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Oleg Dubovoi
Oleg Dubovoi

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How to Become an AI Developer in 2025 (Full Guide + Resources)

Greetings Dev.to community!

Artificial Intelligence is everywhere these days. From chatbots to self-driving cars, AI powers some of the coolest technologies we see today. If you’ve ever wondered how to break into this exciting field, you’re in the right place. In this guide, I’ll explain how you can start your journey to becoming an AI developer.

If you like the article - please like and follow for more content! ❤️


1. Learn Programming

ln

You need to choose a programming language and learn the basics of it.

  • Python: It’s easy to read and write, even for beginners. (Recommended)
  • Java: Useful for AI in enterprise settings and large-scale systems.
  • C++: Often used in performance-critical AI applications like gaming and robotics.
  • R: If you’re into data analysis and statistics.

Step-by-step language learning plan:

Author Recommendation 💡

Don't rush into learning programming. Learn the theory step by step and reinforce it with practice. Write a few pet projects to be sure of your knowledge.

Top 50 Software Development Project Ideas [Beginners]


2. Master Math and Statistics

math

Math and statistics are very important for AI developers because they help to understand how AI works. Math is needed to create and improve models, making them work better and faster. Statistics helps to study data, find patterns, and make predictions.

Linear Algebra

Learn about vectors, matrices, and matrix operations. These are the building blocks of neural networks. For example, weights in a neural network are represented as matrices.

Resources:

Probability and Statistics

These are essential for understanding how AI models make predictions and handle uncertainty. You’ll use concepts like:

  • Probability distributions.
  • Bayes’ theorem.
  • Hypothesis testing.

Resources:

Calculus

While not every AI developer uses calculus daily, it’s essential for understanding how models like neural networks learn through optimization (gradient descent). Focus on:

  • Derivatives
  • Partial derivatives
  • Chain rule

Resources:

Author Recommendation 💡

AI is built on a foundation of mathematics, but don’t let that scare you! You don't need to know all the math to get started with AI. Step by step, you will gradually improve your skills.

Check out this excellent YouTube course: Mathematics for Machine Learning Tutorial


3. Study Machine Learning Basics

Machine learning (ML) is a branch of AI focused on enabling computers and machines to imitate the way that humans learn, to perform tasks autonomously, and to improve their performance and accuracy through experience and exposure to more data.

Types of Machine Learning

mltypes

Machine learning involves showing a large volume of data to a machine so that it can learn and make predictions, find patterns, or classify data. The three machine learning types are supervised, unsupervised, and reinforcement learning.

  • Supervised Learning: When the model learns from labeled data (e.g., predicting house prices).
  • Unsupervised Learning: When the model finds patterns in unlabeled data (e.g., customer segmentation).
  • Reinforcement Learning: When the model learns by trial and error (e.g., training a robot to walk).

Resources:

Common Algorithms

alg

Understanding the fundamentals of key algorithms is essential for anyone entering the field of machine learning. Below are some of the foundational algorithms that form the basis for solving various machine learning problems:

  • Linear Regression: Predicts continuous values using linear relationships.
  • Decision Trees: Splits data into decision-based groups.
  • Support Vector Machines (SVMs): Classifies data by maximizing margins.
  • K-Nearest Neighbors (KNN): Predicts using closest data points.

Resources:

Author Recommendation 💡

I recommend you check out two books from Andriy Burkov - The Hundred-Page Machine Learning Book and Machine Learning Engineering.


4. Dive into AI Frameworks and Tools

To build AI systems, you’ll need to get comfortable with popular AI frameworks and tools. These tools simplify the process of building, training, and deploying machine learning models.

TensorFlow

tensor

Language: Primarily used with Python, other supported languages include C++, JavaScript (via TensorFlow.js), Java, Go, and Swift for specific applications.
Complexity: High
Site: tensorflow

TensorFlow is an open-source deep learning framework developed by Google. It is widely used for building and deploying machine learning and deep learning models, especially at a production level. TensorFlow offers flexibility, scalability, and a comprehensive ecosystem for end-to-end machine learning workflows.

Resources:

PyTorch

pytorch

Language: Python, has limited support for C++
Complexity: Moderate
Site: pytorch

PyTorch, developed by Facebook, is another open-source deep learning framework. It is highly favored by researchers and academics due to its flexibility and dynamic computation graph, which makes it easier to experiment and debug.

Resources:

Keras

keras

Language: Python
Complexity: Low
Site: keras

Keras is a high-level neural network API designed for fast prototyping and ease of use. It runs on top of TensorFlow and simplifies the process of building, training, and deploying neural networks. Keras is ideal for beginners and those who want to quickly implement deep learning models.

Resources:

Scikit-learn

Scikit

Language: Python
Complexity: Low
Site: scikit-learn

Scikit-learn is a powerful library for classical machine learning. It provides tools for data preprocessing, classification, regression, clustering, dimensionality reduction, and model evaluation. Scikit-learn is perfect for beginners and professionals working on traditional machine learning problems.

Resources:


5. Get Comfortable with Data

data

Data Preprocessing

Before feeding data into an AI model, it’s crucial to clean and prepare it for analysis. Data in its raw form often contains inconsistencies, missing values, or noise. Preprocessing ensures the dataset is clean, structured, and ready for use.

  • Handling missing values.
  • Scaling and normalizing data.
  • Splitting data into training and testing sets.

Resources:

Exploratory Data Analysis (EDA)

EDA helps you understand the structure, patterns, and relationships within your data, which can guide your model-building process.

  • Using Pandas: Pandas is a powerful Python library for data manipulation and analysis. Use it to calculate statistics, filter data, and handle large datasets efficiently.
  • Data Visualization: Visualizing data helps uncover patterns, outliers, and relationships between variables. Libraries like Matplotlib and Seaborn allow you to create histograms, scatter plots, box plots, and heatmaps.
  • Uncovering Patterns: Through visualizations and statistical analysis, identify trends (e.g., seasonality in sales data) or correlations (e.g., a positive relationship between study time and grades). These insights often guide feature engineering and model selection.

Resources:

Big Data Tools

When working with massive datasets that exceed the capacity of traditional tools, it’s essential to leverage Big Data frameworks.

  • Apache Spark: Spark is a distributed computing system designed for processing large-scale datasets. It supports machine learning, data streaming, and batch processing, making it a versatile choice for AI projects.
  • Hadoop: Hadoop provides a framework for distributed storage and processing of big data using the MapReduce programming model. While it is less commonly used for machine learning today, it remains a strong choice for foundational data storage.

These tools are essential for applications involving web-scale data, such as social media analysis, recommendation systems, or fraud detection, where datasets can range from terabytes to petabytes.

Resources:


Additional AI / ML Developer Resources 💡

AI and Data Scientist Roadmap
The best books on artificial intelligence (AI)
IT Job Market in 2025: Trends, Roles, and Opportunities
AI in Your Hands: Nvidia’s $3,000 Supercomputer Changes Everything


Salary

salary


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Top comments (22)

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empiree profile image
Oleg Dubovoi
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j19z profile image
J19Z

So... Forget other programing languages just go with Python 😏🐍

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anand_prajapati_e1fc81f74 profile image
Anand Prajapati

Right bro..

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empiree profile image
Oleg Dubovoi

Share your thoughts! If you want to add something to the article - write in the comments.

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marcusobrien profile image
Marcus O'Brien

Is Python really used in production AI systems though ? I understand it's good for fast proof of concepts or experimenting, but when it comes to scaling up to training large systems or live inference etc is it used ? For example processing data in parallel with pythons restrictions on threading compared to c++ or rust or c# etc. or controlling resources if Garbage collection is used in java c# Python etc. I've only seen Python used for small projects and c++ used for production systems. Curious about other people experiences

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aliasfoxkde profile image
Micheal Kinney

For ML yes and many build "production" Web applications, which I believe is the wrong application to use Python for... But look at vLLM for Python as an example, it's the most performant implementation that can run an LLM and that's using Python. And I think what most people write in Python is show because the way they write it.

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supanext profile image
SupaNext

Nice!

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empiree profile image
Oleg Dubovoi

Thanks!

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vandana_kannan_dfd8246508 profile image
Vandana Kannan

Amazing resources! Thank you for this article 😊

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empiree profile image
Oleg Dubovoi

Thank you!

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adams_kaninah_fdcf1cf93f5 profile image
Adams Kaninah

Python language seems to be an all in one solution

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arnatech profile image
Arif Dzikrullah

For starters yes.

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luvai profile image
Chan

How many years would this take me to finish as a beginner?

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aliasfoxkde profile image
Micheal Kinney • Edited

It's say ~1 year to be confident if you spent just 15-30 minutes per day on completing challenges, like those through CodeWars, LeetCode, etc. though this is assuming you take it seriously, review other solutions, improving the quality of your code over time, refactor, and etc. This will remove the "daunting" feeling that code gives beginners and it will become second nature. And once you are confident in one, try the same challenge in another language.

And you should utilize w3schools, rather than most people just "Googling" one-off answers or examples, sign up to keep track of progress, then click on HTML to start, click next (do not jump ahead, if each new concept is easy then just do it)... It will step you though the fundamentals of EVERYTHING needed for Computer Science and DevOps, in the order you should learn them in.

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tian_wijaya profile image
tian

4years

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ameerkulal profile image
amirkhalilzain • Edited

For 4 years, how many hours per day should be provided?

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luvai profile image
Chan

Lol

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aaronsaikovski profile image
Aaron Saikovski

A couple of points. I would add C# and Semantic Kernel or even AutoGen. Maybe even PydanticAI. Also add some Agentic AI

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jf_wang_ecda52b7208abcd84 profile image
jf wang

Hello, I hope to translate this article into Chinese and put it on a Chinese website. If there's any reward, I'll give you half of it. Hope to get your reply.

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empiree profile image
Oleg Dubovoi

Hello. Popularizing this content would be great! I would appreciate it if you could leave a link to the original article, and my social links (Github, LinkedIn) Thanks!

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