Free download and Learn Python & Machine Learning for Financial Analysis Udemy course with Torrent and google drive download link
Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance
Course Table of Contents
Python & Machine Learning for Financial Analysis Description
What you’ll learn in Python & Machine Learning for Financial Analysis
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Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.
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Understand how to leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio.
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Understand the theory and intuition behind Capital Asset Pricing Model (CAPM)
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Understand how to use Jupyter Notebooks for developing, presenting and sharing Data Science projects.
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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.
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Master SciKit-Learn library to build, train and tune machine learning models using real-world datasets.
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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.
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Understand the theory and intuition behind several machine learning algorithms for regression tasks (simple/multiple/polynomial), classification and clustering (K-Means).
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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.
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Assess the performance of trained machine learning classifiers using various KPIs such as accuracy, precision, recall, and F1-score.
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Understand the underlying theory, intuition and mathematics behind Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTM).
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Train ANNs using back propagation and gradient descent algorithms.
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Optimize ANNs hyper parameters such as number of hidden layers and neurons to enhance network performance.
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Master feature engineering and data cleaning strategies for machine learning and data science applications.
