Course Outline
Introduction
- Distinctions between statistical learning (statistical analysis) and machine learning
- The growing adoption of machine learning technology and talent by financial institutions and banks
Different Types of Machine Learning
- Supervised versus unsupervised learning
- Iteration and evaluation processes
- The bias-variance trade-off
- Integrating supervised and unsupervised methods (semi-supervised learning)
Machine Learning Languages and Toolsets
- Open-source versus proprietary systems and software
- Comparison: Python vs. R vs. Matlab
- Libraries and frameworks
Machine Learning Case Studies
- Consumer data and big data applications
- Risk assessment in consumer and business lending
- Enhancing customer service via sentiment analysis
- Identifying identity fraud, billing fraud, and money laundering
Hands-on: Python for Machine Learning
- Setting up the development environment
- Installing Python machine learning libraries and packages
- Working with scikit-learn and PyBrain
How to Load Machine Learning Data
- Databases, data warehouses, and streaming data sources
- Distributed storage and processing using Hadoop and Spark
- Handling exported data and Excel files
Modeling Business Decisions with Supervised Learning
- Data classification techniques
- Using regression analysis to predict outcomes
- Selecting appropriate machine learning algorithms
- Understanding decision tree algorithms
- Understanding random forest algorithms
- Model evaluation methods
- Exercise
Regression Analysis
- Linear regression
- Generalizations and Nonlinearity
- Exercise
Classification
- Bayesian refresher
- Naive Bayes
- Logistic regression
- K-Nearest neighbors
- Exercise
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)
- Exercise
Modeling Business Decisions with Unsupervised Learning
- Approaches when sample data sets are unavailable
- K-means clustering
- Challenges inherent in unsupervised learning
- Techniques beyond K-means
- Bayes networks and Markov Hidden Models
- Exercise
Hands-on: Building a Recommendation System
- Analyzing past customer behavior to enhance new service offerings
Extending your company's capabilities
- Developing models in the cloud
- Accelerating machine learning processes with GPU
- Applying Deep Learning neural networks for computer vision, voice recognition, and text analysis
Closing Remarks
Requirements
- Practical experience with Python programming
- Foundational knowledge of statistics and linear algebra
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.
