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Full 1-Year AI Learning Roadmap

Here’s a full year-wise (12-month) detailed plan to master Artificial Intelligence from scratch — even if you are starting with just basic coding knowledge.

📚 Full 1-Year AI Learning Roadmap

Month 1-2: Foundations (Mathematics + Programming)

  • Mathematics for AI:

    • Linear Algebra: Vectors, Matrices, Matrix multiplication

    • Calculus: Derivatives, Chain Rule, Partial Derivatives

    • Probability and Statistics: Bayes' Theorem, Mean, Variance, Standard Deviation

    • Optimization Basics: Gradient Descent

  • Programming:

    • Master Python (focus on data structures, loops, functions, OOP)

    • Learn libraries: NumPy, Pandas, Matplotlib

✅ Mini project:

  • Matrix calculator

  • Simple data visualizations (bar, scatter plots)

Month 3-4: Machine Learning (ML) Basics

  • Core ML Concepts:

    • Supervised Learning (Linear Regression, Logistic Regression)

    • Unsupervised Learning (K-Means, PCA)

    • Model Evaluation (Accuracy, Precision, Recall, F1 Score)

  • Important Algorithms:

    • Decision Trees

    • Random Forest

    • K-Nearest Neighbors

    • Support Vector Machines

  • Tools:

    • Scikit-learn

    • Jupyter Notebook

✅ Mini-project:

  • Predict house prices

  • Customer segmentation using K-Means

Month 5-6: Deep Learning Foundations

  • Neural Networks:

    • Perceptron

    • Feed-forward Neural Networks

    • Backpropagation

  • Advanced Deep Learning:

    • Convolutional Neural Networks (CNNs)

    • Activation Functions (ReLU, Softmax)

  • Frameworks:

    • TensorFlow or PyTorch

✅ Mini-project:

  • Build a digit recognizer using MNIST dataset

Month 7: Natural Language Processing (NLP)

  • Topics:

    • Text Preprocessing (Tokenization, Stemming, Lemmatization)

    • Word Embeddings (Word2Vec, GloVe)

    • Transformers Introduction (BERT basics)

  • Libraries:

    • NLTK

    • Hugging Face Transformers

✅ Mini project:

  • Sentiment analysis of movie reviews

Month 8: Computer Vision

  • Topics:

    • Image Classification

    • Object Detection (YOLO basics)

    • Image Augmentation

  • Frameworks:

    • OpenCV

    • TensorFlow/PyTorch (for vision models)

✅ Mini project:

  • Build a dog vs cat image classifier

Month 9: Advanced Topics

  • Reinforcement Learning:

    • Q-Learning

    • Policy Gradients

  • Generative AI:

    • GANs (Generative Adversarial Networks)

    • Diffusion Models (intro)

  • Model Deployment Basics:

    • Flask API basics

    • Using Docker for deployment

✅ Mini project:

  • Build an AI that plays a simple game (like Tic-Tac-Toe)

Month 10: Real-world Applications + Big Data

  • MLOps Basics:

    • CI/CD in AI

    • Model Monitoring

    • Version Control for models (DVC)

  • Big Data:

    • Introduction to Apache Spark

    • Processing large datasets

✅ Mini project:

  • Train and deploy an ML model on cloud (AWS/GCP)

Month 11: Ethics, Responsible AI, and Research Skills

  • Topics:

    • AI Bias and Fairness

    • Interpretability and Explainability (XAI)

    • Privacy in AI

✅ Mini project:

  • Write a small research paper on AI fairness in hiring models

Month 12: Specialization and Portfolio Building

  • Choose one specialization:

    • Advanced NLP

    • Advanced Computer Vision

    • Robotics

    • Healthcare AI

    • Finance AI

  • **Work on a Capstone Project:

    • Build an End-to-End AI System

    • Deploy it publicly (GitHub, personal portfolio)

✅ Bonus:

  • Contribute to open-source AI projects

  • Participate in AI competitions (Kaggle, Driven Data)

🌟 Bonus Tips:

  • Follow AI news (ArXiv, Medium AI publications)

  • Listen to AI podcasts (Lex Fridman Podcast, Practical AI)

  • Join AI communities (Reddit r/Machine Learning, Kaggle forums)

  • Take mini courses (Coursera, edX, Fast.ai)

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