Complete Python and Machine Learning in Financial Analysis

Complete Python and Machine Learning in Financial Analysis
Last updated 3/2025
Duration: 20h 17m | .MP4 1920×1080, 30 fps(r) | AAC, 44100 Hz, 2ch | 6.13 GB

Using Python, Machine Learning, and Deep Learning in Financial Analysis with step-by-step coding (with all codes)

What you’ll learn
– You will be able to use the functions provided to download financial data from a number of sources and preprocess it for further analysis
– You will be able to draw some insights into patterns emerging from a selection of the most commonly used metrics (such as MACD and RSI)
– Introduces the basics of time series modeling. Then, we look at exponential smoothing methods and ARIMA class models.
– Introduces you to the concept of volatility forecasting using (G)ARCH class models, how to choose the best-fitting model, and how to interpret your results.
– Introduces the Modern Portfolio Theory and shows you how to obtain the Efficient Frontier in Python. how to evaluate the performance of such portfolios.
– Presents a case of using machine learning for predicting credit default. You will get to know tune the hyperparameters of the models and handle imbalances
– Introduces you to a selection of advanced classifiers (including stacking multiple models)and how to deal with class imbalance, use Bayesian optimization.
– Demonstrates how to use deep learning techniques for working with time series and tabular data. The networks will be trained using PyTorch.

Requirements
– Statistics and Basic Python

Description
Who this course is for:
– Developers
– Financial Analysts
– Data Analysts
– Data Scientists
– Stock and cryptocurrency traders
– Students
– Teachers
– Researchers
More Info

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