This guide is for absolute beginners with no prior experience in AI, ML, or data science. Whether you're a student, developer, or just curious, this roadmap will help you break into AI step-by-step with all prerequisites explained clearly.
Table of Contents
- What is AI/ML?
- Level 1: Core Foundation
- Learn Python
- Math for AI/ML
- Basic DSA
- Level 2: Data Skills
- Data Handling
- Libraries to Master
- Level 3: Machine Learning
- Concepts & Algorithms
- Mini Projects
- Level 4: Advanced AI
- Deep Learning
- NLP
- Computer Vision
- Reinforcement Learning (optional)
- Bonus Skills
- Real Project Ideas
- Final Tool Stack
- Tips to Learn Faster
1. What is AI/ML?
- Artificial Intelligence (AI): Making machines act and think like humans.
- Machine Learning (ML): A part of AI where machines learn from data to improve automatically.
2. Level 1: Core Foundation
Learn Python
Python is the most beginner-friendly and powerful language for AI/ML.
- Variables, data types
- If-else, loops
- Functions
- Lists, tuples, dictionaries
- File handling
- OOP basics
Tools: Google Colab, Jupyter Notebook
Resources: W3Schools, freeCodeCamp, Python.org
Math for AI/ML
You don’t need to be a math genius, but some basics are a must:
- Linear Algebra: Vectors, matrices, dot products
- Statistics & Probability: Mean, median, distributions, Bayes' Theorem
- Calculus: Derivatives and gradient descent
Resources: Khan Academy, 3Blue1Brown, StatQuest
Basic Data Structures & Algorithms (DSA)
- Arrays, Lists, Dictionaries
- Sorting & Searching (concepts)
- Time complexity (Big-O)
- Recursion (basic)
3. Level 2: Data Skills
Handling and Exploring Data
- What is data (structured/unstructured)
- Read/write CSV/JSON
- Data cleaning and preprocessing
- Feature selection
Libraries to Learn
-
NumPy– math with arrays -
Pandas– dataframes -
Matplotlib/Seaborn– data visualization
4. Level 3: Machine Learning
Concepts
- Supervised vs Unsupervised vs Reinforcement Learning
- Overfitting / Underfitting
- Train-test split
- Evaluation metrics (Accuracy, Precision, Recall, F1-score)
Popular Algorithms
- Linear/Logistic Regression
- Decision Trees & Random Forest
- K-Nearest Neighbors (KNN)
- K-Means Clustering
- Naive Bayes
- Support Vector Machines (SVM)
Mini Projects
- House Price Predictor
- Spam Email Classifier
- Customer Segmentation
- Student Mark Predictor
5. Level 4: Advanced AI
Deep Learning (Neural Networks)
- Layers: Input, Hidden, Output
- Activation Functions: ReLU, Sigmoid
- Backpropagation, Loss, Optimizers
- CNN (images), RNN/LSTM (text/time)
Libraries: TensorFlow, PyTorch, Keras
NLP – Natural Language Processing
- Tokenization, Lemmatization
- Bag of Words, TF-IDF
- Word2Vec, GloVe
- Transformers (BERT, GPT)
Libraries: NLTK, spaCy, Hugging Face Transformers
Computer Vision
- Image classification, Object detection
- CNNs
- OpenCV basics
Reinforcement Learning (Optional)
- Agents, Rewards, Actions
- Q-Learning
- Deep Q Networks (DQN)
6. Bonus Skills
- SQL & Databases: MySQL, PostgreSQL
- Git & GitHub: Version control
- Docker: Containerize ML apps
- Cloud: Google Cloud, AWS, Azure (for deployment)
7. Project Ideas
- Movie Recommendation System
- Chatbot with NLP
- AI Voice Assistant
- Image Classifier (Cats vs Dogs)
- Stock Price Predictor
8. Final Tool Stack
| Area | Tools |
|---|---|
| Language | Python |
| Data | Pandas, NumPy, Matplotlib |
| ML | Scikit-learn |
| DL | TensorFlow, PyTorch, Keras |
| NLP | NLTK, Hugging Face |
| CV | OpenCV |
| Deployment | Flask, FastAPI, Docker |
| Platform | Google Colab, Kaggle |
| Versioning | Git, GitHub |
9. Tips to Learn Faster
- Start simple. Build mini projects.
- Practice with Kaggle datasets.
- Join communities (Discord, Reddit, LinkedIn)
- Follow AI blogs (Google AI, OpenAI)
- Contribute to open-source AI projects
Want to become an AI hero? Start small. Stay consistent. Build projects.
You don't need a degree – just curiosity, effort, and the roadmap above.
Happy learning!
Top comments (0)