Master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance
Instructed by Ryan Ahmed 23 hours on-demand video, 18 articles & 15 downloadable resources
After completing the course, you will…
-
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.
Who this course is for:
- 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.
Similar Courses
- Deep Learning A-Z™ 2023: Neural Networks, AI & ChatGPT Bonus
- AWS SageMaker Practical for Beginners | Build 6 Projects
- Machine Learning Regression Masterclass in Python
Deal Score-1
Disclosure: This post may contain affiliate links and we may get small commission if you make a purchase. Read more about Affiliate disclosure here.
