This course is new for 2018! It focuses on the newest technologies of Machine Learning, Artificial Intelligence and Data Science, and introduces the most important concepts and tools.Mer information & fakta
Analysts, budding data scientists, database and BI developers, programmers, power users, DBAs, predictive modellers, forecasters, consultants.
Why attend this class?
Because of Rafal’s 10+ years of real-world machine learning experience.
You will not only learn all the concepts and tools that you need to know from a great teacher who has trained almost 500 data scientists world-wide, a highly-respected presenter, capable of holding your attention, but, above all, from a practitioner of machine learning. Rafal Lukawiecki has been delivering ML, data mining, and data science projects for customers in retail, banking, entertainment, healthcare, manufacturing, education, and government sectors for over ten years. Because of that, you will learn:
- how to avoid common pitfalls,
- how to get ahead of your competition by working faster,
- what is really useful and practical,
- what is more theoretical but still important,
- what hype you should be wary of.
You will be able to ask any questions related to your industry and you will get relevant, pragmatic, no-nonsense answers, helping you get ahead with your own projects.
Machine Learning Fundamentals
We begin with a thorough introduction of all of the key concepts, terminology, components, and tools. Topics include:
- Machine learning vs. data mining vs. artiﬁcial intelligence
- Tool landscape: open source R vs. Microsoft R, Python, SQL Server, ML Server, Azure ML
There are hundreds of machine learning algorithms, yet they belong to just a dozen of groups, of which 5 are in very common use. We will introduce those algorithm classes, and we will discuss some of the most often used examples in each class, while explaining which technology tools (Azure ML, SQL, or R) provide their most convenient implementation. You will also learn how to ﬁnd more algorithms on the Internet and how to ﬁgure out if they are any good for real use. Topics include:
- What do algorithms do?
- Algorithm classes in R, Python, ML Server, Azure ML, and SSAS Data Mining
- Supervised vs. unsupervised learning
- Similarity Matching
Machine learning requires you to prepare your data into a rather unique, ﬂat, denormalised format. While features (inputs) are always necessary, and you may need to engineer thousands of them, we do not need labels (predictive outputs) in all cases. Topics include:
- Cases, observations, signatures
- Inputs and outputs, features, labels, regressors, independent and dependent variables, factors
- Data formats, discretization/quantizing vs. continuous
- Indicator columns
- Feature engineering
- Azure ML data preparation and manipulation modules
- Moving data around and its storage, SQL vs. NoSQL, ﬁles, data lakes, BLOBs, and Hadoop
Process of Data Science
The process consists of problem formulation, data preparation, modelling, validation, and deployment—in an iterative fashion. You will brieﬂy learn about the CRISP-DM industry-standard approach but the key subject of this module will teach you how to apply the scientiﬁc method of reasoning to solve real-world business problems with machine learning and statistics. Notably, you will learn how to start projects by expressing needs as hypotheses, and how to test them. Topics include:
- Stating business question in data science term
- Hypothesis testing and experiments
- Student’s t-test
- Pearson chi-squared test
- Iterative hypothesis reﬁnement
Introduction to Model Building
At the heart of every project we build machine learning models! The process is simple and it follows a well-trodden path. In this module you will build your ﬁrst decision tree and get it ready for validation in the next module. Topics include:
- Connecting to data
- Splitting data to create a holdout
- Training a decision tree
- Scoring the holdout
- Plotting accuracy
Introduction to Model Validation
The most important aspect of any data science, artiﬁcial intelligence, and machine learning project is the iterative validation and improvement of the models. Without validation, your models cannot be reliably used. There are several tests of model validity, most importantly those that check accuracy and reliability. Topics include:
- Testing accuracy
- False positives vs. false negatives
- Classiﬁcation (confusion) matrix
- Precision and recall
- Balancing precision with recall vs. business goals and constraints
- Introduction to lift charts and ROC curves
- Testing reliability
- Testing usefulness
To deliver the best possible training we follow the industry. The agenda and course content are subject to continuous improvement and revision without further notice.