learning_paths.json

{
  "learning_paths": [
    {
      "name": "Probability & Statistics",
      "topics": [
        {"name": "Events and probabilities"},
        {"name": "Conditional probability & Bayes’ theorem"},
        {"name": "Distributions (normal, binomial, Poisson)"},
        {"name": "Confidence intervals & hypothesis testing"},
        {"name": "Expectation, variance, correlation"}
      ]
    },
    {
      "name": "Machine Learning",
      "topics": [
        {"name": "Linear and logistic regression"},
        {"name": "Classification vs regression"},
        {"name": "Clustering & PCA"},
        {"name": "Overfitting, underfitting, cross-validation"},
        {"name": "Feature engineering"}
      ]
    },
    {
      "name": "Markets / Trading / Asset Types / Derivatives",
      "topics": [
        {"name": "Stocks, bonds, commodities, crypto"},
        {"name": "Options, futures, swaps"},
        {"name": "Market microstructure & liquidity"},
        {"name": "Risk metrics (VaR, drawdowns)"},
        {"name": "Portfolio allocation basics"}
      ]
    },
    {
      "name": "Quant Trading",
      "topics": [
        {"name": "Cointegration & mean reversion"},
        {"name": "Hidden Markov Models for regime detection"},
        {"name": "Statistical arbitrage"},
        {"name": "Backtesting frameworks"},
        {"name": "Integrating ML with trading strategies"}
      ]
    }
  ]
}