Here's everything you need to know about learning AI in 2025: tips to get started, helpful resources, and insights from industry experts.
We live at a potentially pivotal moment in human history, where the importance of artificial intelligence (AI) is becoming increasingly undeniable. Just look at this statistic: According to a Forbes Advisor survey , 97% of entrepreneurs expect ChatGPT to bring positive change to at least one area of their business. Furthermore, tools like ChatGPT, Midjourney, and Bard are bringing AI into the mainstream, making the art and science of AI more relevant than ever.
If you're a budding data scientist, machine learning engineer, AI researcher, or simply an AI enthusiast, this guide is for you. In this article, we'll show you how to learn AI from the ground up and share tips from industry experts that can help you along the way. We'll not only cover the skills and tools you need to master, but also how businesses can leverage AI in today's landscape.
What is Artificial Intelligence (AI)?
Artificial intelligence (AI) is a branch of computer science concerned with developing systems that can perform tasks that would normally require human intelligence. These tasks range from understanding natural language to recognizing patterns and making decisions to learning from experience. AI is a broad field with numerous subfields, each with its own goals and specialisations.
What are the different types of artificial intelligence?
As AI becomes increasingly popular, the technology is being discussed in a variety of ways. To simplify the rest of this article, it's important to consider the different types of AI. AI can be divided into three levels based on its capabilities:
1 . Artificial Narrow Intelligence (ANI): This is the most common form of AI we interact with today. ANI is designed to perform a single task, such as speech recognition or recommendations for streaming services.
2 . Artificial General Intelligence (AGI): An AI with AGI has the ability to understand, learn, adapt, and implement knowledge at a human-level across a wide range of tasks. Large-scale language models and tools like ChatGPT have shown that they can generalize across many tasks, but this remains a theoretical concept until 2023.
3 . Artificial Superintelligence (ASI): The final stage of AI, ASI refers to a future scenario in which AI surpasses human intelligence in almost all economically valuable tasks. While this concept is intriguing, it remains largely speculative.
The difference between data science, artificial intelligence, machine learning and deep learning
If you haven't yet explored the topic, you may notice that the terms "machine learning," "deep learning," "data science," and others have crept into AI discourse. AI is a broad field with several subsets, including machine learning (ML) and deep learning (DL).
While there's no official definition for any of these terms, and experts disagree on their precise boundaries, there's a growing consensus on the scope of each. Here's a breakdown of how these terms can be defined:
1 . Artificial intelligence refers to computer systems that can behave, think, and learn intelligently like humans.
2 . Machine learning is a branch of artificial intelligence that focuses on developing algorithms that can learn without being explicitly programmed.
3 . Deep learning is a subfield of machine learning. It's responsible for many of the impressive AI stories in the news (e.g., self-driving cars, ChatGPT). Deep learning algorithms are inspired by the structure of the brain and work particularly well with unstructured data such as images, videos, or text.
Why should you learn artificial intelligence now?
Artificial intelligence is more than just a buzzword. It's a revolutionary technology that's changing the way we work, live, and interact with each other. With the explosion of data and the need to harness it meaningfully, the demand for AI skills is skyrocketing. There's no better time than now to start learning AI. Here's why:
AI is a rapidly growing field
Artificial intelligence isn't the future, it's the present. The number of AI jobs has increased significantly in recent years. According to the World Economic Forum's "Future of Jobs" report , AI and machine learning specialists top the list of the fastest-growing occupations over the next five years. As industries continue to adopt AI technologies to streamline their operations and make better decisions, demand for AI specialists is likely to increase.
This prediction is further underscored by the projected growth of the AI market between 2021 and 2030, as shown in the chart below.
AI is a highly paid job
The rising demand for AI skills naturally comes with attractive compensation. According to Glassdoor, the average salary for an AI engineer in the US as of November 2023 is $153,719 per year, with the possibility of bonuses and profit sharing. Machine learning engineers and data scientists are similarly well paid, with average salaries of $151,158 and $178,515 per year, respectively. This financial balance reflects the value and importance of AI skills in the market.
AI is an intellectual challenge
Artificial intelligence isn't just about well-paying jobs and high market demand. It's also an intellectually stimulating field that will challenge you in exciting ways. It's about developing algorithms to solve complex problems, designing models that simulate human intelligence, and creatively applying these technologies to various real-world scenarios.
AI professionals are constantly learning, adapting, and innovating. The industry is constantly evolving, meaning there's always something new to learn, a problem to solve, or a system to improve. This dynamism makes AI an exciting field for those who enjoy challenges and constant learning.
How long does it take to learn AI?
How long it takes you to learn AI often depends on whether you are self-taught or taking formal training, such as at a university.
With a self-taught path, the duration can vary greatly, as it largely depends on your prior knowledge, commitment, and available learning resources. It can take several months to a year or more to gain a solid understanding of AI concepts, programming languages like Python, mathematics, and various machine learning algorithms through self-study. Self-paced online courses, tutorials, and practical projects can accelerate the learning process.
The university route, on the other hand, typically involves formal training in computer science, data science, or related fields. A bachelor's degree in these disciplines typically takes three to four years to complete, during which students receive comprehensive training in AI and related topics.
Whatever path you choose, continuous learning, practical application, and staying on top of the latest developments are essential for a career in AI.
How to learn AI from scratch in 2024
Learning AI can be an exciting endeavor, but it's not without its challenges. It's a broad field with many subtopics. But with a clear roadmap, the right resources, and a strategic approach, you can navigate this landscape. Here's how to learn AI in 2025:
- Master the required skills Before you enter AI, you should acquire a basic knowledge of certain areas. These fundamental skills will form the backbone of your AI understanding and capabilities: 1 . Mathematical fundamentals. AI relies heavily on mathematical concepts, especially its subfields such as machine learning and deep learning. Of course, you don't have to be a mathematician to succeed in AI, but a basic understanding of linear algebra, calculus, and probability theory is essential. For example, concepts such as matrices and linear transformations from linear algebra are frequently used in AI algorithms. 2 . Basic statistics. AI makes a lot of sense if you understand statistics. In this field, it's important to know how to interpret data and derive insights from it. Concepts such as statistical significance, distribution, regression, and probability play an important role in various AI applications. 3 . A willingness to learn. AI is a rapidly evolving field, with new advances, techniques, and tools constantly emerging. Therefore, a proactive mindset and a passion for learning and adapting to new knowledge and technologies are crucial for anyone seeking to gain a foothold and advance in the field of AI. It's important to note that the depth of understanding and mastery of these prerequisites can vary depending on the AI role you're pursuing. For example, a data scientist might not necessarily need a deep understanding of all the mathematical concepts used in AI, but a researcher seeking to develop new AI algorithms might need a deeper understanding of mathematics. The key is to align your learning path with your career goals and adjust the depth of your learning in different areas accordingly.
- Develop specialized AI skills Now that we've covered the prerequisites, let's dive into the essential skills you need to develop to master AI. Similar to the prerequisites above, the level you need for these skills largely depends on the type of role you're aiming for. Statistics is the discipline that deals with the collection, organization, analysis, interpretation, and presentation of data. It forms the backbone for understanding and working with data in AI. Learn more about statistics in the resources below: 1 . Statistics Fundamentals with Python Skill Track , in which you will learn the four fundamentals of statistics with Python, including summary statistics and probability, statistical models such as linear and logistic regression, sampling techniques, how to conduct hypothesis tests, and how to draw conclusions from a variety of data sets. 2 . Course "Introduction to Statistics" , which teaches the basics of statistics, including measures of center and dispersion, probability distributions and hypothesis testing. 3 . Introduction to Statistics in R to learn how to work with variables, graphs mathematics As mentioned earlier, certain areas of mathematics form the foundation for AI algorithms. Linear algebra, calculus, probability theory, and differential equations are all mathematical tools you'll use on your AI journey. Learn more about mathematics in the following resources: 1 . Demystifying Mathematical Concepts for Deep Learning , which explores the fundamental mathematical concepts for data science and deep learning. 2 . Linear Algebra for Data Science in R. In this course, you will learn the fundamentals of linear algebra, such as using matrix-vector equations, performing eigenvalue/eigenvector analysis, and PCA. 3 . The Fundamentals of Probability in Python course covers basic probability concepts such as random variables, mean and variance, and more. programming Implementing AI requires a solid understanding of programming. If you know how to write code, you can develop AI algorithms, manipulate data, and utilize AI tools and libraries. Python is currently the most popular language in the AI community because of its simplicity and flexibility, as well as its many libraries for data science. 1 . The Python Programming Skill Track helps you improve your Python programming skills. You'll learn how to optimize code, write functions and unit tests, and apply software development best practices. 2 . R Programming Skill Track : Here you will improve your R programming skills by learning how to work with common data structures, optimize code, and write your own functions. Data structures Data structures allow you to store, retrieve, and manipulate data efficiently. Therefore, knowledge of data structures such as arrays, trees, lists, and queues is essential for writing efficient code and developing complex AI algorithms. Data manipulation Data manipulation involves cleaning, transforming, and manipulating data to prepare it for further analysis or to feed it into AI models. Knowledge of data manipulation libraries like Pandas is essential for working in AI. Data science Data science is a blend of various tools, algorithms, and machine learning principles aimed at discovering hidden patterns in raw data. As an AI professional, it's important to understand the process of extracting insights from data. Machine learning Machine learning is a subfield of AI in which machines learn from data to improve their performance or make accurate predictions. It's important to understand the different machine learning algorithms, how they work, and when to use them. Deep Learning Deep learning, a subset of machine learning, uses multi-layered neural networks (hence "deep") to model and understand complex patterns in datasets. It is behind many of the most advanced AI applications, from voice assistants to self-driving cars. Each of these skills is linked to the others and will help you build a broad knowledge of AI concepts. A good way to start is to learn the fundamentals in each area before exploring in detail the areas that interest you most. You can tailor your approach as you see fit, working on the areas that emerge as you progress through your studies and practical experience.
- Learn the most important AI tools and packages Knowing the right tools and packages is crucial to your success in AI. Python and R, in particular, have emerged as leading languages in the AI community due to their simplicity, flexibility, and the availability of robust libraries and frameworks. While you don't need to learn both to succeed in AI, below are some important libraries and frameworks you should familiarize yourself with, depending on which tool you choose: The best AI tools and packages in Python Python is an interpreted high-level programming language known for its readability and versatility. It is widely used in AI thanks to its user-friendly syntax and the wide range of libraries and frameworks available for AI and data science. Pandas Pandas is a Python library that offers extensive capabilities for data analysis. Data scientists use Pandas for various tasks, including data cleaning, data transformation, and statistical analysis. It works well with incomplete, messy, and unlabeled data, making it an important tool for preprocessing datasets. NumPy NumPy, short for Numerical Python, is a Python library that supports large, multidimensional arrays and matrices, as well as a rich collection of mathematical functions for manipulating these arrays. It is an essential library for all scientific computing, including AI. Scikit-Learn Scikit-Learn is a simple and efficient tool for data mining and machine learning. It is based on NumPy, SciPy, and matplotlib and is open source, meaning it is freely available to anyone. It offers various algorithms for classification, regression, clustering, and dimensionality reduction. PyCaret PyCaret is a powerful Python library that simplifies the process of building and deploying artificial intelligence models, allowing users to efficiently explore, preprocess, train, tune, and compare multiple machine learning algorithms with just a few lines of code. PyTorch PyTorch is an open-source machine learning library based on the Torch library. It is used for applications such as natural language processing and artificial neural networks. Its greatest advantage is its flexibility and speed, which makes it suitable for deep learning research. Keras Keras is a user-friendly neural network library written in Python. Designed to minimize the time between your ideas and working models, it offers a straightforward path to neural network modeling. Keras is also modular, making it incredibly versatile when creating new models. Commercially available APIs If you're ready to explore AI, using APIs to access commercially available models is one of the best ways to get started. Commercially available APIs like the OpenAI API, Cohere API, and Anthropic API are good starting points. Hugging face As you progress, you can explore pre-trained models using standard Python packages such as Hugging Face's Transformers and Accelerate, which facilitate the use of GPUs and TPUs. An example of an AI learning plan Below, we've created a possible learning plan that shows where you should focus your time and effort if you're just starting out with AI. Keep in mind that the timeframe, subject areas, and progress depend on many variables. We want to make this plan as practical as possible, so we've recommended projects for you to work on as you progress. Top 5 Tips for Learning AI Embarking on the journey to learn AI can be daunting, but a strategic approach can make the process more structured and manageable. Here are five steps to guide you through your AI learning journey:
- Choose your focus First, decide where you want to focus your efforts based on your career goals. In this article, we'll take a closer look at the different careers in AI; each role has a different focus and requires unique skills. For example, if you're interested in a more application-oriented career as a data scientist or machine learning engineer, you should focus more on programming, data science, and understanding various machine learning algorithms. Your goal is to master the application of AI techniques to solve real-world problems. If you're more interested in a research career, you should delve into the theory behind AI and machine learning. You'll need a solid understanding of mathematics, statistics, and theoretical computer science. This is not a rigid distinction, but rather a starting point that will help you decide what you want to focus on first.
- Start learning Once you've decided on a focus, it's time to start learning. The learning resources suggested in the skills section and the AI learning plan above are useful starting points. Remember: mastering AI is a marathon, not a sprint. Take your time to thoroughly understand each concept before moving on to the next.
- Apply your skills in projects There's no better way to learn than by doing. Applying the skills you learn in real-world projects solidifies your understanding and gives you practical experience that can enrich your portfolio. This can be as simple as building a machine learning model to predict house prices or as complex as developing a deep learning model for image recognition. We've listed example projects in this article.
- Join a community Join AI communities, both online and offline. Participating in forums like Stack Overflow or GitHub, joining AI groups on LinkedIn, or attending AI meetups and conferences can provide invaluable learning opportunities. By joining a community, you can learn about the latest trends, get help when you're stuck, and connect with other AI enthusiasts.
- Continue iterating AI is a rapidly evolving field. Once you've mastered the basics, it's important to continue learning and improving your skills. Follow AI blogs, read research papers, take advanced courses, and always be on the lookout for new ways to challenge yourself. This iterative process will transform you from a beginner to an expert. Remember that the journey to learning AI is challenging but immensely rewarding. Don't be discouraged if you encounter obstacles along the way; they are part of the learning process. Keep your end goal in mind and stay committed along the way. The different careers in AI today Artificial intelligence has opened up numerous career paths, each with unique tasks, tools, and required skills. Let's look at three AI careers that are currently very popular: data scientists, machine learning engineers, and researchers. We'll mention some of the tools they need to master. If you're unfamiliar with these tools, that's perfectly fine - we'll cover them in more detail in a later section. Data scientist Data scientists explore, extract, and report meaningful insights from a company's data. They communicate these insights to non-technical stakeholders and have a strong understanding of machine learning workflows and how to connect them to business applications. They work almost exclusively with coding tools, conduct analyses, and often work with big data tools. Data scientists are the detectives of the data world. They are responsible for unearthing and interpreting rich data sources, managing large volumes of data, and synthesizing data points to identify trends. They apply their analytical, statistical, and programming skills to collect, analyze, and interpret large amounts of data. They then use this information to develop data-driven solutions to difficult business problems. Part of these solutions involves developing machine learning algorithms that generate new insights (e.g., identifying customer segments), automate business processes (e.g., predicting creditworthiness), or provide new value to customers (e.g., recommendation systems). Key qualifications: Good knowledge of Python, R and SQL Understanding of machine learning and AI concepts Knowledge of statistical analysis, quantitative analytics and forecast modeling Data visualization and reporting techniques Effective communication and presentation skills
Indispensable tools:
Data analysis tools (e.g. Pandas, NumPy)
Machine learning libraries (e.g. Scikit-learn)
Data visualization tools (e.g. Matplotlib, Tableau)
Big data frameworks (e.g. Airflow, Spark)
Command line tools (e.g. Git, Bash)
Machine Learning Engineer
Machine learning engineers are the architects of the AI world. They design and deploy machine learning systems that make predictions from companies' data. They also solve problems such as predicting customer churn and lifetime value, and
are responsible for delivering models to the organization. Machine learning engineers typically work exclusively with coding-based tools.
Key qualifications:
Solid knowledge of Python, Java and Scala
Familiarity with machine learning frameworks (such as Scikit-learn, Keras, or PyTorch)
Understanding of data structures, data modeling and software architecture
Advanced mathematical knowledge (linear algebra, calculus, statistics)
Teamwork and exceptional problem-solving skills
Tools:
Libraries and algorithms for machine learning (e.g. Scikit-learn, TensorFlow)
Data science libraries (e.g. Pandas, NumPy)
Cloud platforms (e.g. AWS, Google Cloud Platform)
Version control systems (e.g. Git)
Researchers
Researchers are the thinkers of the AI world. They conduct cutting-edge research to advance the state of the art in AI. Their work often involves inventing new algorithms or improving existing ones. They also present their findings at AI conferences and in scientific articles.
Key qualifications:
Solid understanding of machine learning and deep learning
Proficiency in Python and other programming languages
Comprehensive knowledge of the mathematical theory of AI (e.g. statistical learning theory)
Ability to design and validate new AI models
Strong writing and public speaking skills
Tools:
Deep learning frameworks (like TensorFlow, PyTorch)
Tools for scientific calculations (such as MatLab, Mathematica)
Writing and presentation software (such as LaTeX, Google Slides)
Cloud computing resources (such as AWS, Google Cloud Platform)
How to find a job in AI
A college degree can be a huge advantage for entering an AI career, but it's not the only path. While we believe formal training in AI or a related field is worthwhile, more and more professionals are entering the field through non-traditional routes, proving that with dedication, consistent learning, and a proactive approach, you can land your dream AI job.
Here's how to find a job in AI without a degree:
Learn more about the field
Stay up to date on the latest developments in AI. Follow influential AI experts on Twitter, read AI research papers, and listen to AI-related podcasts
AI thought leaders you should follow include Yoshua Bengio, Fei-Fei Li, and others. You'll gain insights into trending topics, emerging technologies, and the future direction of AI.
Develop an effective resume
In today's digital age, your resume will not only be read by humans, but it will also have to pass through applicant tracking systems (ATS). These are automated software used by many companies to filter out resumes that don't meet certain criteria. Therefore, it's important to design your resume to be ATS-friendly while still catching the attention of recruiters.
How to master AI tools for businesses
This article primarily focused on providing a deep understanding of AI for aspiring AI practitioners, but it's also important for business professionals to become familiar with AI tools. Generative AI tools like ChatGPT are increasingly becoming an integral part of various business processes.
As Noelle Silver, Global AI Solutions & Generative AI & LLM Industry Lead at Accenture
Learn more about generative AI
First, familiarize yourself with the various AI tools available to you. There's a wide range of tools designed for different business functions, such as marketing, sales, customer service, and data analytics. Explore the AI tool landscape and understand the capabilities of each tool.
Applying Generative AI Tools at Work
Once you understand AI tools, the next step is to apply them to your daily workflows. Whether you want to automate data analysis, improve customer interactions, or streamline business processes, AI tools can benefit you, your teams, and the entire company.
We've compiled some resources that provide practical guidance for effectively using AI tools in your work:
Apply human supervision
Although AI tools are incredibly useful, they are not infallible. It's important to review and edit the results they produce. Recognize their limitations and adapt their use accordingly. Remember that these tools are meant to support you, not replace your decisions. Understanding the ethics of AI is important.
Mastering AI tools in business isn't just about understanding the technology, but also knowing how to use it effectively to succeed in your role and your organization. By learning, applying, and developing them, you can stay ahead in the AI-driven business world.
Conclusion
Learning AI is a rewarding endeavor that opens the door to a world of innovative technologies and exciting career opportunities. The knowledge and experience gained through this process go beyond textbooks and lectures. It's a dynamic cycle of learning, applying, experimenting, and improving. A hands-on approach, especially through courses and AI projects, accelerates learning and fosters important skills like problem-solving, critical thinking, and creativity.
If you're just starting out with AI learning, we've compiled a wealth of helpful resources to help you get started, as well as a sample learning plan for some of the key topics you'll need to master on your journey to artificial intelligence.
For more details about AI and ML, go through our company website, squardtech
Top comments (0)