Course Outline
Introduction
- Difference between statistical learning (statistical analysis) and machine learning
- Adoption of machine learning technology by finance and banking companies
Different Types of Machine Learning
- Supervised learning vs unsupervised learning
- Iteration and evaluation
- Bias-variance trade-off
- Combining supervised and unsupervised learning (semi-supervised learning)
Machine Learning Languages and Toolsets
- Open source vs proprietary systems and software
- R vs Python vs Matlab
- Libraries and frameworks
Machine Learning Case Studies
- Consumer data and big data
- Assessing risk in consumer and business lending
- Improving customer service through sentiment analysis
- Detecting identity fraud, billing fraud and money laundering
Introduction to R
- Installing the RStudio IDE
- Loading R packages
- Data structures
- Vectors
- Factors
- Lists
- Data Frames
- Matrixes and Arrays
How to Load Machine Learning Data
- Databases, data warehouses and streaming data
- Distributed storage and processing with Hadoop and Spark
- Importing data from a database
- Importing data from Excel and CSV
Modeling Business Decisions with Supervised Learning
- Classifying your data (classification)
- Using regression analysis to predict outcome
- Choosing from available machine learning algorithms
- Understanding decision tree algorithms
- Understanding random forest algorithms
- Model evaluation
- 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
- When sample data sets are not available
- K-means clustering
- Challenges of 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 additional GPUs
- Applying Deep Learning neural networks for computer vision, voice recognition and text analysis
Closing Remarks
Requirements
- Programming experience with any language
- Basic familiarity with statistics and linear algebra
Testimonials
Trainer, Richard was very good.
Lisa Johansen
Tabitha was engaging, very knowledgeable, clear and prepared.
Cécile McNeil
She was very personable and presented a fluid delivery of the course material.
Karli Buckle
I genuinely enjoyed the real examples of the trainer.
Joana Gomes
The lecturer is very knowledgeable and can substantiate theories with his own personal experiences.
Harry Estipona
The whole day was just great and relaxed which really helped. All the printouts really helped.
Louise Mackrell
It was a one to one session so I was able to ask specific questions relating to my own company needs. The course covered the topic really well and gave me many ideas and actions to take away.
Hazel Matthews
I like the application cases wherein every topic, she has exercises to apply what we have learned.
CRISTINA MEDINA - Innovative Investors & Financing Co., Inc.
I really was benefit from the campari / TRUST FORMULA.
VIRNA INTAL - Innovative Investors & Financing Co., Inc.
I genuinely was benefit from the group activity.
Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
Doing the case study was particularly helpful. The self awareness bit was an eye opener. All in all a great training session.
Zandrea Cabal - Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
The most like about the training is about the workshop so that we apply what i have learned about the topic.
Janette Base - Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
Personally I am blessed and thankful because I was given the opportunity to gain knowledge. . (all topic & subj. Matter).
Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
I really was benefit from the campari.
Marizel Delfinado - Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
I obtained a lot of information in my line of work most especially about leadership and how our system works.
HERMAN TENORIO - Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
All the topics are all informative and related to our daily life and work. So its really very helpful and useful .
EMMA GABRIEL - Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
I generally enjoyed the activity after each topic.
JOCELYN BARTOLOME - Innovative Investors & Financing Co., Inc.; Innovative Investors & Financing Co., Inc.
exercise; when the trainer showed websites related to the subject of the training and how to find relevant information on them
- GFT Poland Sp. z o.o.
Two things: the ability of the trainer to translate all issues in a very clear way and the practical knowledge of the trainer along with his willingness to share it with the group.
- GFT Poland Sp. z o.o.
an accessible way of presenting issues, explaining bothering issues on an ongoing basis
- GFT Poland Sp. z o.o.
specific knowledge transfer
- GFT Poland Sp. z o.o.
Real examples taken from life and the coach's openness.
- GFT Poland Sp. z o.o.
Practical examples showing how the presented financial issues work. In addition, the lecturer pointed to a number of interesting sources allowing for independent deepening of knowledge after the training.
Miłosz Mazur - GFT Poland Sp. z o.o.
The trainer was very knowledgeable and well prepared. I think we was very well capable to prepare a training that was suitable for our needs.