Python & Machine Learning for Financial Analysis Course
Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance
In this course, (1) you will have a true practical project-based learning experience, we will build more than 6 projects together (2) You will have access to all the codes and slides, (3) You will get a certificate of completion that you can post on your LinkedIn profile to showcase your skills in python programming to employers. (4) All of this comes with a 30 day money back guarantee so you can give a course a try risk free! Check out the preview videos and the outline to get an idea of the projects we will be covering.
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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), Markowitz portfolio optimization, and efficient frontier.
- Apply Python to implement several trading strategies such as momentum-based and moving average trading strategies.
- 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.
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