Recent technological developments have spurred both excitement about the world of opportunities and fear of becoming obsolete. While the adoption of artificial intelligence by businesses has led to the automation of many tasks, new roles continue to emerge daily.
Upskilling is the only way to stay current in this AI-driven world, and those who know how to adapt and leverage new technologies will thrive in the future job market.
But how do you learn AI if you’re starting from scratch? Which skills will survive the test of time and remain relevant in today’s dynamic work environment?
This guide will help you navigate the ocean of information and create a comprehensive learning plan. We'll explore the AI basics, its various types, the in-demand skills, and the essential theory you need to master. We’ll also furnish you with a range of valuable resources to help you get started.
Table of Contents
- What Is Artificial Intelligence?
- Types of AI
- Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science
- How to Learn AI Step by Step
- Learn AI-Proof Skills with 365 Data Science
What Is Artificial Intelligence?
Artificial intelligence (AI) refers to the development of computer systems simulating human intelligence that can perform tasks like visual perceptions, speech recognition, decision-making, etc. They learn from input data and produce human-like output. The goal of AI is to solve complex problems and enhance productivity across different domains.
The primary subfields of AI are machine learning, deep learning, natural language processing (NLP), computer vision, and speech recognition. Technologies like ChatGPT—generative pretrained transformers—are part of the large language models (LLMs) family, which belong to NLP.
Types of AI
- Narrow AI (ANI), also known as weak AI, specializes in one task. This is the only type of AI that exists today and has real-world applications.
- General AI (AGI), or strong AI, can apply knowledge in a wide array of tasks. It can understand, learn, adapt, and implement information from different domains, exhibiting a form of consciousness. Technologies like ChatGPT have not yet reached the level of artificial general intelligence.
- Superintelligent AI (ASI) outperforms human intelligence in most cognitive tasks, demonstrating superior problem-solving, learning, and adaptability capabilities.
However advanced recent developments may seem, we are still at the stage of weak AI. Superintelligent AI is just a fictional concept, and only time will tell whether we’ll reach that level. Still, we’re closer than ever to AGI. What an exciting time to learn AI!
Artificial Intelligence vs Machine Learning vs Deep Learning vs Data Science
Essentially, artificial intelligence involves the development of computer systems mimicking human intelligence. And contemporary AI solutions use machine learning (ML) and deep learning (DL) to achieve this.
Machine learning enables computers to learn and improve from data without specific instructions on how to perform a given task. ML models analyze and extract patterns from large datasets, allowing the system to make predictions or execute a number of actions with increasing accuracy.
Deep learning is a branch of machine learning inspired by the human brain’s function. DL models represent artificial neural networks that pass information through several layers and generate an output. They outperform simpler ML models at the expense of interpretability. In other words, they are a black box, and we don’t know exactly what happens inside the hidden layers.
How are AI, ML, and DL connected to data science?
Data science is a multidisciplinary field that involves the collection, analysis, and interpretation of data to extract insights and inform decisions. In their daily line of work, data scientists use data analysis, machine learning, and deep learning, to achieve this. But while most problems can be solved with data analysis, ML, or DL techniques, some may require the development of AI solutions.
At the same time, knowing when and how to use AI technologies is equally important. Most business settings don’t require a profound knowledge of the mechanics behind AI tools but rather the proficiency to use them. In addition, not all problems call for complex solutions—simpler approaches are sometimes more effective and certainly cheaper and easier to implement.
Keep this in mind when you’re learning AI. 365 Data Science helps you acquire not only the technical skills, but also the business, strategic, and analytical thinking needed to leverage technologies effectively.
How to Learn AI Step by Step
As a new field, AI is rarely encountered as a standalone major in universities. Most AI specialists come from related STEM disciplines like data science, computer science, statistics, or mathematics.
If you wish to pursue a more traditional learning path, you can obtain a degree in one of these academic fields or look for a dedicated AI program. The requirements may vary depending on your chosen AI career. Research and scientific roles typically require formal higher education, while applied roles prioritize practical skills and experience. And given its recency and dynamic nature, online AI education and independent learning are common ways to start your career.
So, if you wish to learn AI on your own but aren’t sure where to begin, follow the steps below.
Step 1: Build Fundamental Theoretical Knowledge
To understand and apply the complex concepts of artificial intelligence, you need a solid theoretical foundation in mathematics, statistics, and data.
While you don’t have to be a mathematician to learn AI, deep learning and machine learning are based on mathematical principles. So, understanding concepts related to linear algebra and calculus will improve your ability to detect and fix errors in models and even develop new algorithms.
And before you frown at the idea of studying calculus, keep in mind that advanced concepts are only needed if you want to create AI algorithms. Using existing algorithms for data science doesn’t require calculus. So, if your goal is to understand and be able to work with AI, having a general idea of mathematical concepts will suffice.
You can start your AI learning journey with our Mathematics course and build a solid theoretical foundation for your future career.
Statistics provides the tools to analyze, interpret, and visualize data, which is crucial for understanding and evaluating the performance of AI models. In addition, many AI techniques are based on statistical principles such as regression, clustering, and classification.
So, the next stop in your AI learning journey should be statistics. Our Statistics course covers all crucial concepts to help you get started.
Probability provides a framework for making decisions under uncertainty, which is the basis of AI. In essence, AI models estimate and choose the most probable outcome and learn by updating probabilities as new information becomes available. Some algorithms, such as Naïve Bayes, are almost entirely based on probabilistic principles.
Our Probability course provides the fundamental concepts necessary for learning artificial intelligence.
Since AI models are trained on data, working with it should be second nature to you.
- Data collection: Learn key data collection methods, best practices, and how to use APIs, web scraping tools, and large databases.
- Data cleaning and preprocessing: Real-world data is often messy and incomplete, so you need to know how to clean and preprocess it to suit AI models. This could involve handling missing values, removing outliers, or balancing imbalanced data.
- Data wrangling: Sometimes, you need to transform raw data into a different format to facilitate the analysis. You can manipulate and reshape it using Python or R.
- Database management: Working with large databases is an inseparable part of the AI process. SQL enables you to handle, query, and manage large datasets efficiently.
Step 2: Learn Programming
Programming is an important part of an AI specialist’s role. It is the key to turning theoretical concepts into functional algorithms, and you cannot understand, develop, and implement AI models without it. Although tools like ChatGPT might help you write code faster, you still need to understand programming and learn to code.
Python and R are especially prevalent due to their robust libraries designed specifically for AI, ML, and DL. So, start by learning the fundamentals of R and Python programming, then specialize in using frameworks optimized for AI.
This popular Python library supports large, multidimensional arrays and matrices, offering various high-level mathematical functions to operate on them. Although not an AI library per se, nearly every AI tool uses NumPy.
Pandas is ideal for data manipulation and analysis of tables with different data types. NumPy and Pandas are the backbones of data manipulation in Python.
scikit-learn is a popular ML Python library suitable for data mining and analysis. It also supports various supervised and unsupervised learning algorithms.
TensorFlow is an open-source library developed by Google, commonly used to build and train deep learning models. It provides multiple levels of abstraction, allowing you to select the right one for your needs.
As one of the most popular deep learning libraries, TensorFlow comes with a huge number of guides. This makes it an ideal starting point for learning to create DL models.
This list of libraries isn’t exhaustive, but it’s a good start when you’re learning how to program AI solutions.
Step 3: Leverage Machine Learning
Nearly all contemporary AI solutions are developed using machine learning. So, understanding key ML concepts is crucial whether you aim for a research or applied role. Your knowledge of math, statistics, probability, programming, and data-related concepts will provide a solid foundation for your ML studies.
You can start with our beginner-friendly Machine Learning in Python course to grasp the logic behind key ML methods like linear regression, cluster analysis, logistic regression, and k-means clustering. Continue with our Machine Learning Algorithms A–Z course to understand the inner workings of more advanced ML algorithms.
But there’s more to machine learning than that. You must be able to execute the end-to-end ML process, from defining а problem to model deployment. In addition, knowing when and how to apply ML in a real-life business context is equally important.
Step 4: Understand Deep Learning
Deep learning is crucial for AI, as it allows you to build more advanced models. With the knowledge you’ve acquired so far, understanding the logic behind deep neural networks will come intuitively. You’ll simply have to obtain the necessary technical skills and learn the terminology. Our Deep Learning with TensorFlow 2 course is a good place to start.
Step 5: Specialize in a Subfield
Once you’ve got the fundamental AI knowledge, you can select your field of specialization—natural language processing, computer vision, robotics, etc. There are plenty of jobs in AI, and each one requires a slightly different set of skills and qualifications.
Step 6: Attain Soft Skills for AI
As advanced as it’s become, AI cannot yet comprehend the intricacies of real-life situations which require adaptability, intuition, and strategic thinking. Technology may change rapidly, but these soft skills will remain in demand.
To succeed in today’s world, AI professionals need to understand not just the technical aspects, but also the business and strategic context. They need to communicate effectively with various stakeholders, make decisions, and leverage AI to achieve company goals.
We, at 365 Data Science, can equip you with everything you need to thrive in an AI-driven world:
- Data Strategy
- Data Literacy
- Communication and Presentation Skills for Analysts and Managers
- Machine Learning Deep Dive: Business Applications and Coding Walkthroughs
- Data-Driven Business Growth
- AI Applications for Business Success
- Product Management for AI & Data Science
By adding these soft skills to your technical data and programming toolbox, you'll be well-prepared for the future of work.
Step 7: Commit to Lifelong Learning
The AI field is evolving rapidly, so your artificial intelligence training will never be truly complete. You should embrace this reality with curiosity—after all, continuous learning is the only way to stay current and adapt to the dynamic AI landscape.
Take every opportunity to upskill, follow relevant news, read scientific literature, and attend conferences and seminars. Lifelong learning and improvement will make you a better specialist and help stay up to date with recent technological advancements.
The future of machine learning and AI holds both exciting new opportunities and uncertainty. The progress made since the early days of AI—from symbolic reasoning to complex deep learning systems—has revolutionized today’s work landscape and sparked the need for acquiring new skills.
But there’s still a long way for us to reach the next stages of AI where new demands will continue to emerge. By nurturing a mindset of lifelong learning and improvement, you position yourself for success. So, we encourage you to embrace emerging technologies and contribute to the ongoing AI revolution.
Learn AI-Proof Skills with 365 Data Science
Recent AI developments have taken the world by storm. Whether you perceive AI as a disruptive force or an exciting opportunity, its influence is undeniable. And learning how to leverage it is the only way to stay current.
At 365 Data Science, we understand what it takes to succeed in today’s world. That’s why we don't just teach technical data and programming proficiency; we also equip you with the soft skills, business understanding, strategic, and analytical thinking needed to thrive in an AI-driven world.
Sign up and try our program for free.