AI Understanding

Some of the significant skills to understand and learn AI efficiently are as follows:

Programming Proficiency

Artificial intelligence functions on data learning and algorithms. To implement and develop these models on computers, you must learn the major programming languages, such as Python or R, and coding skills assist you in manipulating and analyzing data to make informed decisions to meet clients' expectations. Learning coding and programming languages is a challenging task for everyone. Each language has specific structures and syntax, so it is hard for a beginner to understand them. However, you might make an effort if you're passionate about them. As a fresher, you must always begin with the basics and then move on to the advanced languages for efficient learning.

Data Science

Data is the key element of artificial intelligence, and artificial intelligence engineers must have an in-depth understanding of engineering and data science. They must learn to clean, acquire, and transform data into an essential format. Knowing SQL databases is crucial to managing and querying extensive data set tools, including Spark, AWS S3, and Apache, the most commonly used tools for data processing in AI projects.

Deep Learning

Deep learning is a significant process of artificial intelligence that trains computers to analyze data in a process inspired by the brain of a human being. Deep learning models are crafted to recognize texts, sounds, complex patterns in pictures, and other data for producing accurate predictions and insights.

Data Structures

A data structure is a specialized format to process, retrieve, organize, and store data. Multiple advanced and basic kinds of data structures are designed for arranging data for specific purposes. Data structure makes it easy to work and access the data appropriately.

Data Manipulation

Analyzing, handling, and gathering data are the significant functions of AI. All artificial intelligence models require well-processed and accurate data for work, and this requires a professional with good experience. Artificial intelligence experts can deal with outliers and missing values, which is a complex task for beginners. AI algorithms also depend on mathematics and statistics. Hence, people with minimal or no knowledge of algebra, probability, and calculus might need help understanding artificial intelligence.


Central concepts in linear algebra and vectors are implemented to represent and transform data. Artificial intelligence chat boards implement linear algebra in multiple tasks, including word embedding that converts words into numerical factors for understanding and analysis. Whether you are willing to pursue a machine learning engineer, a robotic scientist, or a data scientist, you must excel in proficient mathematics as it enhances analytical thinking skills, which are essential in artificial intelligence.


With the evolution of artificial intelligence, statistics remains a critical element in understanding and improving artificial intelligence models. With statistical models, AI algorithms can understand data and adapt new information for making informed decisions.

Understanding New Trends

With the constant updates and innovations in the field of artificial intelligence, we acknowledge a new framework or technique emerging every day, making it a challenging task to align with the pace. However, an artificial intelligence expert must stay updated with the new methodologies. AI includes multiple concepts from several fields, such as data science, programming, mathematics, or computer science. Even though it can be challenging to understand the stories of multiple people, an AI expert needs to have an in-depth knowledge of these subjects.


Even though artificial intelligence masters multiple complex tasks with specific rules, creativity promotes better problem-solving. Generating new ideas that cannot be conceived by artificial intelligence in the absence of human input is essential. Combining the creativity of humans with artificial intelligence, computers offer powerful outcomes across multiple fields.

Challenges to Learn AI

As a beginner in learning artificial intelligence, you might face multiple challenges. However, some of the most significant challenges might include:

Extensive Programming

Intensive programming is a critical element of artificial intelligence. Hence, it would help if you learned to code for programming computers to automate decision-making for yourself.

Data Proficiency

Machines require a wide range of data to master complex tasks. Hence, it can be difficult to acquire, especially for beginners.


Artificial intelligence is highly complex and requires studying multiple subjects such as calculus, statistics, and computer science.

How To Get Better at AI?

To achieve proficiency in artificial intelligence, you can follow some of the key points and strategies mentioned below:

  • Practice coding: as artificial intelligence revolves around coding, you must practice coding regularly in several programming languages implemented to develop models and multiple algorithms.
  • Understand the basics: you must begin by studying the fundamentals of artificial intelligence, which includes neural networks, computer vision, machine learning, natural language processing, and deep learning.
  • Project building: you apply multiple skills and gain experience when you build projects. You can begin with small projects and gradually move to complex ones.
Looking forward to a successful career in AI and Machine learning. Enrol in our Professional Certificate Program in AI and ML in collaboration with Purdue University now.


Several engineers are thinking, " Is learning AI hard? " Yes, Artificial intelligence can be a hard field to study and learn, but with the right resources, guidance, and practice, you can make a recognized position for yourself in this field. Begin by crafting your foundation and keep polishing your skills with multiple online courses in the ever-changing field of artificial intelligence on the Simplilearn online learning platform. Once you keep up with the field, it will give you a competitive position in the market.

Embark on a transformative journey into the world of artificial intelligence and machine learning with Simplilearn's Post Graduate Program in AI and Machine Learning. This comprehensive certification course, designed in collaboration with Purdue University and in partnership with IBM, offers an in-depth exploration of key AI and ML concepts, practical applications, and the latest tools and technologies in the field. Whether you're looking to advance your career, pivot to a new field, or deepen your understanding of these cutting-edge technologies, this program provides the knowledge, skills, and credentials needed to succeed. With hands-on projects, interactive learning experiences, and guidance from industry experts, you'll emerge from this program ready to tackle the challenges and opportunities of AI and machine learning in a variety of professional settings. Unlock your potential and shape the future by visiting Simplilearn's PGP in AI and Machine Learning.

FAQs about AI

1. What Prerequisites Do I Need to Start Learning AI?

The prerequisites include proficiency in programming languages such as Java and Python. Knowledge of probability, statistics, calculus, and linear algebra is also essential.

2. How Long Does It Take To Learn AI?

As a fresher, it might take around 6 to 12 months to study and understand artificial intelligence.

3. Is AI a Stable Career?

Without a doubt, artificial intelligence stands at an outstanding career path with multiple scopes. As an AI expert, you can create a career as a robotics scientist, data scientist, machine learning engineer, research assistant, product manager, and more. In addition, you can also expect good pay in this field.

4. How Long Does It Take to Learn AI?

For a beginner, it might take 6 to 12 months to understand Artificial Intelligence.

Our AI & Machine Learning Courses Duration And Fees

AI & Machine Learning Courses typically range from a few weeks to several months, with fees varying based on program and institution.

Program NameDurationFees
Applied Generative AI Specialization

Cohort Starts: 23 Apr, 2024

4 Months$ 4,000
Generative AI for Business Transformation

Cohort Starts: 24 Apr, 2024

4 Months$ 3,350
AI & Machine Learning Bootcamp

Cohort Starts: 6 May, 2024

6 Months$ 10,000
Post Graduate Program in AI and Machine Learning

Cohort Starts: 9 May, 2024

11 Months$ 4,800
AI and Machine Learning Bootcamp - UT Dallas6 Months$ 8,000
Artificial Intelligence Engineer11 Months$ 1,449