Ml And Python In Finance Real Cases And Practical Solutions

Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance

Last updated 2022-01-10 | 4.5

- Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.
- Understand how to leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns
- risk and Sharpe ratio.
- Understand the theory and intuition behind Capital Asset Pricing Model (CAPM)

What you'll learn

Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.

Understand how to leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns

risk and Sharpe ratio.

Understand the theory and intuition behind Capital Asset Pricing Model (CAPM)

Understand how to use Jupyter Notebooks for developing

presenting and sharing Data Science projects.

Learn how to use key Python Libraries such as NumPy for scientific computing

Pandas for Data Analysis

Matplotlib for data plotting/visualization

and Seaborn for statistical plots.

Master SciKit-Learn library to build

train and tune machine learning models using real-world datasets.

Apply machine and deep learning models to solve real-world problems in the banking and finance sectors such as stock prices prediction

security news sentiment analysis

credit card fraud detection

bank customer segmentation

and loan default prediction.

Understand the theory and intuition behind several machine learning algorithms for regression tasks (simple/multiple/polynomial)

classification and clustering (K-Means).

Assess the performance of trained machine learning regression models using various KPI (Key Performance indicators) such as Mean Absolute Error

Mean Squared Error

and Root Mean Squared Error intuition

R-Squared intuition

and Adjusted R-Squared.

Assess the performance of trained machine learning classifiers using various KPIs such as accuracy

precision

recall

and F1-score.

Understand the underlying theory

intuition and mathematics behind Artificial Neural Networks (ANNs)

Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTM).

Train ANNs using back propagation and gradient descent algorithms.

Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance.

Master feature engineering and data cleaning strategies for machine learning and data science applications.

* Requirements

* No prior experience required.

Description

Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.
Understand how to leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio.
Understand the theory and intuition behind Capital Asset Pricing Model (CAPM)
Understand how to use Jupyter Notebooks for developing, presenting and sharing Data Science projects.
Learn how to use key Python Libraries such as NumPy for scientific computing, Pandas for Data Analysis, Matplotlib for data plotting/visualization, and Seaborn for statistical plots.
Master SciKit-Learn library to build, train and tune machine learning models using real-world datasets.
Apply machine and deep learning models to solve real-world problems in the banking and finance sectors such as stock prices prediction, security news sentiment analysis, credit card fraud detection, bank customer segmentation, and loan default prediction.
Understand the theory and intuition behind several machine learning algorithms for regression tasks (simple/multiple/polynomial), classification and clustering (K-Means).
Assess the performance of trained machine learning regression models using various KPI (Key Performance indicators) such as Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error intuition, R-Squared intuition, and Adjusted R-Squared.
Assess the performance of trained machine learning classifiers using various KPIs such as accuracy, precision, recall, and F1-score.
Understand the underlying theory, intuition and mathematics behind Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTM).
Train ANNs using back propagation and gradient descent algorithms.
Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance.
Master feature engineering and data cleaning strategies for machine learning and data science applications.