Python & Machine Learning for Financial Analysis

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

4.52 (4598 reviews)

Udemy

platform

English

language

Data Science

What you will 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.

key Python Libraries such as NumPy for scientific computing, Pandas for Data Analysis, Matplotlib/Seaborn for data plotting/visualization

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

Understand the theory and intuition behind several machine learning algorithms for regression, classification and clustering

Assess the performance of trained machine learning regression models using various KPI (Key Performance indicators)

Assess the performance of trained machine learning classifiers using various KPIs such as accuracy, precision, recall, and F1-score.

Understand the underlying theory, intuition behind Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs) & 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.

Course Gallery

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Comidoc Review

Our Verdict

This course effectively combines Python programming, data science, and machine learning, specifically tailored to financial applications. While it does provide essential building blocks for beginners, it might not be an ideal entry point for those entirely new to the subject matter due to some inconsistencies in pacing and depth of explanation. Despite a few areas that require improvement, its strong focus on practical problem-solving sets it apart from other courses.

What We Liked

Comprehensive coverage of Python programming fundamentals for data science and machine learning, specifically applied to finance.
Detailed explanations of key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio.
In-depth exploration of the theory and intuition behind various machine learning algorithms for regression, classification and clustering, with practical applications in finance.
Hands-on experience with popular Python libraries like NumPy, Pandas, Matplotlib/Seaborn, and SciKit-Learn.

Potential Drawbacks

Some students expressed disappointment with the AI/ML section, citing insufficient explanation of function parameters and lack of a comprehensive introduction to some concepts.
Certain sections seem rushed with abrupt transitions, causing confusion for learners unfamiliar with the subject matter.
Assessment of machine learning models' performance can be more thoroughly explained, especially regarding real-world applicability.
Lack of clarity in feature engineering and data cleaning processes.