Complete Python and Machine Learning in Financial Analysis

Course Overview

• Course Title: Complete Python and Machine Learning in Financial Analysis
• Instructor: S. Emadedin Hashemi (AI Expert and Data Scientist)
• Target Audience:
  • Aspiring data scientists specializing in finance
  • Financial analysts seeking Python and machine learning skills
  • Quantitative analysts and traders interested in algorithmic trading
  • Intermediate Python programmers expanding into financial applications
  • Professionals aiming to apply AI/ML in finance, risk management, or portfolio optimization
• Prerequisites:
  • Basic Python programming (syntax, libraries like NumPy/Pandas)
  • Fundamental statistics (mean, variance, distributions, hypothesis testing)

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Curriculum Highlights

• Key Topics Covered:

  • Data Acquisition & Preprocessing
    • Downloading financial data from Yahoo Finance, Alpha Vantage, Quandl
    • Cleaning and structuring time-series financial data
  • Technical Analysis in Python
    • Bollinger Bands, MACD (Moving Average Convergence Divergence)
    • RSI (Relative Strength Index), backtesting trading strategies
  • Time Series Modeling
    • Exponential smoothing methods
    • ARIMA/SARIMA models for forecasting
    • GARCH/EGARCH for volatility modeling
  • Factor Models & Asset Pricing
    • CAPM (Capital Asset Pricing Model)
    • Fama-French 3-/5-factor models
    • Carhart 4-factor model
  • Monte Carlo Simulations
    • Stock price simulation
    • Option pricing (European/American)
    • Value at Risk (VaR) estimation
  • Portfolio Optimization
    • Modern Portfolio Theory (MPT)
    • Efficient Frontier calculation in Python
    • Portfolio performance evaluation (Sharpe ratio, Sortino ratio)
  • Machine Learning for Finance
    • Credit default prediction (imbalanced data handling)
    • Hyperparameter tuning (GridSearch, Bayesian optimization)
    • Advanced classifiers: Random Forest, XGBoost, LightGBM, stacked models
  • Deep Learning with PyTorch
    • Neural networks for time-series forecasting
    • Tabular data processing with deep learning

• Key Skills Learned:

  • Automate financial data collection and preprocessing in Python
  • Implement technical indicators and backtest trading strategies
  • Build and validate time-series models (ARIMA, GARCH)
  • Apply factor models for asset pricing and risk analysis
  • Perform Monte Carlo simulations for option pricing and risk management
  • Optimize portfolio allocation using MPT and Python
  • Develop ML models for credit risk and fraud detection
  • Tune hyperparameters using Bayesian optimization
  • Design deep learning models (PyTorch) for financial forecasting

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Course Format

• Duration: 20.5 hours on-demand video
• Format: Self-paced online course (lifetime access)
• Resources:
  • 19 supplementary articles
  • 18 downloadable resources (Jupyter notebooks, datasets, code templates)
  • Mobile and TV access
  • Certificate of completion

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Special Offer

• Limited Time Coupon Code: N/A