Udemy – Complete Python and Machine Learning in Financial Analysis 2025-3

Udemy – Complete Python and Machine Learning in Financial Analysis 2025-3 Downloadly IRSpace

Aug 17, 2025 - 02:23 Updated: Aug 19, 2025 - 14:19

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Udemy – Complete Python and Machine Learning in Financial Analysis 2025-3

Complete Python and Machine Learning in Financial Analysis Course. This course teaches advanced financial analysis and machine learning algorithms with Python so that participants can perform specialized analyses in financial markets. In this course, topics such as technical and fundamental analysis, analytical tools, and the Python programming environment are comprehensively covered. Deep learning algorithms and artificial neural networks are also taught to enhance individuals’ analytical abilities. The course begins with methods for collecting and processing financial data, and then examines the statistical properties of asset prices and technical analysis indicators such as Bollinger Bands, MACD, and RSI. It then covers time series analysis models such as ARIMA and GARCH, factor models such as CAPM and Fama-French, and portfolio optimization methods with Monte Carlo simulation. The final part of the course will involve the implementation of a practical project in the financial domain, which involves credit card fraud detection using advanced models such as Random Forest, XGBoost, and LightGBM. It will also examine hyperparameter tuning and solving the problem of data imbalance, and finally, the application of deep learning (PyTorch) in solving financial problems will be demonstrated.

What you will learn

Use the provided functions to download financial data from multiple sources and preprocess it for further analysis.
Gain insight into patterns from a set of the most commonly used metrics (such as MACD and RSI).
Introduction to the basics of time series modeling; then, exponential smoothing methods and ARIMA class models are reviewed.
Shows how to estimate different factor models in Python; one, three, four, and five factor models.
It introduces the concepts of volatility forecasting using (G)ARCH class models, how to choose the best model, and how to interpret the results.
Introduces the concept of Monte Carlo simulations and uses them to simulate stock prices, value European/American options, and calculate VaR.
Introduces modern portfolio theory and shows how to derive the Efficient Frontier in Python and evaluate the performance of such portfolios.
Provides a use case for machine learning to predict credit defaults. You will learn how to tune model hyperparameters and manage imbalances.
It introduces a set of advanced classifiers (including stacking multiple models) and how to deal with class imbalance, using Bayesian optimization.
Shows how to use deep learning techniques to work with time series and tabular data. The networks are trained using PyTorch.