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Reading list

Machine Learning Basics for a Newbie
6 Steps of Machine learning Lifecycle Introduction to Predictive Modeling
Introduction to Exploratory Data Analysis & Data Insights Descriptive Statistics Inferential Statistics How to Understand Population Distributions?
Reading Data Files into Python Different Variable Datatypes
Probability for Data Science Basic Concepts of Probability Axioms of Probability Conditional Probability
Central Tendencies for Continuous Variables Spread of Data KDE plots for Continuous Variable Overview of Distribution for Continuous variables Normal Distribution Skewed Distribution Skeweness and Kurtosis Distribution for Continuous Variable
Central Tendencies for Categorical Variables Understanding Discrete Distributions Performing EDA on Categorical Variables
Dealing with Missing Values Understanding Outliers Identifying Outliers in Data Outlier Detection in Python Outliers Detection Using IQR, Z-score, LOF and DBSCAN
Sample and Population Central Limit Theorem Confidence Interval and Margin of Error
Bivariate Analysis Introduction
Covariance Pearson Correlation Spearman's Correlation & Kendall's Tau Correlation versus Causation Tabular and Graphical methods for Bivariate Analysis Performing Bivariate Analysis on Continuous-Continuous Variables
Tabular and Graphical methods for Continuous-Categorical Variables Introduction to Hypothesis Testing P-value Two sample Z-test T-test T-test vs Z-test Performing Bivariate Analysis on Continuous-Catagorical variables
Chi-Squares Test Bivariate Analysis on Categorical Categorical Variables
Multivariate Analysis A Comprehensive Guide to Data Exploration The Data Science behind IPL
Supervised Learning vs Unsupervised Learning Reinforcement Learning Generative and Descriminative Models Parametric and Non Parametric model
Machine Learning Pipeline Preparing Dataset Build a Benchmark Model: Regression Build a Benchmark Model: Classification
Evaluation Metrics for Machine Learning Everyone should know Confusion Matrix Accuracy Precision and Recall AUC-ROC Log Loss R2 and Adjusted R2
Dealing with Missing Values Replacing Missing Values Imputing Missing Values in Data Working with Categorical Variables Working with Outliers Preprocessing Data for Model Building
Understanding Cost Function Understanding Gradient Descent Math Behind Gradient Descent Assumptions of Linear Regression Implement Linear Regression from Scratch Train Linear Regression in Python Implementing Linear Regression in R Diagnosing Residual Plots in Linear Regression Models Generalized Linear Models Introduction to Logistic Regression Odds Ratio Implementing Logistic Regression from Scratch Introduction to Scikit-learn in Python Train Logistic Regression in python Multiclass using Logistic Regression How to use Multinomial and Ordinal Logistic Regression in R ? Challenges with Linear Regression Introduction to Regularisation Implementing Regularisation Ridge Regression Lasso Regression
Introduction to K Nearest Neighbours Determining the Right Value of K in KNN Implement KNN from Scratch Implement KNN in Python
Bias Variance Tradeoff Introduction to Overfitting and Underfitting Visualizing Overfitting and Underfitting Selecting the Right Model What is Validation? Hold-Out Validation Understanding K Fold Cross Validation
Introduction to Feature Selection Feature Selection Algorithms Missing Value Ratio Low Variance Filter High Correlation Filter Backward Feature Elimination Forward Feature Selection Implement Feature Selection in Python Implement Feature Selection in R
Introduction to Decision Tree Purity in Decision Tree Terminologies Related to Decision Tree How to Select Best Split Point in Decision Tree? Chi-Squares Information Gain Reduction in Variance Optimizing Performance of Decision Tree Train Decision Tree using Scikit Learn Pruning of Decision Trees
Introduction to Feature Engineering Feature Transformation Feature Scaling Feature Engineering Frequency Encoding Automated Feature Engineering: Feature Tools
Introduction to Naive Bayes Conditional Probability and Bayes Theorem Introduction to Bayesian Adjustment Rating: The Incredible Concept Behind Online Ratings! Working of Naive Bayes Math behind Naive Bayes Types of Naive Bayes Implementation of Naive Bayes
Understanding how to solve Multiclass and Multilabled Classification Problem Evaluation Metrics: Multi Class Classification
Introduction to Ensemble Techniques Basic Ensemble Techniques Implementing Basic Ensemble Techniques Finding Optimal Weights of Ensemble Learner using Neural Network Why Ensemble Models Work well?
Introduction to Stacking Implementing Stacking Variants of Stacking Implementing Variants of Stacking Introduction to Blending Bootstrap Sampling Introduction to Random Sampling Hyper-parameters of Random Forest Implementing Random Forest Out-of-Bag (OOB) Score in the Random Forest IPL Team Win Prediction Project Using Machine Learning Introduction to Boosting Gradient Boosting Algorithm Math behind GBM Implementing GBM in python Regularized Greedy Forests Extreme Gradient Boosting Implementing XGBM in python Tuning Hyperparameters of XGBoost in Python Implement XGBM in R/H2O Adaptive Boosting Implementing Adaptive Boosing LightGBM Implementing LightGBM in Python Catboost Implementing Catboost in Python
Different Hyperparameter Tuning methods Implementing Different Hyperparameter Tuning methods GridsearchCV RandomizedsearchCV Bayesian Optimization for Hyperparameter Tuning Hyperopt
Understanding SVM Algorithm SVM Kernels In-depth Intuition and Practical Implementation SVM Kernel Tricks Kernels and Hyperparameters in SVM Implementing SVM from Scratch in Python and R
Introduction to Principal Component Analysis Steps to Perform Principal Compound Analysis Computation of Covariance Matrix Finding Eigenvectors and Eigenvalues Implementing PCA in python Visualizing PCA A Brief Introduction to Linear Discriminant Analysis Introduction to Factor Analysis
Introduction to Clustering Applications of Clustering Evaluation Metrics for Clustering Understanding K-Means Implementation of K-Means in Python Implementation of K-Means in R Choosing Right Value for K Profiling Market Segments using K-Means Clustering Hierarchical Clustering Implementation of Hierarchial Clustering DBSCAN Defining Similarity between clusters Build Better and Accurate Clusters with Gaussian Mixture Models
Understand Basics of Recommendation Engine with Case Study
8 Ways to Improve Accuracy of Machine Learning Models
Introduction to Dask Working with CuML
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to Machine Learning Interpretability Framework and Interpretable Models model Agnostic Methods for Interpretability Implementing Interpretable Model Understanding SHAP Out-of-Core ML Introduction to Interpretable Machine Learning Models Model Agnostic Methods for Interpretability Game Theory & Shapley Values
Introduction to AutoML Implementation of MLBox Introduction to PyCaret TPOT Auto-Sklearn EvalML
Pickle and Joblib Introduction to Model Deployment
Deploying Machine Learning Model using Streamlit Deploying ML Models in Docker Deploy Using Streamlit Deploy on Heroku Deploy Using Netlify Introduction to Amazon Sagemaker Setting up Amazon SageMaker Using SageMaker Endpoint to Generate Inference Deploy on Microsoft Azure Cloud Introduction to Flask for Model Deploying ML model using Flask
Model Deployment in Android Model Deployment in Iphone

Tutorial – Build a simple Machine Learning Model using AzureML

Tavish Srivastava
Tavish Srivastava Last Updated : 26 Jun, 2020
5 min read

Introduction

How difficult is it to build a machine learning model on R or Python?

For beginners, it’s a Herculean task. For intermediates and experts, it’s just a matter of system capacity, problem understanding and a little time. Machine Learning models sometime face the issue of system incompatibility. Specially, when the data set is huge. In such cases, either the model takes longer to compute or the system crashes. Hence, for beginners and experts, the use of machine learning offers untimely challenges as well.

The good news is, machine learning has become a lot easier is last few years. As a beginner in machine learning, you can kick start your machine learning journey with Microsoft AzureML.

In this article, I’ll impart the necessary information to get you started with Machine Learning. Also, I’ve demonstrated a step by step tutorial to create a machine learning model using this software.

The speed of computation on Microsoft AzureML is comparable to R or Python. Hence, I’d say its worth trying for experts also.

 

What is AzureML?

Microsoft-AzureAzureML is a GUI implementation of machine learning algorithm by Microsoft. Using this tool, implementation of such algorithm becomes exceptionally easy. If you are versed with E-Miner, than understanding this tool wouldn’t be difficult. I found this tool more resourceful and more graphical than E-Miner.

 

What are the resources available on AzureML?

Let’s talk about various resources available with this tool.

  1. Sample Data sets : I love to test tools which have many in-built data sets. It makes the process easier and convenient to test the power of tool. AzureML comes with a long list of inbuilt data sets. Here are the details of available data sets: List of Data Sets
  2. Machine Learning Tools : Azure ML has almost every machine learning algorithm and evaluation metrics formulation.
  3. Data transformation : It has all the filtering options, transformation options, summarization options and matrix calculation options.
  4. Data Format Conversion Options: What if you want to add your own dataset? AzureML has a handful of options to read data sets from your local system. Here are these options :

dataconversion

 

Building a Model

Now, you know the potential of AzureML. Let’s now focus on the ways of using them. I will take up an easy to understand example to demonstrate the same. I’d suggest you to practice these steps with me to get a better understanding of this tutorial.

This is where you start  – (By clicking on create a new experiment)

You get an empty experiment table :

Start

 

Now you can choose a pallete:

Pallette

 

Step 1 : Choose the Data set – This can be your sample data or you can upload also. In this tutorial, I’ll use “Breast Cancer data” from the in-built data sets. Just drag and drop this data in the main window.

step 1

 

Step 2: Choose a sampling tool: You can use search option from pallette to find the “split data” option. Place this below your data set and join.

step 2

You now see two touch points at the split data node. This basically means that you have two data sets ready to be taken forward. Towards the right side, you have the freedom to choose the type of split.

 

Step 3 :  Train a machine learning model : You will need two nodes for this step. Firstly, it will be the type of model you want to build. Secondly, it will be the train model node. You can refer to the following figure :

step 3

You can still notice an exclamation mark in the train model node. It suggests that you need to specify the target variable. Let’s choose the target variable by clicking the mark. Now, you would see a window on right side.  Now choose “Launch column slector”.

step 3_1

Here I have chosen “Class” as the target variable.

 

Step 4: Now you score: Refer to the following figure

step 4

 

Step 5: Finally evaluate

step 5

And RUN the model!

Visualizing the Data Set and Output

To visualize any node, you simply go to node, press right click, then click visualize.

Here is how visual data looks like in our case :

data

As you can see, Class variable only has two values as expected. This tool neatly draws distribution for each variable and allows you to check normality as well.

Here is how the scored model looks like :

scored_distribution

As clearly visible, the estimated probabilities are mostly near zero and one. The cumulative distribution stays almost flat in between. Hence, the model outputs highly segregated values.

Finally, here is how the evaluation graphs look like :

evaluation1evaluation2

evaluation3

 

End Notes

As you can see, the model was highly efficient and it took me less than a minute to build and execute. The evaluation matrices computed is quite exhaustive and probably has the number you were looking for. I loved the tool because of the time efficiency and the user ease it provides.

Did you find this article helpful ? Share with us your experience with Azure ML. I’d love to hear you.

If you like what you just read & want to continue your analytics learning, subscribe to our emails, follow us on twitter or like our facebook page.

Tavish Srivastava
Tavish Srivastava

Tavish Srivastava, co-founder and Chief Strategy Officer of Analytics Vidhya, is an IIT Madras graduate and a passionate data-science professional with 8+ years of diverse experience in markets including the US, India and Singapore, domains including Digital Acquisitions, Customer Servicing and Customer Management, and industry including Retail Banking, Credit Cards and Insurance. He is fascinated by the idea of artificial intelligence inspired by human intelligence and enjoys every discussion, theory or even movie related to this idea.

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Responses From Readers

Clear

Trinadh
Trinadh

Hi Tavish, Thank you for demonstrating AzureML tool. It is amazing how fast one can build a model using this tool.. It`s quite helpful. Thanks

123

Aditya Anand
Aditya Anand

Hi Tavish, Thanks for this informative article. .As a beginner in machine learning it helped me alot.

123

Ravi Chawla
Ravi Chawla

Hey Tavish, Thank you for sharing such valuable informations, your posts always brings something new for newbies like me. By the way is there any way to use free trial of Azure without providing payment information?

123

Tavish
Tavish

Oh yes ! You have a trial period of 8 minutes where you can enter as a guest. No credit card details required.

123

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