Course Outline
Introduction
- Distinction between statistical learning (statistical analysis) and machine learning
- Trends in the adoption of machine learning technology and talent by financial and banking institutions
Different Types of Machine Learning
- Supervised learning versus unsupervised learning
- Iteration and evaluation processes
- Understanding the bias-variance trade-off
- Combining supervised and unsupervised learning (semi-supervised learning)
Machine Learning Languages and Toolsets
- Comparison of open-source versus proprietary systems and software
- Analysis of Python, R, and Matlab
- Overview of libraries and frameworks
Machine Learning Case Studies
- Utilizing consumer data and big data
- Assessing risk in consumer and business lending
- Enhancing customer service through sentiment analysis
- Identifying identity fraud, billing fraud, and money laundering
Hands-on: Python for Machine Learning
- Setting up the development environment
- Acquiring Python machine learning libraries and packages
- Working with scikit-learn and PyBrain
How to Load Machine Learning Data
- Sources: databases, data warehouses, and streaming data
- Distributed storage and processing using Hadoop and Spark
- Exported data and Excel integration
Modeling Business Decisions with Supervised Learning
- Classifying data (classification)
- Using regression analysis to predict outcomes
- Selecting from available machine learning algorithms
- Understanding decision tree algorithms
- Understanding random forest algorithms
- Model evaluation techniques
- 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
- Bootstrap aggregation (bagging)
- Exercise
Modeling Business Decisions with Unsupervised Learning
- Scenarios where sample data sets are unavailable
- K-means clustering
- Challenges associated with unsupervised learning
- Beyond K-means
- Bayes networks and Markov Hidden Models
- Exercise
Hands-on: Building a Recommendation System
- Analyzing past customer behavior to improve new service offerings
Extending your company's capabilities
- Developing models in the cloud
- Accelerating machine learning with GPU
- Applying Deep Learning neural networks for computer vision, voice recognition, and text analysis
Closing Remarks
Requirements
- Experience with Python programming
- Basic familiarity with 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.
