Course Outline
Introduction
- Distinguishing between statistical learning (statistical analysis) and machine learning
- Adoption of machine learning technology and talent by financial institutions and banking companies for government
Different Types of Machine Learning
- Supervised learning versus unsupervised learning
- Iteration and evaluation processes
- Bias-variance trade-off considerations
- Combining supervised and unsupervised learning (semi-supervised learning)
Machine Learning Languages and Toolsets
- Open source versus proprietary systems and software
- Python, R, and Matlab comparisons
- Libraries and frameworks for machine learning applications
Machine Learning Case Studies
- Consumer data and big data analytics
- Assessing risk in consumer and business lending
- Improving customer service through sentiment analysis
- Detecting identity fraud, billing fraud, and money laundering
Hands-on: Python for Machine Learning
- Preparing the development environment
- Obtaining Python machine learning libraries and packages
- Working with scikit-learn and PyBrain frameworks
How to Load Machine Learning Data
- Databases, data warehouses, and streaming data sources
- Distributed storage and processing using Hadoop and Spark
- Exported data and Excel integration
Modeling Business Decisions with Supervised Learning
- Classifying your data (classification techniques)
- Using regression analysis to predict outcomes
- Selecting appropriate machine learning algorithms
- Understanding decision tree algorithms
- Understanding random forest algorithms
- Evaluating model performance
- Exercise: Practical application
Regression Analysis
- Linear regression methodologies
- Generalizations and nonlinearity in regression models
- Exercise: Regression analysis practice
Classification
- Bayesian refresher for classification tasks
- Naive Bayes algorithm application
- Logistic regression techniques
- K-Nearest neighbors approach
- Exercise: Classification methods practice
Hands-on: Building an Estimation Model
- Assessing lending risk based on customer type and history
Evaluating the Performance of Machine Learning Algorithms
- Cross-validation and resampling techniques
- Bootstrap aggregation (bagging) methods
- Exercise: Algorithm performance evaluation
Modeling Business Decisions with Unsupervised Learning
- Scenarios where sample data sets are not available
- K-means clustering algorithms
- Challenges associated with unsupervised learning
- Advanced techniques beyond K-means
- Bayes networks and Markov Hidden Models
- Exercise: Unsupervised learning application
Hands-on: Building a Recommendation System
- Analyzing past customer behavior to enhance new service offerings
Extending Your Organization's Capabilities
- Developing models in the cloud environment
- Accelerating machine learning with GPU technology
- Applying deep learning neural networks for computer vision, voice recognition, and text analysis
Closing Remarks
Requirements
- Experience with Python programming for government applications
- Basic familiarity with statistics and linear algebra for government data 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.
