Python & Machine Learning for Financial Analysis

Python & Machine Learning for Financial Analysis

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

What you’ll learn

• Master Python 3 programming fundamentals for Data Science and Machine Learning with a focus on Finance.
• Understand how to leverage Python's power 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 the 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 price 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 backpropagation and gradient descent algorithms.
• Optimize ANNs' hyperparameters, such as in the 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 is required.

Who this course is for:

• Financial analysts want to harness Data Science and AI's power to optimize business processes, maximize revenue, and reduce costs.
• Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in the Finance/Banking sectors.
• Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio and gain real-world practical experience.
• There is no prior experience required. Even if you have never used python or any programming language before, don’t worry! You will have a clear video explanation for each of the topics we will be covering. We will start from the basics and gradually build up your knowledge.

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