Course Outline
Introduction
- Differentiating between statistical learning (statistical analysis) and machine learning
- Adoption of machine learning technology and talent by financial institutions for government applications
Understanding Different Types of Machine Learning
- Supervised learning compared to unsupervised learning
- Iteration and evaluation processes in machine learning models
- The bias-variance trade-off in model selection
- Combining supervised and unsupervised learning techniques (semi-supervised learning)
Understanding Machine Learning Languages and Toolsets for Government Use
- Comparison of open source versus proprietary systems and software
- Evaluating Python, R, and Matlab for machine learning tasks
- Overview of libraries and frameworks available for government applications
Understanding Neural Networks for Government Applications
Understanding Basic Concepts in Finance for Government Operations
- Stocks trading fundamentals
- Time series data analysis
- Financial analyses methodologies
Machine Learning Case Studies in Finance for Government Use
- Signal generation and testing in financial models
- Feature engineering for enhanced predictive accuracy
- Artificial intelligence-driven algorithmic trading strategies
- Quantitative trade predictions using machine learning
- Robo-advisors for efficient portfolio management
- Risk management and fraud detection techniques
- Insurance underwriting processes enhanced by machine learning
Hands-on: Python for Machine Learning in Government Applications
- Setting up the workspace for government use
- Obtaining Python machine learning libraries and packages for government applications
- Working with Pandas for data manipulation
- Utilizing Scikit-Learn for machine learning tasks
Importing Financial Data into Python for Government Use
- Using Pandas for data integration
- Utilizing Quandl for financial data access
- Integrating with Excel for government reporting
Working with Time Series Data with Python for Government Applications
- Exploring and preprocessing time series data
- Visualizing time series data for insights
Implementing Common Financial Analyses with Python for Government Use
- Calculating returns on investments
- Applying moving windows for trend analysis
- Computing volatility measures
- Performing ordinary least-squares regression (OLS)
Developing an Algorithmic Trading Strategy Using Supervised Machine Learning with Python for Government Applications
- Understanding the momentum trading strategy
- Exploring the reversion trading strategy
- Implementing a simple moving averages (SMA) trading strategy
Backtesting Your Machine Learning Trading Strategy for Government Use
- Identifying common backtesting pitfalls
- Components of an effective backtester
- Using Python tools for backtesting
- Implementing a basic backtesting framework
Improving Your Machine Learning Trading Strategy for Government Use
- KMeans clustering for data segmentation
- K-Nearest Neighbors (KNN) for classification and regression
- Classification or regression trees for decision-making
- Genetic algorithms for optimization
- Managing multi-symbol portfolios effectively
- Incorporating a risk management framework
- Utilizing event-driven backtesting techniques
Evaluating Your Machine Learning Trading Strategy's Performance for Government Use
- Calculating the Sharpe ratio for performance assessment
- Determining the maximum drawdown for risk evaluation
- Computing the compound annual growth rate (CAGR)
- Measuring the distribution of returns
- Using trade-level metrics for detailed analysis
- Summary of key findings and recommendations
Troubleshooting for Government Applications
Closing Remarks for Government Use
Requirements
- Basic experience with Python programming for government applications
- Basic familiarity with statistics and linear algebra for government analysis
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
