Python & Machine Learning for Financial Analysis

Python & Machine Learning for Financial Analysis, available at $94.99, has an average rating of 4.6, with 139 lectures, based on 4423 reviews, and has 100510 subscribers.

You will learn about 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. This course is ideal for individuals who are Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs. or Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors. or Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience. or 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. It is particularly useful for Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs. or Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in Finance/Banking sectors. or Investment bankers and financial analysts wanting to advance their careers, build their data science portfolio, and gain real-world practical experience. or 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.

Enroll now: Python & Machine Learning for Financial Analysis

Summary

Title: Python & Machine Learning for Financial Analysis

Price: $94.99

Average Rating: 4.6

Number of Lectures: 139

Number of Published Lectures: 131

Number of Curriculum Items: 139

Number of Published Curriculum Objects: 131

Original Price: $199.99

Quality Status: approved

Status: Live

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.

Who Should Attend

• Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs.
• Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in 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.

Target Audiences

• Financial analysts who want to harness the power of Data science and AI to optimize business processes, maximize revenue, reduce costs.
• Python programmer beginners and data scientists wanting to gain a fundamental understanding of Python and Data Science applications in 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.