# Python & Machine Learning for Financial Analysis

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*

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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.

Description

Are you ready to learn python programming fundamentals and directly apply them to solve real world applications in Finance and Banking?

If the answer is yes, then welcome to the “The Complete Python and Machine Learning for Financial Analysis” course in which you will learn everything you need to develop practical real-world finance/banking applications in Python!

So why Python?

Python is ranked as the number one programming language to learn in 2020, here are 6 reasons you need to learn Python right now!

1. #1 language for AI & Machine Learning: Python is the #1 programming language for machine learning and artificial intelligence.

2. Easy to learn: Python is one of the easiest programming language to learn especially of you have not done any coding in the past.

3. Jobs: high demand and low supply of python developers make it the ideal programming language to learn now.

4. High salary: Average salary of Python programmers in the US is around $116 thousand dollars a year.

5. Scalability: Python is extremely powerful and scalable and therefore real-world apps such as Google, Instagram, YouTube, and Spotify are all built on Python.

6. Versatility: Python is the most versatile programming language in the world, you can use it for data science, financial analysis, machine learning, computer vision, data analysis and visualization, web development, gaming and robotics applications.

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