Python & Machine Learning in Financial Analysis – Discounted Course

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Last Updated on October 31, 2023 by GeeksGod

Course : Complete Python and Machine Learning in Financial Analysis

Free Udemy Coupon: Financial Analysis

In this comprehensive course on financial analysis, you will gain a deep understanding of various up-to-date financial analysis techniques. You will also learn how to apply machine learning algorithms in the Python environment to enhance your financial analysis skills.

H2: Understanding Financial Analysis

This course starts by introducing you to the fundamentals of financial analysis. You will explore both technical analysis and fundamental analysis and learn how to use different tools for your analysis.

H3: Technical Analysis

Technical analysis is an important aspect of financial analysis. In this section, you will learn how to calculate popular indicators used in technical analysis, such as Bollinger Bands, Moving Average Convergence Divergence (MACD), and Relative Strength Index (RSI).

Building on the indicators calculated in the previous section, you will then learn how to backtest automatic trading strategies. This will allow you to evaluate the performance of your strategies and make informed investment decisions.

H3: Time Series Analysis

Time series analysis is crucial for understanding and predicting financial data. In this section, you will explore popular time series models, including exponential smoothing, AutoRegressive Integrated Moving Average (ARIMA), and Generalized Autoregressive Conditional Heteroskedasticity (GARCH).

H4: Factor Models

Factor models are used to explain the relationship between the returns of assets and various factors. In this section, you will learn about the Capital Asset Pricing Model (CAPM) and the Fama-French three-factor model.

H4: Asset Allocation Optimization

Optimizing asset allocation is essential for diversifying your portfolio and maximizing returns. This section will introduce you to different methods of asset allocation optimization, including Monte Carlo simulations.

The latter part of the course focuses on advanced financial analysis techniques using machine learning and deep learning algorithms.

H3: Credit Card Fraud/Default Prediction

In this section, you will learn how to apply advanced classifiers, such as random forest, XGBoost, LightGBM, and stacked models, to predict credit card fraud and default. You will also learn how to handle class imbalance and tune the hyperparameters of the models.

H3: Deep Learning in Finance

In the final section, you will discover how deep learning, using PyTorch, can solve various financial problems. You will gain hands-on experience in applying deep learning algorithms to tasks such as predicting stock prices and portfolio optimization.

In conclusion, this comprehensive course on financial analysis will equip you with the knowledge and skills to perform highly specialized financial analysis. By incorporating machine learning and deep learning techniques, you will be able to make data-driven investment decisions and excel in the field of finance.

Enroll now in this course to gain a deep understanding of financial analysis and enhance your expertise in the field.

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What you will learn :

1. You will be able to use the functions provided to download financial data from a number of sources and preprocess it for further analysis
2. 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)
3. Introduces the basics of time series modeling. Then, we look at exponential smoothing methods and ARIMA class models.
4. shows you how to estimate various factor models in Python. one ,three-, four-, and five-factor models.
5. 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.
6. Introduces concept of Monte Carlo simulations and use them for simulating stock prices, the valuation of European/American options and calculating the VaR.
7. Introduces the Modern Portfolio Theory and shows you how to obtain the Efficient Frontier in Python. how to evaluate the performance of such portfolios.
8. 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
9. Introduces you to a selection of advanced classifiers (including stacking multiple models)and how to deal with class imbalance, use Bayesian optimization.
10. Demonstrates how to use deep learning techniques for working with time series and tabular data. The networks will be trained using PyTorch.