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Beginner 8–12 weeks 5 Stages

Your First Steps into Artificial Intelligence

You don't need a PhD to understand AI. This roadmap takes you from zero knowledge to confidently understanding how AI systems work, the key terminology, and the ability to build your first machine learning project. Designed for complete beginners — no prior programming or math background required.

Who This Is For
Complete beginners with no prior AI or programming experience
Time Commitment
8–12 weeks
Difficulty
Beginner
Stages
5 stages, 15 resources

Prerequisites

Basic computer literacy
High school mathematics (algebra, basic statistics)
Curiosity and willingness to learn

The Roadmap

1

Understand What AI Actually Is

1–2 weeks

Before touching any code, build a solid mental model of what AI is (and isn't). Understand the difference between AI, Machine Learning, and Deep Learning. Learn why AI is transforming industries and what it can and cannot do today. This conceptual foundation will make everything else click faster.

What is Artificial Intelligence — history and definitions
AI vs Machine Learning vs Deep Learning — the hierarchy
Narrow AI vs AGI — what exists today
Supervised, Unsupervised, and Reinforcement Learning — the three paradigms
Real-world AI applications — from recommendation engines to self-driving cars
AI ethics basics — bias, fairness, and societal impact
Free Course Elements of AI (University of Helsinki) Free Course AI for Everyone (Andrew Ng, Coursera) Reference Viqus AI Glossary — Fundamentals
Artificial Intelligence (AI) Narrow AI (Weak AI) Artificial General Intelligence (AGI) Machine Learning (ML) Deep Learning (DL) Supervised Learning Unsupervised Learning Reinforcement Learning (RL)
2

Learn Python Essentials for AI

2–3 weeks

Python is the language of AI. You don't need to become a software engineer — but you need enough Python to manipulate data, use libraries, and run ML models. Focus on practical skills: variables, loops, functions, data structures, and the core data science libraries (NumPy, Pandas, Matplotlib).

Python syntax — variables, data types, loops, conditionals
Functions, modules, and error handling
NumPy — numerical computing and arrays
Pandas — data manipulation and analysis
Matplotlib/Seaborn — data visualization
Jupyter Notebooks — interactive coding environment
Free Course Python for Everybody (Dr. Chuck) Free Interactive Kaggle Python Micro-Course Free Tool Google Colab
Data Science Algorithm
3

Mathematics for Machine Learning

2–3 weeks

You don't need to master abstract math — but you do need to understand the intuition behind the key mathematical concepts that power ML. Focus on linear algebra (vectors, matrices), basic calculus (derivatives, gradients), probability and statistics (distributions, Bayes' theorem), and how they connect to training ML models.

Linear algebra intuition — vectors, matrices, dot products
Calculus basics — derivatives, partial derivatives, gradients
Probability and statistics — distributions, mean, variance, Bayes' theorem
Cost functions and optimization — how models learn
Gradient descent — the engine of machine learning
Free Video Series 3Blue1Brown: Essence of Linear Algebra Free YouTube StatQuest with Josh Starmer Free Course Khan Academy: Statistics & Probability
Gradient Descent Learning Rate Regularization
4

Your First Machine Learning Models

2–3 weeks

Now you bring it all together. Learn to train, evaluate, and improve ML models using scikit-learn. Start with simple models (linear regression, decision trees) and work up to understanding model evaluation, overfitting, and the ML workflow. Build a complete project from data loading to predictions.

The ML workflow — data → features → train → evaluate → deploy
Linear Regression and Logistic Regression
Decision Trees and Random Forests
Model evaluation — accuracy, precision, recall, cross-validation
Overfitting and underfitting — why they happen and how to fix them
Hands-on project — predict house prices or classify iris species
Free Interactive Kaggle Intro to Machine Learning Free Course Google Machine Learning Crash Course Reference scikit-learn Documentation
Regression Classification Decision Tree & Random Forest Cross-Validation Precision & Recall Feature Engineering Supervised Learning
5

Explore What's Next

1–2 weeks

You now have a solid AI foundation. In this final stage, explore the landscape of AI specializations to decide your next step. Try a deep learning tutorial, experiment with a pre-trained LLM, explore computer vision, or dive deeper into data science. The goal is discovering which area excites you most — that's your next roadmap.

Deep Learning introduction — neural networks and frameworks
Generative AI and LLMs — what they are and how to use them
Computer Vision basics — image classification with CNNs
NLP fundamentals — text processing and sentiment analysis
AI Ethics — bias, fairness, and responsible development
Career paths in AI — which roles match your interests
Free Course Fast.ai: Practical Deep Learning for Coders Free Course Hugging Face NLP Course Reference Viqus AI Use Cases
Deep Learning (DL) Artificial Neural Network (ANN) Generative AI Large Language Model (LLM) Computer Vision Natural Language Processing (NLP) Convolutional Neural Network (CNN) AI Ethics

Tools & Technologies

Python
Jupyter Notebooks
Google Colab
scikit-learn
NumPy & Pandas
Matplotlib

Career Outcomes

AI-literate professional in any industry
Foundation for specialized AI/ML career paths
Ability to evaluate AI tools and solutions
Qualified for entry-level data analyst or AI operations roles

Frequently Asked Questions

Can I learn AI without a computer science degree?

Absolutely. Most successful AI practitioners are self-taught or career-changers. What matters is dedication, consistent practice, and following a structured learning path. This roadmap is designed specifically for people without technical backgrounds.

How long does it take to learn AI basics?

With consistent effort (10-15 hours per week), you can build a solid AI foundation in 8-12 weeks. You'll understand key concepts, be able to read AI news critically, and build simple ML models. Becoming job-ready in an AI role typically takes 6-12 months of continued learning.

Do I need to be good at math to learn AI?

You need to understand mathematical concepts at an intuitive level, but you don't need to be a math expert. Focus on understanding what gradient descent does rather than deriving it from scratch. Libraries like scikit-learn handle the math computationally — your job is understanding what the math means.

What programming language should I learn first for AI?

Python, without question. It's the dominant language in AI/ML with the richest ecosystem of libraries (scikit-learn, TensorFlow, PyTorch, Hugging Face). It's also one of the easiest languages to learn for beginners. Start with Python and you won't need another language for years.