ML0001 - Practical Data Science with Cortana Intelligence: Azure Machine Learning, SQL Data Mining and Microsoft R

EDU Trainings s.r.o.

Popis kurzu

Obsah kurzu

Module 1: Data Science Fundamentals

• Introduction to data science and its components
• Machine learning vs data mining vs artificial intelligence
• Tools landscape
• Statistics
• Big data
• Data wrangling
• Teamwork

Module 2.1: Tools (SQL & R)

• Getting started with and using SSAS DM, and SQL R
• Structures, models, data flows
• Configuration concerns and pricing
• Using Rattle with R and RStudio
• Using SQL Server 2016 R Server and Services
• Getting a feel for the data: interpreting notched boxplots in R

Module 2.2: Tools (R & Azure ML)

• Overview of Cortana Intelligence Suite
• Getting started with and using Azure ML and Cortana R
• Azure requirements and dependencies
• Provisioning workspaces
• Uploading and connecting to SQL Azure data
• Creating and running Azure ML experiments (programs)
• Embedding R in Azure ML

Module 3: Data

• Inputs and outputs, features and labels
• Data formats, discretization vs continuous
• Cases, observations, signatures
• Feature engineering
• Azure ML data preparation and manipulation modules
• Preparing unstructured text for text analysis
• Feature hashing
• Moving data around and its storage
• Briefly: other Cortana Intelligence Suite tools for data management and storage, including data lakes, BLOBs, and other Hadoop

Module 4: Process

• Stating business question in data science term
• CRISP-DM
• Scientific method of reasoning
• Hypothesis testing and experiments
• Student’s t-test
• Pearson chi-squared test
• Iterative hypothesis refinement

Module 5: Algorithms

• What does data mining do?
• Algorithm classes in Azure ML, R, and SSAS
• Supervised vs Unsupervised learning
• Classifiers
• Clustering
• Regression
• Similarity Matching
• Recommenders

Module 6: Clustering, Segmentation, and Anomaly Detection and Prediction

• Introduction to segmentation
• Clustering algorithms (k-means, EM, and others)
• Interpreting clusters
• Cluster characteristics
• Discrimination
• Tornado charts
• Using clustering for text analysis
• Anomaly detection with clustering, PCA and SVMs

Module 7: Classification

• Introduction to classifiers
• Two-class (binary) vs multi-class
• Decision trees, forests, and boosting
• Decision jungles
• Neural networks and logistic regression
• Overfitting (overtraining) concerns
• Using classifiers for text analysis
• Associative decision trees

Module 8: Basic Statistics

• Basic concepts of statistics: population vs sample, measure types, means and dispersion, distributions
• Confidence intervals, p-values
• Correlation
• Descriptive statistics with R
• Basic concepts of probability
• Finding important features using p-values, linear regression and ANOVA

Module 9: Model Validation

• Testing accuracy
• Lift charts
• Testing reliability
• Testing usefulness

Module 10: Classifier Precision

• Testing classifiers
• False positives vs. false negatives
• Classification (confusion) matrix
• Precision
• Recall
• Balancing precision with recall vs business goals and constraints
• Charting precision-recall (sensitivity-specificity)
• ROC curves
• Other measures of accuracy
• Cross-validation
• Optimising binary classifier thresholds for a known business goal of prediction quality
• Refining models to improve accuracy and reliability
• Hyperparameter tuning
• Class imbalance problem (fraud analytics and rare event prediction)

Module 11: Regressions

• Introduction to simple regressions
• Linear regression (classic)
• Regression decision trees and other ensemble regression algorithms
• Relationship to ANOVA
• Measuring linear regression quality (R-squared, predictor p-values, RMSE, MAE, RAE, RSE, and additional testing using R)

Module 12: Similarity Matching & Recommenders

• Introduction to recommender concepts
• Model-based, similarity-based, and hybrid recommenders
• Association rules
• Understanding itemsets and rules
• Rule importance vs. rule probability
• Data structures for association rules
• Market Basket Analysis
• Collaborative filtering
• Matchbox recommenders
• Validating recommenders

Module 13: Other Algorithms (Brief Overview)

• Sequence clustering and Markov chains
• SVM (Support Vector Machines)
• Time series
• Image recognition
• Text analysis

Module 14: Production & Model Maintenance

• Deploying models to production
• SSAS models and DMX queries
• Azure ML web services: preparation and publishing
• REST APIs: request/response vs batch
• On-going maintenance and model updates

Kontaktní osoba

Lukáš Vallo
+420 724 792 023
lukas.vallo@edutrainings.cz

Hodnocení