Artificial Intelligence Learning Roadmap [AI Roadmap] 2025- Step-by-Step Guide

Do you want to learn Artificial Intelligence and looking for an Artificial Intelligence Learning Roadmap [AI Roadmap]?… If yes, this article is for you. In this article, you will find a step-by-step Roadmap to learn Artificial Intelligence for 2025.

So, without further ado, let’s start the Artificial Intelligence Learning Roadmap [AI Roadmap].

Artificial Intelligence Learning Roadmap 2025/AI Engineer Roadmap For Beginners

Artificial Intelligence is getting popular nowadays. We can see the evolution of AI in every field such as Self-Driven Cars, Robotics, Product Recommendation, Google Assistant/Siri/Alexa, etc.

To become an AI Engineer, you must have the following skills-

1. Mathematics
2. Programming
3. Big Data
4. Data Science
5. Machine Learning
6. Deep Learning
7. Generative AI/ Large Language Models (LLMs)
8. Natural Language Processing
9. Business Intelligence

Now, let’s move to the Artificial Intelligence Learning Roadmap 2025. I have updated this Artificial Intelligence Learning Roadmap because there are lots of new terms coming in the field of AI in 2025. That’s why, you have to be updated with these new terms such as Large Language Models, Generative AI, and Retrieval Augmented Generation (RAG).

Now, I would like to mention a complete step-by-step AI Project, so that you can start working on your first AI Project-

Step-by-Step AI Project

import pandas as pd
from sklearn.datasets import load_files

data = load_files('aclImdb/train')
reviews, labels = data.data, data.target

from sklearn.feature_extraction.text import CountVectorizer
from sklearn.model_selection import train_test_split
from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import make_pipeline
from sklearn.metrics import accuracy_score

# Clean reviews
reviews = [review.lower().replace(b'<br />', b' ') for review in reviews]

# Split data
X_train, X_test, y_train, y_test = train_test_split(reviews, labels, test_size=0.2, random_state=42)

# Create a model pipeline
model = make_pipeline(CountVectorizer(), MultinomialNB())

model.fit(X_train, y_train)

y_pred = model.predict(X_test)
accuracy = accuracy_score(y_test, y_pred)
print(f'Accuracy: {accuracy}')

from flask import Flask, request, jsonify
app = Flask(__name__)

@app.route('/predict', methods=['POST'])
def predict():
    review = request.json['review']
    prediction = model.predict([review])
    return jsonify({'sentiment': 'positive' if prediction[0] == 1 else 'negative'})

if __name__ == '__main__':
    app.run(debug=True)

This project helps you understand how to start and complete a basic AI project. As you gain experience, you can explore more advanced techniques and projects. Congratulations, it’s your first step toward AI.

Simplified Time-Framed AI Roadmap/ Artificial Intelligence Learning Roadmap 2025

What does an AI Engineer do daily?

1. Understanding Problems: An AI engineer starts their day by trying to understand what sorts of problems AI can help solve.
2. Data Collection and Cleanup: They gather data from various sources and ensure it’s clean and ready to use in their AI models.
3. Creating AI Models: AI engineers build AI models, which are like special tools that can help with these problems.
4. Testing Models: They check how well these AI tools work and make improvements if needed.
5. Writing Instructions: They write instructions in the form of computer code to make sure their AI models understand what to do.
6. Staying Updated: They continue learning about the latest AI technology and techniques to stay on top of their game.
7. Teamwork: AI engineers work with others on AI projects and share their progress and ideas.
8. Fixing Issues: If anything goes wrong during their work, like computer problems, they figure out how to fix them.
9. Keeping Records: They write down everything they do and how it all works for future reference.
10. Meetings and Communication: They participate in meetings to discuss how their projects are going and plans for the future.
11. Project Management: They ensure their AI projects stay on track, meeting deadlines and goals.
12. Thinking Ethically: AI engineers consider what’s right and wrong in using AI and ensure their work follows the rules and is ethically sound.
13. Continuous Learning: They keep learning new skills and stay updated with the latest advancements in AI.
14. Putting AI to Use: They make sure the AI models they’ve created are working correctly in real-life situations.
15. Quality Checks: They ensure the results produced by AI are accurate and trustworthy through careful testing.

Being an AI engineer requires flexibility, good problem-solving skills, and proficiency in computer programming to succeed in the exciting field of AI.

Now, let’s understand the biggest confusion many AI Enthusiasts have which is How AI, ML, Deep Learning, Generative AI, LLMs, and RAG are connected. So, let’s understand the relationship between AI, ML, Deep Learning, Generative AI, LLMs, and RAG.

How AI, ML, Deep Learning, Generative AI, LLMs, and RAG are Connected?

As the name suggests, “Artificial Intelligence“, What do you understand? It means intelligence that is artificial. Right?.

Let’s break it into more detail.

What do you understand by “Artificial“? In my opinion, Artificial means, that is something not belong to humans.

And What do you understand by “Intelligence”? According to me, Intelligence means the ability to think, learn, and understand.

Yes, Artificial Intelligence makes machines as intelligent as humans. The main objective of Artificial Intelligence is to make machines powerful and thoughtful just like humans.

Artificial Intelligence is a broad area of Computer Science. AI allows machines to mimic the human.

Artificial Intelligence makes machines so powerful that machines can make decisions by themselves. AI gives the machine the power of common sense, reasoning skills, and decision-making skills.

Within this domain, Machine Learning (ML) is a technique that allows machines to learn and improves their performance by itself. In machine learning, some set of instructions are given in the form of training the model. On the basis of training data, the machine learning model learns and predicts the outcome.

Deep learning is an advanced form of Machine Learning. If you have a small dataset and you want to make a model, then machine learning works perfectly. But if you have a large dataset and many features present in your dataset then machine learning algorithms fail to perform.

Here, deep learning is used. Deep learning works perfectly fine with large datasets and with lots of features. Deep learning works on artificial neural networks, which are the same as the human brain, where neurons are connected. There are three layers, input layer, hidden layer, and output layer.

Generative AI refers to AI systems capable of producing new content, like images, music, or text, based on patterns learned from existing data.

Large Language Models (LLMs) are sophisticated AI models adept at understanding and generating human language, enabling tasks like translation, summarization, and question-answering.

Retrieval-augmented generation (RAG) is an approach that combines generative AI with retrieving relevant information from a large database to enhance the quality and relevance of generated content.

In essence, AI encompasses various techniques like ML, deep learning, generative AI, LLMs, and RAG, each contributing to the advancement of intelligent systems in different ways.

I hope now your confusion is clear.

Now it’s time to wrap up this Artificial Intelligence Learning Roadmap [AI Roadmap] 2025!

Conclusion

In this article, I have discussed a step-by-step Artificial Intelligence Learning Roadmap [AI Roadmap] 2025. If you have any doubts or queries regarding Artificial Intelligence Learning Roadmap, feel free to ask me in the comment section. I am here to help you.

All the Best for your Career!

Happy Learning!

FAQ on Artificial Intelligence Learning Roadmap

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Though of the Day…

‘ It’s what you learn after you know it all that counts.’

– John Wooden

Written By Aqsa Zafar

Founder of MLTUT, Machine Learning Ph.D. scholar at Dayananda Sagar University. Research on social media depression detection. Create tutorials on ML and data science for diverse applications. Passionate about sharing knowledge through website and social media.