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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