Fundamentals of Machine Learning

Kesto: 2 pv , Opetuskieli: englanti, Materiaalikieli: englanti, Materiaali: online



50% lectures, 30% demos, 20% tutorials.

You are encouraged to follow the demos on your machine, and you will be challenged to find answers to 3 larger problems during the tutorials. While they are a hands-on part of the course, if you prefer not to practice, you are welcome to use that time for additional Q&A, or to analyse your own data. We will provide you with all the necessary data sets, and we will explain what free or evaluation edition software needs to be installed to follow the course on your own laptop. In some training centers we are able to provide pre-built machines which you can use instead of your own—please inquire. You will need an Azure account (even a free one) during the course. You can copy course experiments and data into your workspace for learning and for future reference after the course.



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

•        Teamwork



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

•        Classifiers

•        Clustering

•        Regressions

•        Similarity Matching

•        Recommenders



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:

•        CRISP-DM

•        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


If you can not participate this course, you can send someone else instead of you. If cancellation is done less than 14 days before the course start, we will charge 50% of the price. In case of no show without any cancellation, we will charge the whole price. Cancellation fee will also be charged in case of illness.

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