Predictive analytics, machine learning, data science and AI: Sydney, 3–4 June 2019
June 3 @ 9:30 am - June 4 @ 5:00 pm
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Our leading course has transformed the machine-learning and data-science practice of the many managers, sponsors, key stakeholders, entrepreneurs and beginning data-science practitioners who have attended it.
This course is an intuitive, hands-on introduction to data science and machine learning. The training focuses on central concepts and key skills, leaving the trainee with a deep understanding of the foundations of data science and even some of the more advanced tools used in the field.
The course also covers key issues of data science practice in a work environment, and directs trainees to a range of further learning directions.
The skills taught are transferable to all software platforms, and the course does not involve coding, or require any coding knowledge or experience. A tool with a graphical user interface is used so trainees can focus on learning the central skills and ideas.
Key skills taught include building, assessing, selecting and deploying predictive models, as well as employing some of the most commonly used methods in the field, including general linear models (GLMs), and advanced methods such as random forests.
Earlybird pricing is available until 20 May 2019.
Group discounts also apply during the earlybird period: 5% for 2–4 people, 10% for 5–6 people, 15% for 7–8 people, and 20% for 9 or more people. Select your desired quantity of tickets and click “Add to cart” to see the discount calculated before checkout.
This course will provide a conceptual overview and practical hands-on experience of a wide range of key tools, techniques and processes.
At the heart of the data mining toolkit is the suite of predictive modelling methods. Accordingly, the course will develop attendees' literacy in the strengths, characteristics and correct application of a range of predictive modelling methods, from relatively simple linear models through to complex and powerful Random Forests, Support Vector Machines, Decision Trees, Tree Boosting Machines and Neural Networks will be covered along the way.
It will also teach the correct framing of predictive modelling problems, suitably preparing data, evaluating model accuracy and stability, interpreting results and interrogating models.
The two key styles of predictive modelling – operational for targeting and explanatory for insights – will be described and distinguished.
As well as predictive modelling, the course will cover a range of other key data mining tools, including:
- Data exploration and visualisation: univariate summaries, correlation matrices, heat maps, hierarchical clustering.
- Cluster analysis – used for customer segmentation and anomaly detection
- Other "unsupervised" outlier detection tools.
This course will primarily be taught using Rattle, a graphical interface for predictive modelling and data science in R. Participants will be exposed to "Big Data" techniques as applied to machine learning and deployed on Cloud Computing platforms.
The following additional topics may be covered depending on the pace and interests of the class:
- Link and network analysis visualisation – which provide a simple and compelling way to communicate and analyse relationships, and are commonly applied in forensics, human resources and law enforcement.
- Association analysis – used in retail market basket analysis and the assessment of risk groupings.
- Frequent item set analysis.
Who should attend?
This course is suitable for anyone in management, administrative, product, marketing, finance, risk and IT roles who works with data and wants to become acquainted with modern data analysis tools.
No prior knowledge of R is required to take this course.
Attendees should, by the end of the course:
- Learn fundamentals of predictive modelling and experience using a range of methods.
- Have improved their ability to assess the effectiveness and fitness for purpose of any predictive modelling tool or technique.
- Have experience with a range of unsupervised data techniques.
- Be exposed to Big Data and Cloud Computing applications.
- An overview of key terms: what do data science, machine learning, AI and deep learning actually mean?
- An intuitive and original introduction to what a machine learning model is, and what it does.
- Practical exercise: Exploratory data analysis–summaries, visualisation, bar charts, pair plots and correlation plots.
- Key terms: What is data? What is a model? What is a record? Field. Training set. Target variable. Missing value.
- Introduction to predictive modelling: What is a decision tree model, how is one built, how does it make predictions and what else can be done with it?
- Practical exercise: Building a decision tree model for classification.
- Decision trees for regression (estimation of amounts), and practical exercise.
- Linear regression models, and practical exercise.
- Generalised linear models (logistic regression) for classification, and practical exercise.
- Most important part of the course: How are predictive models evaluated? What is the KPI of predictive modelling?
- What is the one thing that all practitioners, managers and stakeholders of machine learning must know? And what makes the definition, measurement and improvement of this KPI tricky ?
- An intuitive, visual explanation of the problem of overfitting and the importance of out-of-sample testing.
- Creating training/validation spits.
- Using out-of-sample testing to evaluate models and select a final model.
- The importance of a three-way training/validation/test split.
- Accuracy measures for classification modelling.
- Practical exercises: build multiple classification models, assess them on out-of-sample data and select the best final model out of a range of models including random forests, gradient boosting and support vector machines. Repeat as a model optimisation task to build the most accurate possible decision tree.
- Model deployment. Practical exercise: make new predictions on a developed model.
- Model stability and degradation: the importance of rebuilding models and out-of-time testing
- Advanced classification topics: selecting a classification threshold using ROC curve charts.
- Calculation of the area under the ROC curve as a classification error measure.
- K-fold cross-validation: the “industry standard” in modern machine learning model evaluation.
- Random forests: a powerful, simple to use, and reliable modelling method. How does it work? What are its unique strengths ?
- Practical exercise with random forest.
Courses are taught by Dr Eugene Dubossarsky and his hand-picked team of highly skilled instructors.
About our training
Eugene Dubossarsky’s courses are unlike those offered in universities, online, or by private providers. His data-science classes, in particular, give clients not just knowledge of a process, but the real power of understanding the underlying concepts, allowing them to confidently practice, manage, promote and risk-assess data science.
Dr Dubossarsky says “the way many courses teach data science is like teaching people to memorise and recite poetry in a language they do not understand”. By contrast, he confers an understanding of that language, taught in an intuitive, accessible way that leaves trainees with an instinct for data science. Keeping formulae and mathematics to a bare minimum and taking an intuitive, visual approach, Eugene’s courses deliver a compressed mentoring experience as much as they do content. This is difficult for an average trainer to replicate. Trainees benefit from his extensive knowledge and over 20 years of commercial data-science experience, as well as his unique teaching style.
The resulting testimonials speak for themselves, and candidates come from all walks of life: CEOs, general managers, salespeople, IT professionals, marketing staff, public servants and of course people from many functions in the finance world. These testimonials are extensive, and many more are available on request. With specific regard to finance, Eugene has mentored and advised senior leaders and their teams in a number of major Australian banks.
Having studied stats at Uni I was surprised how far the field has progressed in the last few years, particularly in the area of big data. The great thing about Eugene’s course is I left with a sense that I was up to date with the latest big data modelling concepts but more importantly could also deploy them with some confidence using R. Eugene also made it clear he was available to answer questions after the course, so you are not left hanging. I would absolutely recommend this!
—Damon Rasheed, CEO, Rate Detective
For someone who does not come from an IT background R is a terrifying program. Before doing the Introduction to R course I had previously done other courses in R but always found myself in over my head because they assumed a high level of program experience (even course that required no prior programming knowledge). This course is not like that at all. It starts at ground zero and teaches you everything you need to know to be able to use R confidently in your everyday workplace. It is a must attend for anyone who wants use R!
Data science can be a challenging topic but Eugene’s “Introduction to Machine Learning” course turns complex statistical models into plain English. The course contents and presentation were accessible and I enjoyed the mixture of hands-on rattle() exercises, the challenge of building multiple models with real life data, and the salient theory whiteboard discussions created many “aha” moments.
It was a great introductory course and it gave me with a better grasp of Machine Learning in general, a great framework for thinking about it and practical hands-on skills that I can put to immediate use. I wish I had done this course sooner.
—Charl Swart, Director of Business Operations, Unisys Credit Services
Questions and further details
Meals and refreshments
Catered morning tea and lunch are provided on both days of the course. Please notify us at least a week ahead if you have any special dietary requirements.
Use email@example.com to email us any questions about the course, including requests for more detail, or for specific content you would like to see covered, or queries regarding prerequisites and suitability.
If you would like to attend but for any reason cannot, please also let us know.
Course material may vary from advertised due to demands and learning pace of attendees. Additional material may be presented, along with or in place of advertised.
Cancellations and refunds
You can get a full refund if you cancel 2 weeks or more before the course starts. No refunds will be issued for cancellations made less than 2 weeks before the course starts.
Frequently asked questions (FAQ)
Do I need to bring my own computer?
There’s no need to bring your own laptop or PC. Our courses take place in modern, professional training facilities that have all the computing equipment you’ll need.
I'm lost! How do I find the venue?
Presciient training, coaching, mentoring and capability development for analytics
Please ask about tailored, in-house training courses, coaching analytics teams, executive mentoring and strategic advice and other services to build your organisation's strategic and operational analytics capability.
Our courses include:
- Predictive Analytics, Machine Learning, Data Science and AI
- Data Literacy for Everyone
- Introduction to R and Data Visualisation
- Introduction to Python for Data Analysis
- Forecasting and Trend Analytics
- Advanced Machine Learning Masterclass
- Advanced Masterclass 2: Random Forests
- Advanced R
- Quantum Computing
- Text and Language Analytics
- Fraud and Anomaly Detection
- Introduction to Machine Learning
- Introduction to Data Science
- Kaggle Boot Camp
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For any enquiries, please call +61 4 1457 3322.
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