Thank you for sending your enquiry! One of our team member will contact you shortly.
Thank you for sending your booking! One of our team member will contact you shortly.
Course Outline
Introduction
- Difference between statistical learning (statistical analysis) and machine learning
- Adoption of machine learning technology and talent by finance companies
Understanding 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)
Understanding Machine Learning Languages and Toolsets
- Open source vs proprietary systems and software
- Python vs R vs Matlab
- Libraries and frameworks
Understanding Neural Networks
Understanding Basic Concepts in Finance
- Understanding Stocks Trading
- Understanding Time Series Data
- Understanding Financial Analyses
Machine Learning Case Studies in Finance
- Signal Generation and Testing
- Feature Engineering
- Artificial Intelligence Algorithmic Trading
- Quantitative Trade Predictions
- Robo-Advisors for Portfolio Management
- Risk Management and Fraud Detection
- Insurance Underwriting
Hands-on: Python for Machine Learning
- Setting Up the Workspace
- Obtaining Python machine learning libraries and packages
- Working with Pandas
- Working with Scikit-Learn
Importing Financial Data into Python
- Using Pandas
- Using Quandl
- Integrating with Excel
Working with Time Series Data with Python
- Exploring Your Data
- Visualizing Your Data
Implementing Common Financial Analyses with Python
- Returns
- Moving Windows
- Volatility Calculation
- Ordinary Least-Squares Regression (OLS)
Developing an Algorithmic Trading Strategy Using Supervised Machine Learning with Python
- Understanding the Momentum Trading Strategy
- Understanding the Reversion Trading Strategy
- Implementing Your Simple Moving Averages (SMA) Trading Strategy
Backtesting Your Machine Learning Trading Strategy
- Learning Backtesting Pitfalls
- Components of Your Backtester
- Using Python Backtesting Tools
- Implementing Your Simple Backtester
Improving Your Machine Learning Trading Strategy
- KMeans
- K-Nearest Neighbors (KNN)
- Classification or Regression Trees
- Genetic Algorithm
- Working with Multi-Symbol Portfolios
- Using a Risk Management Framework
- Using Event-Driven Backtesting
Evaluating Your Machine Learning Trading Strategy's Performance
- Using the Sharpe Ratio
- Calculating a Maximum Drawdown
- Using Compound Annual Growth Rate (CAGR)
- Measuring Distribution of Returns
- Using Trade-Level Metrics
- Summary
Troubleshooting
Closing Remarks
Requirements
- Basic experience with Python programming
- 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.
Sophia van den Broek - Triple Jump
Related Courses
AdaBoost Python for Machine Learning
14 hours
AutoML with Auto-Keras
14 hours
AutoML
14 hours
Google Cloud AutoML
7 hours
AutoML with Auto-sklearn
14 hours
Pattern Recognition
21 hours
DataRobot
7 hours
Data Mining with Weka
14 hours
H2O AutoML
14 hours
Pattern Matching
14 hours
Machine Learning with Random Forest
14 hours
Apache SystemML for Machine Learning
14 hours