Evangelos Bolofis | The AI Intersection

The Educational Roadmap

How to Learn AI in 2025: A Step-by-Step Roadmap for Absolute Beginners

By Evangelos Bolofis, AI Expert at Cognizant

So, you want to learn AI. You see the headlines, you hear about the groundbreaking innovations, and you want in. But where on Earth do you start? The field is massive, intimidating, and full of complex jargon.

Relax. You don’t need a Ph.D. in advanced mathematics to get started. What you need is a plan. This is the official ebolofis.ai roadmap to learning AI in 2025, designed for beginners who are serious about building real, marketable skills.

Step 1: Build Your Mental Model (1 Week)

Before you write a single line of code, understand the core concepts.

  • Action: Read our foundational guide, “What is AI? A No-Nonsense Guide for 2025.”
  • Action: Watch a high-level documentary like “AlphaGo” on YouTube to see Machine Learning in a competitive context.
  • Goal: Be able to confidently explain the difference between AI, Machine Learning, and Deep Learning to a friend.

Step 2: Learn Python – The Language of AI (4-6 Weeks)

The vast majority of AI and data science work is done in Python. It’s clean, readable, and has incredible open-source libraries that do the heavy lifting for you.

  • Resource: “Python for Everybody” by Dr. Charles Severance (available for free on platforms like Coursera and freeCodeCamp). It is widely regarded as the best starting point for non-programmers.
  • Goal: Get comfortable with the fundamentals: variables, loops, functions, and basic data structures. Competence, not mastery, is the goal here.

Step 3: Master the Core ML Libraries (6-8 Weeks)

This is where the magic happens. You’ll learn the key Python libraries that turn your code into powerful machine-learning models.

  • NumPy & Pandas: For manipulating and analyzing data (think of them as Excel on steroids).
  • Matplotlib & Seaborn: For creating charts and visualizations to understand your data.
  • Scikit-Learn: The most important one. It provides easy-to-use tools for building classic machine learning models for tasks like prediction and classification.
  • Resource: The “IBM Data Science Professional Certificate” on Coursera or “Machine Learning A-Z” on Udemy are excellent, project-based ways to learn these tools.

Step 4: Build Your First Projects (Ongoing)

Theory is useless without practice. Projects are what build your skills and your portfolio.

  • Project 1 (Beginner): Titanic Survival Prediction on the website Kaggle. It’s the “Hello, World!” of machine learning, a perfect first challenge.
  • Project 2 (Intermediate): House Price Prediction. Use a dataset of home features (square footage, bedrooms) to predict sale prices.
  • Project 3 (Advanced): Build a simple movie recommendation engine using a dataset of user ratings.
  • Goal: Have at least three solid projects on your GitHub profile that you can discuss and explain.

Step 5: Specialize and Go Deeper (Your Career)

Once you have the fundamentals, you can choose a specialty that excites you.

  • Deep Learning & Computer Vision: Working with images and video (using frameworks like TensorFlow or PyTorch).
  • Natural Language Processing (NLP): Working with text and language (using libraries like Hugging Face Transformers).
  • Data Science & Analytics: Focusing on extracting business insights from data.

The ebolofis.ai Promise: Learning AI is a marathon, not a sprint. It takes dedication, but it has never been more accessible. Follow this roadmap, stay curious, and keep building. You will be amazed at how far you can go.