AI/ML Roadmap

Complete AI/ML Roadmap

Complete AI/ML Learning Roadmap

1. Mathematics Foundations (2-4 Weeks)

Linear Algebra

  • Vectors & Matrices
  • Eigenvalues & SVD
  • Matrix Calculus

Probability

  • Bayesian Inference
  • Distributions
  • MLE

2. AI Foundation (3-4 Weeks)

Core Concepts

  • Search Algorithms (A*)
  • Knowledge Representation
  • Agent Architectures

Theory

  • Turing Test
  • Moravec's Paradox
  • Ethical Principles

3. Programming & Data Skills (3-4 Weeks)

  • Python: NumPy/Pandas
  • Data Visualization
  • SQL & Git
  • APIs & Web Scraping

4. Machine Learning Core (6-8 Weeks)

Supervised Learning

  • Regression/Classification
  • SVMs
  • Ensemble Methods

Unsupervised

  • Clustering
  • Dimensionality Reduction
  • Anomaly Detection

5. Deep Learning (6-8 Weeks)

  • Neural Networks
  • CNNs/RNNs
  • Transformers
  • PyTorch/TensorFlow

6. Ethics & Deployment (2-3 Weeks)

Ethics

  • Bias Detection
  • Explainable AI

Deployment

  • APIs
  • Docker
  • Cloud Platforms

7. Projects & Portfolio (Ongoing)

Milestones

  • Kaggle Competitions
  • Research Reproduction
  • Open Source Contributions

When to Revise Foundations

  • Linear Algebra: During PCA/SVD implementation
  • Calculus: When understanding backpropagation
  • Probability: Working with Bayesian models

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