ML Finance – Machine Learning In Finance

Master the most in-demand skill-set of the world’s top financial institutions with one of the most practical, comprehensive and affordable courses in Financial Machine Learning.

15+ Real-World Practical Applications

Case studies along with their python-based implementation.

Financial Applications Coverage

• Algo Trading
• Portfolio Management
• Fraud detection
• Leanding and Loand Default prediction
• Sentiment Analysis
• Derivatives Pricing and Hedging
• Asset Price Prediction
• and many more

Who Should Take The Course

• Buy/sell side quants
• Asset/Wealth Managers
• CXOs
• Data Scientists
• Machine Learning Engineers
• Students targeting finance sector
• Business Analysts
• AI/ML enthusiasts

What You’ll Learn In Machine Learning In Finance

• Apply machine and deep learning models to solve real-world problems in finance.
• Understand the theory and intuition behind several machine learning algorithms for regression, classification and clustering
• Understand the underlying theory, intuition and mathematics behind Artificial Neural Networks (ANNs) and Deep Neural network.
• Different machine learning based cutting-edge approaches to portfolio optimization.
• Master Python 3 programming fundamentals for Data Science and Machine Learning with focus on Finance.
• Leverage the power of Python to apply key financial concepts such as calculating daily portfolio returns, risk and Sharpe ratio.
• Use key Python Libraries such as NumPy for scientific computing, Pandas for Data Analysis, Matplotlib for data plotting/visualization, and Keras, tensorflow for deep learning.
• Assess the performance of trained machine learning regression models using various KPIs.
• Train ANNs using back propagation and gradient descent algorithms.
• Master feature engineering and data cleaning strategies for machine learning and data science applications.

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