ML and Python in Finance Real Cases and Practical Solutions

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.

Best Seller CourseMath for Data Science Masterclass

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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