Ml azure
Azure Machine Learning#
Table of Contents
- Azure Machine Learning
- Azure Machine Learning
- Module 1
- Module 2: Working with Data
- Module 3: Visualizing Data & EXploring Models
Azure Machine Learning#
Sources#
https://github.com/MicrosoftLearning/Data-Science-and-ML-Essentials/tree/master/Labs
Requirement#
- Anaconda
OR
- Spyder (or IPython console)
- scikit-learn
- matplotlib
- numpy
Module 1: Intro to Data Science#
Introduction#
- Evolving subject, no single definition
- Requires a range of skills
Exploration and quantitative analysis of all available structured or unstructured data to develop understanding, extract knowledge, and formulate actionable results.
Data --> Decisions --> Actions
Data ---> What happened? --> Why did it happen? --> What will happen? ---> Decision#
Accidents like plane crashes etc
Areas of interest: Automatic Trading, Bidding
Steps#
- Finding data sources
- Acquiring data
- Cleaning and transforming data, Reshaping (99% work)
- Relationship finding
- Decision
Types of Analytics#
- Retrospective
- Real-time
- Predictive (Most ML falls under)
- Prescriptive
- Intelligent Saas apps (Cortana, ..)
Predictive vs Prescriptive#
- Predictive analysis calibrated on past data, tells us what to expect
- Prescriptive analysis tells what actions to take
Historical Notes#
- Big Data by astronomers Cox & Ellsworth in 1997
- By CCC in 2012
- By KDD in 1996
Big Data Process#
CCC#
- A
KDD#
- A
Module 1#
Chapter 4: Regression#
Intro#
Simple Linear Regression#
Ridge Regression#
Support Vector Machine Regression (SVM)#
Cross-Validation#
Nested Cross-Validation#
- Popular evalution technique in ML
- Divide data set into 10 folds, pich one for test, reserve 1 for validation, and rest 8 as test data.
Chapter 5: Classification#
Intro#
- Prediction of labels/predictable data - X (true/false or 1/-1) using independent variable/Feature/ - Y..
Decision Boundary#
Classification Error#
Loss Functions#
Different ML Techniques & LFs#
Logistic Regression#
SVM Regression#
AdaBoost Regression#
Decision Tree#
Boosted Decision Tree#
Imbalanced Dataset#
Minority Class Data (Excess amount, Weight)#
ROC (Receiver Operating Characteristic) Curve#
FPR & TPR (False Positive Rate & True Positive Rate)#
Chapter 6: Clustering#
Intro#
- Unsuperwised label prediction
Unsuperwised Learning#
- Means training data has no ground truth labels to learn from
K- Means Clustering#
- Input K = number of clusterss
- Randomly initialize centers
- Assign all the points to the closest centers
- Repeat till convergence
Hierarchical Agglomerative Clustering#
- Start with each point in its own cluster
- Repeatedly merge the clusters of the closest two points
Distance metrics are important#
- Large impact on the solution
- Some algos uses "Adaptive" distance measures
Chapter 7: Recommender Systems & Matrix Factorization#
Intro#
\left(\begin{array}{cc}
5 & * & 1 & 1\\
5 & * & 1 & 1
\end{array}\right)
Example:#
Netflix contest
Options#
- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
Matrix-Factorization#
Carmen_1 \left (\begin{array}{cc}
5 & 1
\end{array}\right)
Chapter 8: Intro to Data Science Technologies#
Why Azure ML?#
- Easy to deploy services on production
Supports?#
- Sql
- R
- Python
Cortana Analytics Suite#
- https://www.microsoft.com/cortanaanalytics
- Preconfigures Solutions
- Dashboard & Visualization
- Machine Learning & Analytics
- Azure Bigdata (Hadoop Implementation)
- Information Management
Azure ML Studio#
- https://account.azure.com
- Experiments contain workflow
- Experiments constructed of modules
- Modules:
- Transform Data
- Compute Models
- Score Models
- Evaluate Models
- Create custom modules with SQL, R & Python
Module 2: Working with Data#
Chapter 9#
Chapter 10#
Chapter 11: Data Sampling and Quantization#
Azure ML Table Data Types:#
- Numeric: Integer, Floating points
- Boolean
- String
- Date time
- Time span
- Categorial
- Image
Continuous Vs Catergorial Variables#
Continuous: Countable, e.g. Time, Temperature, Counts* Categorial: Classifiable, e.g. Gender, Type, City
* descrete continuous
Quantization#
A range with sampled data.
What?#
Continuous variables must be sampled
Sampling?#
Digitizing the domain. * Time stamped * Precision
Example#
- Temperature every minute
- Count over 1 hour
Quantization of Continuous Variable#
Convert continuous variables into categorial using binning/categorizing.
Binning: Allocating each value into one category/bin.
Example: * Small, Medium & Large
Module to use: Quantize Module
Extra#
Metadata Editor
Chapter 12: Data Cleansing and Transformation#
(Data Munging)
- Deals with
- Missing & repeated values
- Outliers and errors
- Scaling
- Filtering with custom code
- Iterative process
- Example: Forest-Fire Data
Missing & Repeated Values#
- are common
- many ML algos don't deal with missing values
- repeated values bias results, so
- search for them
- make estimation
- treat them
Clean Missing & Repeated values#
- remove rows
- substitute a specific value
- Interpolate values - Linear/polynomial on the basis of growth/trend of the data
- forward/backword fill
- With Azure ML Module: Clean Missing Data, Remove Duplicate Rows
- With R
- Missing data: is.na()
- Repeated data: duplicated()
- With Python
- Missing data: pandas.isnull()
- Repeated data: DataFrame.drop_duplicates()
Errors & Outliers#
- can bias model training, so
- search for them
- validate
- treat them
Visualizing Outliers#
- Scatter plot matrix
- R - pairs plot
- Python - pandas.tools.plotting.scatter_matrix
- Bar chart or graph
- histogram
Clean Errors & Outliers#
- Error treatment
- Censor: remove entire row
- Trim: trim the value inbetween a range
- Interpolate: Linear or polynomial on the basis of growth/trend of the data
- Substitute
- With Azure ML Module: Clip values (select column--> set lower/upper threshold)
- With R
- data.frame = data.fram[filter.expression,]
- With Python
- frame1 = frame1[(frame1["col1"] > 40.0) & (frame1["col2"] < 30.0) & (frame1["col3"] < 23.0)]
Scaling Data#
(aka Normalization, Transformation) * Why: * to put all the numerical data into same range line -1 to 1 or 0 to 10 other than a:0-1, b:0-100, c:500:1000 * not doing so: * will make adverse effect on training model * will get biased training model
- What:
- looking at numerical features/columns
- numerical features/variable/columns needs similar scale
- Scaling methods:
- zero mean & unit variance
- min-max: all numeric values in range 0 to 1
- logrithmic: does distributional changes (good for classification)
- LogNormal:
- Hyperbolic tangent scaling: distribution transformation
- ordered data like time-series may need to de-trend
- scale after treating outliers
- How:
- Azure ML Module: Normalize Data
- R:
- Python:
- Doubts:
- How to make such transformations?
Module 3: Visualizing Data & EXploring Models#
Chapter 13: Data Exploration & Visualization#
Exploratory Data Analysis#
- What:
- Explore the data with visualization
- Understand the relationships in the data
- How:
- Create multiple views of data
- Data conditioning: Poweful plotting method to project multiple dimension on two dimension page/screen
View of data#
- Relationships in data can be complex
- Data exploration requires multiple views
- Conditioned (aka faceted, trellis, lattice) plots are ideal
- project multiple dimension onto two
- plots of subsets (group by)
Types of plots#
- Scatter and line plots
- Bar:
- like histogram but
- Used for categorical & factor data like disease, blood grp
- Types: ordered, un-ordered
- Histogram:
- used for continuos variable like time, temp
- density or count are plotted on vertical axis
- widely used
- Violin
- Q-Q
- Box:
- Shows 4 quartiles, i.e.
- a box divided in two half (by median),
- one upper vertical line, one lower and
- dot as outliers
- Shows 4 quartiles, i.e.
- Line: connecting dot--> Polynomial regression--> curve
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