How To Learn AI From Scratch [2023 Guide]

Artificial intelligence is a fascinating and growing field. Although we’re far from having the robot servants depicted in science fiction films, AI is already a part of our everyday lives. While some AI applications, like autonomous cars, are still in the developmental stages, other uses, like predictive analysis, are already here. 

AI is a versatile field with applications in all industries, which means that AI-related jobs are in high demand. A McKinsey survey found that AI is increasingly being used for service operation optimization, product enhancement, risk modeling, and fraud prevention. Between now and 2030, the demand for computer and information research jobs is expected to grow by 22%. 

What Is Artificial Intelligence?

Artificial intelligence is the building of computer programs that can mimic tasks associated with human intelligence. AI solves problems by using computer programming and large data sets. The field of AI includes machine learning, deep learning, and natural language processing, which allow computers to “learn” from experience and perform human-like tasks, often much more efficiently than humans can. 

This type of AI is called narrow or weak AI. In these cases, a computer accomplishes a specific task by recognizing patterns in large data sets. Some examples of narrow AI include recommendations from your streaming platform, chess bots, and smart speakers. 

While narrow AI can adapt to inputs, it can’t perform outside of its given parameters. Still, it has its uses. The Fourth Industrial Revolution and the digital-first approach of modern businesses generate enormous amounts of data that can fuel narrow AI applications.

Strong AI, also called artificial general intelligence (AGI), is the kind of artificial intelligence associated with robots in science fiction plots. This type of AI isn’t going to happen soon, although developers are working to overcome the challenges associated with AGI, such as prediction and control models. 

Why Learn AI?

AI is an exciting field at the forefront of finding solutions to society’s most pressing problems, including disease, pollution, and climate change. It’s also a rapidly growing sector of the economy, with AI software revenue expected to increase 21.3% from the previous year, for a total of $62.5 billion in 2022. By learning AI, you’ll be prepared for a challenging and rewarding career that pays well too. The average base salary for an AI engineer is over $119,000. 

What Does an AI Engineer Do?

The roles and responsibilities for an AI engineer will vary based on their industry, but generally speaking, AI engineers develop AI systems and applications to make better decisions, improve performance, and increase efficiency. AI engineering is a complex job that requires you to: 

• Achieve objectives using AI methods 
• Solve problems with logic, probability analysis, and machine learning
• Monitor and steer development projects by analyzing systems
• Understand and apply best practices in speech recognition, data processing, data mining, and robotics 

8 Steps for Effectively Learning AI

One of the biggest hurdles to learning AI is not knowing where to start. It’s a broad field that consists of many components. Many of the concepts involved in AI rely on advanced math and formal logic, which can be an obstacle to joining the industry. To help you overcome these hurdles, we’ve broken down the field of AI into a manageable step-by-step guide to mastery.

Understand the Prerequisites

Before you start learning AI, you should have a solid foundation in the following areas.

Computer Science Fundamentals

You’ll need to understand the fundamental principles of computer science before you can start programming AI. This includes: 

• Theory and algorithms such as Boolean algebra, binary mathematics, and theory of computation
• Computer hardware systems, including the physical components of computers, digital logic, computer architecture, and network architecture 
• Software systems and elements such as programming languages, compilers, computer graphics, and operating systems

Probability and Statistics

Probability is one of the core principles used in AI, as it allows you to teach the computer to “reason” in the face of uncertainty. Machines learn through data, which they understand through statistics. Probability and statistics can answer questions such as: 

• What is the most common outcome? 
• What is the expected outcome? 
• What does the data look like? 

Probability and statistics for AI should include some of the following topics:

• Numerical and graphical description of data
• Elements of probability
• Sampling distributions
• Probability distribution functions
• Estimation of population parameters
• Hypothesis tests 

Mathematics

In addition to probability and statistics, you’ll need to know some math fundamentals to master AI, including: 

• Linear algebra, which is essential to understanding approaches to AI and machine learning
• Basic differential and some multivariable calculus, which deals with changes in parameters, functions, errors, and approximations
• Coordinate and nonlinear transformations, which are key ideas in AI
• Linear and higher-order regressions to make predictions based on data sets
• Logistic regression to classify data 
• Numerical analysis to turn math formulae into effective code

Programming

You’ll also want to be familiar with the programming languages suitable for developing AI applications. Some of the most useful include:

• Python, which is easy to learn and has widely available resources and support
• Java, which is user-friendly and platform-independent
• R, which was created to handle large data sets
• Prolog, which was used to create the first-ever chatbot therapist, Eliza, in 1966
• Lisp, which is the second-oldest programming language, predated only by Fortran
• SQL, which is used to manage databases 

Data Structures

To learn, AI requires input in the form of data. Data structures are different methods of organizing data to be used effectively. If you want to launch a career in AI, you’ll need to understand how to use and apply the most suitable data structure for your program. Some of the most common types of data structures are: 

• Array
• Linked list
• Stack
• Queue
• Binary tree
• Binary search tree
• Heap
• Hashing
• Graph
• Matrix

Algorithms

An algorithm gives step-by-step methods for performing a calculation. To facilitate machine learning, you’ll have to design algorithms that allow a computer to learn on its own. Algorithms can use data mining and pattern recognition to make recommendations. This is how apps recommend shows for you to watch and how Facebook decides what shows up in your feed. 

Algorithms are also used for more consequential purposes, such as approving home loans and deciding jail sentences. Algorithms are powerful tools, but they’re not as objective as they sometimes seem, and massive amounts of data can lead to some spurious correlations. So algorithms have to be tempered with the good judgment of human minds.

Ace AI Theory 

Once you’ve learned the prerequisites, you’re ready to dive into AI theory. Regardless of whether you learn AI through an in-person class, with a self-paced online course, or in piecemeal fashion with YouTube videos, you’ll need to cover the same basic theoretical concepts. Here are some of the most important tenets that you’ll need to learn:

Problem-Solving

The purpose of AI is to solve a problem, which involves a number of techniques, including algorithms and heuristics. An AI system includes an agent and its environment. In AI, an agent is the program that makes decisions. A problem-solving agent in AI is focused on achieving its goal. Once the goal is formulated, a process for solving the problem is created through problem formulation. This involves several components, including: 

• The initial state of the agent
• The possible actions the agent can take
• A transaction model that describes each action
• A goal test to determine if the goal has been achieved
• The cost of each action path

Reasoning

Reasoning is the process of drawing conclusions or making predictions based on your existing knowledge. Because machines aren’t capable of thinking, they have to be programmed to do this kind of reasoning with algorithms. When you’re programming AI to reach conclusions, you’ll need to teach it how to complete a task based on one of several reasoning methods, such as the following.

Deductive reasoning. This type of reasoning uses existing data to determine if the premise of an argument is valid. It’s a kind of reasoning that applies general principles to a specific case. If you’ve ever taken an introductory logic course, you probably remember the basic deductive reasoning example: If all men are mortal and Socrates is a man, then Socrates is mortal. 

Inductive reasoning. Unlike deductive reasoning, inductive reasoning produces a general conclusion from specific observations. In inductive reasoning, a conclusion can be false even if all of the observations are true. For example, you might notice that all of the dogs in your neighborhood are brown and reach the erroneous conclusion that all dogs are brown. In AI, supervised learning uses inductive reasoning to generalize from specific data. The more comprehensive a database is, the better its generalizations will be. 

Abductive reasoning. Abductive reasoning is the process of drawing a conclusion that most likely fits the observations. This type of reasoning is used by doctors to make medical diagnoses. Abductive reasoning is similar to deductive reasoning, but the premise doesn’t guarantee the conclusion. In AI, this type of reasoning could be used by a diagnostic assistant program to suggest a diagnosis based on the symptoms a patient exhibits.

Common-sense reasoning. Common-sense reasoning is an informal type of reasoning that relies on experience. Using good judgment, rather than formal rules, it is implemented with heuristic knowledge and heuristic rules, which are common-sense rules intended to increase the likelihood of solving a problem. Common-sense reasoning is most widely used in the AI field of natural language processing to help computers communicate more effectively with humans. 

Monotonic reasoning. In monotonic reasoning, once a conclusion is reached, it will never change, even if additional facts are added. Any theorem that proves an example is using monotonic reasoning. For example, “The earth revolves around the sun.”

In AI, monotonic reasoning can be used for applications such as content filtering. A website that contains any amount of inappropriate content will be filtered out, and that decision will never change, even if the website has plenty of appropriate content. 

Non-monotonic reasoning. In non-monotonic reasoning, the conclusion may be invalidated if new information is added. Incomplete and uncertain models use non-monotonic reasoning. This type of reasoning is useful in AI applications such as robotic navigation systems. 

Data Manipulation

AI relies on data sets to learn and to make predictions, so you’ll need to be skilled at structuring data into a useful format. You will need to create programs that identify connections among data sets. SQL is the programming language used to manage databases, and R is frequently used in data science applications. 

Natural Language Understanding

Natural language understanding is a subset of natural language processing that uses programming to understand human speech. It allows computers to understand human speech without the formal syntax of computer languages. Natural language understanding also allows computers to communicate back to humans in their own language. 

Natural language understanding uses algorithms to analyze human speech and format it as a structured data model based on sentiment, named entities, and numeric entities. Voice-enabled assistants and chatbots both use natural language processing. 

Computer Vision

Computer vision is the process of training computers to observe and understand visual input. It allows computers to extract information from images, videos, and other visual inputs. The program can then use that information to take action or make recommendations. Computers can analyze visual information much faster than humans, analyzing thousands of images per minute. 

As with other AI training methods, computer vision requires large data sets to notice small differences and recognize particular images. Algorithmic models in machine learning enable the computer to teach itself about visual data. 

Automated Programming

Automated programming is a type of computer program that generates the code for another program based on a set of specifications. One example of this is DeepMind’s AlphaCode, which writes computer programs well enough to rank in the 54th percentile of human programmers when tested in coding challenges. 

AlphaCode was given a set of challenges used in coding competitions such as transforming a random string of letters into another random string of the same letters using limited inputs. AlphaCode approached this challenge by generating a huge number of possible answers. It then ran the code, tested the output, and tested the answer to select the best option. 

AlphaCode isn’t the only example of automated programming: Microsoft and OpenAI have GPT-3, which automatically completes strings of code. Automated programming is still limited in scope, but it could eventually make programming more accessible to non-programmers.

Master Data Processing

Data processing is such a significant aspect of AI that it’s a field unto itself. Big data permeates all aspects of modern life. Almost all businesses incorporate data-driven decision-making into their strategies. This is possible through machine learning, which relies on processing massive data sets. If you’re interested in the big data element of AI, you might enjoy one of the following careers. 

Data Preprocessing

Data preprocessing involves transforming raw data into an understandable format and ensuring its quality. The quality of data depends on its: 

• Accuracy
• Consistency
• Completeness
• Timeliness
• Trustworthiness

During data preprocessing, data is cleaned to remove inaccurate, incomplete, or unnecessary data. Multiple sources of data are also combined into one data set during this phase. Finally, data is reduced and transformed so that it’s ready to use. 

Machine Learning Engineering

A machine learning engineer builds AI systems that automate predictive models based on machine learning. Their systems use huge data sets to generate and develop algorithms that learn from results and refine the process of performing future operations for more accurate results. 

What Is Machine Learning?

Machine learning is a branch of AI in which computers are taught to learn and improve on their processes with minimal human intervention. Machine learning programs can even detect more complex and subtle patterns than humans can. This happens through the use of data sets and pattern recognition. There are two main types of machine learning: supervised learning and unsupervised learning. 

In supervised learning, you can collect or produce data from a previous output of machine learning. You give the computer a training set of labeled data points. 

In unsupervised learning, the algorithm tries to discern the inherent structure of the data without a training set. This can help you find many unknown patterns in your data. 

How To Learn Machine Learning

Machine learning is a specialized field of AI, so you’ll still need to understand the prerequisites and general AI theory. In addition, here are some steps that you can take to specialize in machine learning:

• Learn Python
• Learn data science tools such as Jupyter and Anaconda
• Learn data analysis tools like Pandas, NumPy, and Matplotlib
• Use the Python library SciKit-Learn to find patterns in your data
• Learn to build deep learning neural networks
• Work on your own projects

How Is Machine Learning Related to AI?

Machine learning is a branch of AI. Machine learning is one way to implement AI principles, giving computers the ability to learn on their own without being explicitly programmed. 

Data Scientist

Data science is closely related to machine learning engineering, but they aren’t the same. Data science is a broad field aimed at extracting insights from data. Machine learning is one tool data scientists use. 

As far as education goes, data scientists often have advanced degrees in a variety of subject areas, while machine learning engineers usually come from the field of software engineering. 

What Is Data Science?

Data science is the process of using scientific methods, processes, algorithms, and systems to extract meaning and insights from unstructured data. 

What Does a Data Scientist Do?

Data scientists use machine learning or deep learning models to develop solutions for business problems. Unlike machine learning engineers, data scientists often use existing machine learning tools to process data, although they may have to develop novel applications if needed. After identifying business problems that can be solved with machine learning, a data scientist will then develop custom algorithms and models to solve those problems. 

How To Learn Data Science

As with machine learning, mastering the prerequisites and fundamentals of AI is necessary for learning data science. Because it’s a branch of AI, many of the same principles apply. Once you’ve mastered the basics, you can continue your learning journey by: 

• Mastering data cleaning, which will be a huge part of your job
• Using existing data sets to work on your own projects
• Gain experience and contribute to valuable work through data science volunteering

How Is Data Science Related to AI? 

Data scientists use AI to do their jobs, so there’s a lot of overlap between data science, machine learning, and artificial intelligence. The biggest difference among the three is that data science uses AI and machine learning to produce insights. Data science relies on humans to gain insights and make conclusions from the results produced by machine learning. 

Data Engineer

It can be difficult to distinguish between a data scientist and a data engineer, particularly if you look at job postings. Data scientists are often expected to also fill the role of a data engineer. However, these two roles are distinct. 

What Is Data Engineering?

Data engineering is the process of designing and building pipelines for transforming data into a usable format. These pipelines take data from different sources and combine them into a single source for further analysis. 

What Does a Data Engineer Do?

Data engineers build and maintain data infrastructure that serves as the foundation for all other data functions. They use databases, servers, and large-scale processing systems to transform unstructured data into usable formats. They do this through a process called ETL (extract, transform, load) using tools such as SQL, Cassandra, and BigTable. 

How To Learn Data Engineering

Data engineers need to be well-versed in the following skills: 

• Data warehousing 
• ETL tools such as Xplenty or Hevo
• Machine learning
• Database systems such as SQL
• Programming languages such as Python and Julia
• Algorithms and data structures
• Distributed systems

How Is Data Engineering Related to AI?

Data engineering provides the raw materials for data-related AI tasks. Machine learning and AI require such massive amounts of data that it wouldn’t be possible to scale them without data engineering. The exponential growth in data that’s created on a daily basis feeds AI, but the majority of it is unstructured. Data engineering transforms unstructured data into usable formats for AI developers. 

Work on AI Projects

The best way to develop an understanding of AI algorithms is to build them from scratch. Start with projects that require simple algorithms and then take on harder projects, gradually increasing the skill level required. When you’re trying to master AI, theory alone isn’t enough. A practical, hands-on approach will cement your learning and boost your skills. 

How To Choose Projects

There are several ways to choose AI projects. Because AI is applicable to every industry, the options can seem overwhelming. Start by choosing projects based on your interests, fundamental projects, and projects that add value to your community. 

Choose a Project Based on Your Interests

Pick a project that combines learning AI with your other hobbies and interests. If you’re an avid gamer, design a game you can play against. Chess is a classic option. 

Work on Fundamental Projects

There are some traditional projects that are routinely recommended for beginners. These projects are fun and teach some foundational skills. Although there is controversy over what’s considered foundational in AI, there’s no doubt that learning to train one model on a huge amount of data and then adapting it to different applications is a fundamental skill in AI. Even if you eventually decide this model isn’t foundational, it still has many practical uses. 

One common fundamental project recommended for beginners is using Enron’s email database to analyze social networks or detect anomalies. The Enron debacle in 2001 was one of the most massive fraud scandals in recent history. The investigation resulted in a database of more than half a million emails that are publicly archived. 

Build Professionally and Personally Valuable Projects

One way to make your portfolio stand out is to include projects that are important to you personally and add value to the community. Choose an issue that is significant to you and design an algorithm to address a problem related to it, like using social media posts to predict depression. 

Ideas To Get You Started

If you can’t think of any projects or you’re just looking for inspiration, here are some ideas: 

• Fake news detection
• Translation
• Stock price prediction
• Facial recognition
• Human activity recognition
• Sales price forecasting

Learn and Work With AI Tools

There are many AI tools you can choose from, but these are some of the most popular frameworks and tools currently in use.

SciKit-Learn

SciKit-Learn is one of the most popular tools in ML libraries. It’s used with unsupervised and administered calculations. SciKit is a great tool to use for fledglings. 

TensorFlow

TensorFlow can be used for a variety of machine learning tasks, but it’s especially useful for the training and inference of deep neural networks. 

PyTorch

PyTorch was developed by Facebook. It’s used primarily for applications such as natural language processing and computer vision. 

Opt for AI Courses

Although you could take a piecemeal approach to learning AI, choosing a formal course will accelerate the process and provide some structure. A class can provide accountability, feedback, and resources if you run into questions or problems. 

Depending on how you learn best, you can choose a self-paced MOOC, a formal graduate degree program, or a bootcamp. 

Apply for an Internship

Once you’ve finished your classes and built a portfolio, applying for an internship is a great way to get some real-world experience to make your job search easier. To increase your chances of landing an internship, you can:

• Tell people in your professional and personal networks that you’re looking for an internship
• Attend local meetups and AI hackathons 
• Keep your professional networking accounts updated 
• Take advantage of career resources from your coursework 
• Prepare for your technical interview 

Get a Job

Your internship should provide experience and professional connections that will help you land a job. When you finish your internship, reach out to the contacts you’ve developed to let them know you’re looking for a permanent position.

The most valuable aspect of an AI internship is the opportunity to solve real-world problems. Be sure to highlight the AI projects you worked on during your internship when you’re discussing your portfolio with prospective employers, including the specific contributions you made. 

Can You Learn AI on Your Own?

You can learn AI on your own, although it’s more complicated than learning a programming language like Python. There are many resources for teaching yourself AI, including YouTube videos, blogs, and free online courses.

Because AI includes advanced mathematical concepts such as linear functions, linear algebra, probability, statistics, and logic, it may be easier to learn as part of an organized course. However, as long as you have a comprehensive learning plan and are dedicated, you can learn by yourself. 

AI FAQs

Is AI Easy To Learn?

It depends. AI includes some advanced concepts that may be difficult for some to learn, depending on your background. If you choose a good program that teaches these concepts in manageable chunks, it will be easier to learn. A good instructor can also make a big difference, and they can even serve as a mentor once you’ve launched your career. 

Can a Fresher Learn AI?

If you’re determined to learn AI as a fresher (a university freshman) you can, but it won’t be easy. As a first-year student, you undoubtedly have a full schedule already. Adding AI classes or independent work on top of your regular classes may be too much. One option for learning as a fresher is to take advantage of your school breaks. Studying AI during vacations and holidays will let you learn it without taking away from your regular classes. 

Can You Learn AI Without Knowing Programming and Coding?

While some platforms purport to be no-code AI solutions, you will have to learn programming and coding if you want to become proficient in AI, as the fundamental processes of AI are carried out via programming. Designing and executing problem-solving algorithms is vital to teaching computers to solve problems like humans. 

How Long Does It Take To Learn AI and Build a Career in AI?

It depends on what you’re learning and what you already know. If you’re starting from scratch and learning the basics of AI, you should be able to do it in about six months. At that point, you can start looking for entry-level positions. If you’re learning more complicated AI, such as data science, you may need an advanced degree that will take several years to earn. As far as building your career, you’re generally considered junior level for the first two years of work. Between years two and five, you’ll be considered a mid-level AI engineer. After five years of working in the field, you’ll be considered for senior-level roles.

Since you’re here…
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