Comments Off on 92% Off Python & Machine Learning for Financial Analysis
Learn from Kirill Eremenko is a Data Scientist Leading with over five years of experience in finance, retail, transport, and other industries, you will be able to master Python Programming Fundamentals and Harness the Power of ML to Solve Real-World Practical Applications in Finance. If you’re interested in Python & Machine Learning for Financial Analysis Online Course, try this course today.
When You Enroll in Python & Machine Learning for Financial Analysis, You’ll Enjoy These Benefits:
- Throughout the course, You get full lifetime access to more than 23 hours of video at your own pace or re-watch if necessary. Also, you’ll be able to watch wherever you are, whenever you want on the computer, tablet, mobile, and TV,
- Access to course materials never expires, so you can learn at your own pace and return to your favorite lessons at any time.
- Also, Udemy provides a certificate of completion for every course. Udemy automatically emails you a link to your certificate as soon as you complete the course. You can add the certificate to your Linkedin profile or your online CV if you like.
What You’ll Learn in Python & Machine Learning for Financial Analysis
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 Is This Course 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.
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