Fundamentals of Machine Learning
Course focuses on the newest technologies of Microsoft Machine Learning Server and SQL Server 2017. By popular demand, second part (3 days) of this course teaches programming in R, however most of the course is also applicable to Python programmers, as the key libraries are the same.
- Machine Learning Fundamentals
- 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
- Topics include:
- Algorithms
- 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
- Topics include:
- Data
- 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
- Topics include:
- Process of Data Science
- 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
- Topics include:
- Introduction to Model Building
- Topics include:
- Connecting to data
- Splitting data to create a holdout
- Training a decision tree
- Scoring the holdout
- Plotting accuracy
- Topics include:
- Introduction to Model Validation
- 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
- Topics include:
Hind kokkuleppel
See koolitus toimus viimati novembris 2019. Me ei tea veel, kas ja millal see kordub — seepärast on ta siin alles. Küsi järele, kui see sind huvitab.
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