Learn Artificial Intelligence for Free from Scratch to Mastery | The Comprehensive Beginner's Guide – Lesson 1 (Learn AI from Scratch)

Al-Ankabut · November 3, 2026 · 5 min read

Learn artificial intelligence from scratch for beginners

Learning artificial intelligence from scratch has become easier than ever, even if you have no prior experience in programming or technology. If you want to understand what Artificial Intelligence (AI) means, how computers learn from data, analyze information, and perform tasks intelligently, this guide is designed to help you start step by step and learn the fundamentals of AI in an easy, practical way tailored for beginners.

Followers of Information Net, today we embark on a journey to learn artificial intelligence for free from scratch to mastery. We will walk together through a series of progressive educational lessons, starting from understanding core AI concepts, knowing how it works and how machines learn, all the way to learning programming, building models, and smart applications. You do not need to be a professional programmer or have prior experience because we will start completely from the basics, learning the Python language as one of the most important programming languages used in the field of AI. Afterward, we will explore Machine Learning and Deep Learning techniques, discover Generative AI tools, and understand how they work.

Our goal is not just to teach you how to use ready-made AI tools, but also to understand how they work, how you can design smart models, and how to build your first project by yourself. With each lesson, you will gain new knowledge and skills to help you progress on your path of learning artificial intelligence—starting from simple concepts, moving through programming and practical application, until you have a solid foundation to build upon and continue your journey in the field. Get ready and take your first step with us into the world of artificial intelligence!

Objectives of Lesson 1

By the end of this lesson, you will be able to understand the basics of artificial intelligence and know how to start your path in building it. We will focus on the following objectives:

Understanding the meaning of artificial intelligence, how it works, and how it learns from data.
Knowing the difference between using AI tools versus building tools and models yourself.
Learning the basic stages involved in developing artificial intelligence tools.
Understanding the types of machine learning and the uses of each type.
Discovering the most important programming languages, libraries, and tools used in the field.
Knowing the appropriate path to learn AI from scratch, starting from Python up to executing your first practical project.
Preparing yourself to transition to Lesson 2 and start learning Python basics.

1. AI Basics: What is Artificial Intelligence and How Does It Work?

Artificial Intelligence (Artificial Intelligence or AI) is a field of technology aimed at developing software and systems capable of performing tasks that typically require human capabilities, such as speech understanding, image recognition, information analysis, prediction, and problem-solving.

Simply put, instead of writing fixed instructions for a computer for every single situation, we train it on large amounts of data so it can learn from them and handle new situations.

How Does AI Learn?

Imagine you want to teach a computer to distinguish between pictures of cats and dogs. Initially, you provide a large collection of images, with the animal in each image clearly labeled. Next, we use an algorithm and an AI model to analyze the images and learn the characteristics that help differentiate them.

During training, the model compares its predictions with the correct answers and adjusts its settings to minimize errors. Once training is complete, we can show it a new image it has never seen before and ask it to predict whether the image is a cat or a dog.

Does a Computer Think Like a Human?

Not in the human sense. Artificial Intelligence analyzes data and uses patterns it has learned to produce answers or predictions, but it does not necessarily possess consciousness, emotions, or human-like understanding.

This is important to understand from the start because creating artificial intelligence does not mean we are creating a human brain; rather, it is the process of developing models and programs that can perform smart tasks with varying degrees of accuracy.

The Difference Between Traditional Programming and AI Programming

To understand the value of AI, let's compare it to traditional programming using a real-life example: the email spam filter.

In traditional programming, a programmer manually writes rules, such as: if the message contains the phrase "guaranteed profit", then it is spam. The problem is that spammers constantly change their tactics, so the programmer would have to chase and adjust rules all the time, and many spam emails would still slip through the filter.

In artificial intelligence, however, we feed the model thousands of messages, each labeled as spam or normal. The model autonomously extracts the patterns that distinguish the two types, even those an average human might miss. When new tactics emerge, we can simply update the data and retrain the model instead of starting over.

This does not mean traditional programming is obsolete; it is still the best choice for many tasks with clear, fixed rules, such as calculating invoices or currency conversion. AI, on the other hand, becomes the optimal choice when rules are complex, changing, or difficult to describe in words.

Components of Any AI System

No matter the scale of the system, whether simple or massive, you will likely find it built upon five core elements:

Data: The raw material the model learns from, such as images, texts, sounds, and numbers. The cleaner and more diverse the data, the better the result.
Algorithm (Algorithm): The mathematical method or recipe we use for the model to learn from the data.
Model: The output we obtain after training; the program that has learned the patterns and is capable of making predictions.
Training: The process of feeding data to the model multiple times, during which it adjusts itself each time to reduce errors.
Evaluation: The stage of testing the model on new data to determine whether it has genuinely learned or merely memorized the examples.

If you think of it like a teacher and a student, data is the curriculum, the algorithm is the teaching method, the model is the student after learning, training is studying, and evaluation is the exam.

Terminology You Need to Know From the Start

You will encounter these terms frequently in any course or article about AI, so let's simplify them now:

Term	Simple Meaning	Example
Dataset	The file or collection containing all the examples.	Thousands of images labeled as cat or dog.
Feature	A piece of information describing each example.	House area and number of rooms.
Label	The correct answer we want the model to learn to predict.	House price or animal type.
Training	The stage where the model learns from data.	Presenting examples to the model and adjusting its settings.
Prediction	Using the trained model with new data.	Predicting the price of a new house.
Overfitting	The model memorizes examples instead of understanding patterns, failing with new data.	A student who memorizes questions and gets confused by any new question.

You are not required to memorize these terms right now, as they will repeat frequently in upcoming lessons and you will naturally get used to them.

Why Learn AI from Scratch Now?

As we saw at the beginning of the lesson, AI applications are now present in most devices and applications we interact with daily, from mobile phones to maps and video platforms. This means understanding AI basics has become a useful skill not only for programmers, but for anyone who wants to understand and benefit from the technology around them.

Furthermore, learning AI has become more accessible than before because you can start for free using Python, Google Colab, and open-source libraries. You don't need expensive hardware or prior experience; the important thing is to start with small, organized steps, which is exactly what we will follow in this series.

2. What is the Difference Between Using AI and Building AI?

There is a big difference between using a ready-made AI tool and being the person who develops that tool from scratch.

Using AI means you interact with an existing program and ask it to perform a specific task. For example, you might use ChatGPT to write an article, an image generation tool, or a text translation program. In this case, you benefit from the technology without needing to know all the programming details behind it.

Building AI, on the other hand, means you participate in developing the model or system itself. This may include collecting and organizing data, choosing algorithms, writing code, training the model, testing it, and improving its results.

An Example Illustrating the Difference

Let's assume you are using an AI tool to generate images:

If you write a description for an image and click the generate button, you are using AI.
If you develop an image generation model, prepare the data, write the code, and train the model, you are participating in building AI.

This does not mean everyone has to build a massive model from scratch to be an AI developer. You can start by using open-source models and learning how to run, modify, and connect them to your applications.

Why Learn to Build Instead of Just Using?

Using ready-made tools is important and you will need it all the time, but learning how to build gives you advantages you won't find if you stop at usage:

Understand the tool from the inside: When you know how a model learns, you will understand why it sometimes makes mistakes, when to trust its results, and when to review them.
Solve your own specific problems: Ready-made tools are general for everyone, but you might have a specific problem in your work or study that requires a custom model tailored to your own data.
Become a sought-after skilled professional: Companies building products based on AI need people who understand the programming side, not just tool users.
Control what you build: When you build yourself, you can choose the data, settings, and presentation method, developing the solution according to your needs.

Levels of Involvement in Building AI

Building is not a single level; there are degrees, and you can start from the simplest and progress to the harder ones:

Using a ready-made tool: Such as using ChatGPT or image generators without writing code.
Connecting a ready-made model to your application: Using an API to integrate an existing AI model inside your website or app, like a customer service chatbot.
Customizing an existing model: Taking an open-source model, modifying it, and training it on domain-specific data to be more accurate in a specific task.
Training a model from scratch: Collecting data, designing the model, and training it entirely, which is the hardest and most resource-intensive level.

As a beginner, levels two and three are the best places to start after learning the basics, because you will see quick results and learn from experience.

Quick Comparison Between Using and Building

Comparison Point	Using AI	Building AI
Required Skill	Ability to write a clear prompt.	Programming, understanding data and models.
Starting Time	Start in minutes.	Requires a period of learning and practice.
Output	An answer, image, or summary.	A model or application you can develop and share.
Control Over Result	Limited by the tool's capabilities.	High, depending on your level.

3. How Were AI Tools and Applications Developed?

The AI tools and applications we use today were not created with the push of a button; rather, they are the result of many years of research and development in programming, mathematics, and data science.

The basic idea is that developers and researchers build mathematical and programmatic models, then train them on data suitable for the required task. As the model evolves and improves, it becomes capable of performing more complex tasks.

Core Stages of Building an AI Tool

Defining the goal: First, we determine what the tool will do, such as understanding text, recognizing images, or answering questions.
Preparing data: We collect data suitable for the task, organize it, and review its quality so the model can learn from it.
Choosing the model: We select the appropriate model type and algorithm based on the problem we are trying to solve.
Writing code: We use programming languages like Python and specialized libraries to build the model and set up the training process.
Training the model: We present the data to the model, and it begins learning the patterns and relationships within it.
Testing results: We test the model on new data to determine its accuracy and ability to handle situations it hasn't been trained on.
Developing the tool: We refine the model and fix errors, after which we can connect it to a website or app for people to use.

How Tools Like ChatGPT Were Made

Modern conversational tools like ChatGPT rely on Large Language Models, which are models trained on massive amounts of text to learn language patterns and relationships between words.

After the basic training stage, the model can be fine-tuned using additional stages of training and evaluation to provide more useful answers and better handle user instructions.

However, it is important to know that building a model of this scale requires massive computing resources, data, and expertise. As a beginner, you are not expected to start at this level; you can learn using smaller models and open-source tools, then progress gradually.

Types of Machine Learning

When you start diving deeper into building AI, you will find that machine learning is divided into three main types, each with its own method and use cases:

Supervised Learning: Here, we present the model with examples alongside the correct answers—like a student solving questions with an answer key. Its most common uses include predicting house prices, classifying messages into spam and normal, and image recognition. This is the most suitable type for beginners because its results are easy to measure and understand.
Unsupervised Learning: Here, we present the model with data without answers and let it discover patterns and groupings on its own. For example, an e-commerce store might use it to segment customers into groups based on their purchasing behavior without anyone pre-defining those groups.
Reinforcement Learning: Here, the model learns through trial and error, receiving a reward for correct actions and a penalty for mistakes—much like teaching a child a new game by trial and error until they figure out the best way to win. It is used in gaming, robotics, and some control systems.

Tools You Will Use in Building

You don't need to learn all these tools at once, but it's helpful to know their roles from now on:

Tool	Its Role	When Do You Learn It?
Python	The core programming language in the field of AI.	At the beginning.
Google Colab and Jupyter	An environment where you write code, run it, and see results step-by-step.	At the beginning.
NumPy and Pandas	Handling numbers, tables, and data cleaning.	After Python basics.
Scikit-learn	Building and evaluating traditional machine learning models.	After data handling.
PyTorch and TensorFlow	Building neural networks and deep learning.	After machine learning.
Hugging Face	A platform featuring ready-made, open-source models you can try and modify.	When you are ready to try advanced models.

If you think about it, each tool builds on the previous one, which is why ordering matters and patience at the beginning saves you a lot of time later.

Your First Model in a Few Lines

To feel that the topic isn't as complex as some imagine, here is a quick look at a simple Python code snippet that builds a model to recognize iris flower types based on petal measurements. You don't need to understand every line right now; we will explain it in detail after learning the basics:

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
X, y = load_iris(return_X_y=True) # Data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
model = KNeighborsClassifier() # Model
model.fit(X_train, y_train) # Training
print(model.score(X_test, y_test)) # Evaluation on new data

The code does four things: loads the data, splits it into training and testing sets, trains the model, and then prints its accuracy on data it hasn't seen before. You can try it on Google Colab (a free browser-based Python environment) without installing anything. This is precisely the same cycle we discussed: data, model, training, evaluation.

4. What is the Easiest Way to Learn AI from Scratch to Mastery?

If you are a beginner with no prior programming experience, the best way to learn AI for beginners is to learn gradually and apply every piece of information in a practical project. Do not try to start by training massive models or diving into complex mathematics before understanding the basics.

Here is a roadmap you can follow:

Phase One: Understanding AI Basics

Start by understanding the meaning of artificial intelligence, the difference between machine learning and deep learning, and how computers learn from data.

Phase Two: Learning Python

Learn Python basics, such as variables, conditions, loops, functions, lists, and file handling. You don't need to be an expert in the language before moving to the next phase, but you must be able to write and understand simple code.

Phase Three: Learning Data Handling

Learn to use NumPy and Pandas libraries (for handling numbers and tables) so you know how to read, organize, and analyze data. This is a crucial step because data is the foundation upon which machine learning models rely.

Phase Four: Machine Learning

Start using Scikit-learn, and learn how to build, train, and test a simple model. Try projects like price prediction, data classification, and outcome forecasting.

Phase Five: Deep Learning

Once you understand the basics, move on to deep learning using PyTorch and TensorFlow (two libraries for building and training neural networks), and get familiar with neural networks and how they are trained.

Phase Six: Execute Real Projects

Start with projects suited to your level, such as:

A program that recognizes and classifies images.
A model that predicts house prices.
A simple text analysis tool.
A program that distinguishes between normal and spam messages.
An application connecting an AI model to a website.

Phase Seven: Specialization and Development

After gaining experience in the basics, choose the field you want to specialize in, such as natural language processing, computer vision, generative AI, or developing applications based on ready-made models.

Approximate Timeline

These durations are just estimates that vary from person to person, assuming you study 1 to 2 hours a day:

Phase	Approximate Duration	What Will You Be Able to Do?
Understanding Basics	About 1 week.	Explain what AI is and how it learns.
Learning Python	4 to 6 weeks.	Write simple programs using conditions, loops, and functions.
Data Handling	2 to 3 weeks.	Read, clean, and analyze a data table.
Machine Learning	4 to 6 weeks.	Build a simple prediction model and test it.
Deep Learning	6 to 8 weeks.	Build a simple neural network and train it.
Projects & Specialization	Ongoing.	Build real projects and specialize in a domain.

Tips to Help You Stay on Track

The hardest part of learning isn't understanding, but persistence. Here are some tips to help you:

Study a little every day: Thirty minutes daily consistently is much better than a whole day once a week.
Apply after every phase: A small project after each phase will cement the information better than reading ten lessons.
Take notes: Write down what you understood in your own style, and refer to your notes when you forget.
Don't fear errors: Error messages are a normal part of programming; read them carefully because they usually tell you where the problem is.
Use AI as an assistant: Ask it to explain a difficult concept with a simpler example or review your code, but don't let it think for you.
Don't compare yourself to others: Everyone has their own pace and circumstances; the important thing is that you progress compared to yesterday.

Common Mistakes to Avoid

Many beginners fall into the same traps, and avoiding them will save you months:

Drowning in math from day one: Start with the concept and application, and return to mathematical details when you need them.
Watching lessons without applying: True understanding comes when you write code with your own hands, experiment, and make mistakes.
Jumping between tools and courses: Choose one path and complete it before moving to another.
Ignoring data quality: An excellent model on bad data will give you bad results.
Copying code without understanding: If you copy working code without understanding it, you'll get lost the moment you need to modify it.

5. Frequently Asked Questions About Learning AI Programming

Can I learn artificial intelligence from scratch?

Yes, you can start without any prior experience. Start by understanding basic concepts, then learn Python and data handling, and gradually move on to Machine Learning and executing projects.

Do I need to be a math expert to learn artificial intelligence?

You don't need to be a math expert from the start. However, as you progress, you will need to understand the basics of algebra, statistics, probability, and some calculus concepts because they help you understand how models learn and improve their results.

Do I need to learn Python before AI?

It is best to learn Python basics first because it is one of the most popular languages used in developing AI models, and it features libraries that make implementing projects and experiments easier.

Can I learn artificial intelligence from a mobile phone?

You can start from a mobile phone using educational platforms and programming tools that run in the browser, but a computer will be easier and better when you start writing lots of code and executing larger projects.

Do I need a powerful computer?

Not at the beginning. You can learn Python and execute simple projects on an ordinary device, and use Google Colab to test some models that require higher computing resources. However, large projects may require stronger capabilities.

Can I learn artificial intelligence for free?

Yes, you can start learning AI for free using Python, VS Code, Google Colab, and open-source libraries. You might need to pay later for additional services or computing resources depending on the nature of your projects, but paying is not a requirement to start.

How long does it take to learn artificial intelligence?

The duration varies depending on your study time, prior experience, and the projects you execute. You can understand the basics during an initial period of regular study, but reaching an advanced level requires continuous practice and ongoing learning.

Can I build an AI tool myself?

Yes, you can start with a simple tool based on a ready-made model or a small model you train yourself. Over time, you will learn how to develop your own models and connect them to websites and applications.

Will AI take my job?

No one can predict the future with certainty, but it is clear that AI is changing the way many tasks are executed rather than completely eliminating jobs. People who learn to use it and understand how it works have a clear advantage because they can complete their work faster and build new solutions. Therefore, learning AI now is an investment in your skills, not a threat to them.

Do I need to learn English to learn artificial intelligence?

It's not strictly mandatory to be fluent, but English will benefit you immensely because most official documentation for libraries, tools, and research is written in English. Start by learning basic technical terms, and you'll find your level improving naturally with reading and practice. You can also use a smart assistant to translate difficult paragraphs for you.

I don't have much time, what should I start with?

Start with very small steps: 20 to 30 minutes a day. One day you read about a new concept, another day you write simple code, and another day you review what you learned. Long-term consistency will take you much further than a massive effort that stops after two weeks.

How do I make sure what AI says is correct?

AI tools can make mistakes or present information that looks convincing but is inaccurate. Therefore, review important information from trusted sources, test the code yourself before relying on it, and compare answers across multiple sources if the topic is sensitive. This is precisely one of the reasons you should learn the basics: so you can judge the output yourself.

6. Lesson Summary: Key AI Basics for Beginners

Before we wrap up, let's review together the most important points we learned in the lesson so they stick in your mind and form a strong foundation in your journey of learning AI from scratch:

Meaning of Artificial Intelligence (AI): A field aimed at developing systems capable of performing tasks that typically require human capabilities, such as speech understanding, image recognition, and prediction.
Learning Method: Instead of writing fixed rules for every situation, we train the model on lots of data, and it compares its predictions with correct answers and adjusts its settings to reduce errors.
System Components: Data, algorithm, model, training, and evaluation.
Usage vs. Building: Using ready-made tools is different from building them, and building has levels starting from using a ready-made tool up to training a model from scratch.
Types of Machine Learning: Supervised learning, unsupervised learning, and reinforcement learning, with supervised learning being the most suitable for beginners.
Essential Tools: Python, Google Colab, NumPy, Pandas, and Scikit-learn, followed by PyTorch and TensorFlow when you start learning deep learning.
Roadmap: Understand basics, then Python, then data handling, then machine learning, then deep learning, followed by executing real projects and specializing.
Secret to Persistence: Simple daily study and practical application after each phase are more important than long lessons taken once a week.

What to Do After This Lesson?

To truly benefit from the lesson, try these simple practical steps today:

Review the terminology table in the lesson and try to explain each term in your own words without looking.
Open Google Colab and try out the first model code from the lesson, observing the output it prints.

Write a short paragraph in your own style explaining the difference between using AI and building AI.
Determine where you stand on the roadmap, and choose a fixed time every day for learning.

7. Conclusion: What Will You Learn in Lesson 2?

We have reached the end of the first lesson in the series on learning how to build AI from scratch on Information Net. We explored the meaning of AI, how it learns from data, the difference between using and developing ready-made tools, and why Python is one of the best languages to start with.

However, this is just the beginning because the important next step is to start writing real code and understanding how to use programming in executing AI projects.

In the second lesson, we will continue the journey of learning AI for beginners by covering Python basics—starting from installing the language and setting up the work environment, followed by variables, data types, conditions, loops, and functions, complete with practical examples you can try yourself.

Our goal is that you do not settle for just using AI tools, but begin to understand how they are made and how you can develop your own tools and projects.

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