Free Certification Course Title: Python and Machine Learning in Financial Analysis
Using Python and machine 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.
shows you how to estimate various factor models in Python. one ,three-, four-, and five-factor 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 concept of Monte Carlo simulations and use them for simulating stock prices, the valuation of European/American options and calculating the VaR.
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.
Who this course is for:
- Financial Analysts
- Data Analysts
- Data Scientists
- Stock and cryptocurrency traders
This course includes:
20.5 hours on-demand video
18 downloadable resources
Full lifetime access
Access on mobile and TV
Certificate of completion
How to Subscribe for 2022 Python and Machine Learning in Financial Analysis?
- Sign Up on Udemy.com
- Subscribe Here(2022 Python and Machine Learning in Financial Analysis): Click Here
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