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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

Learn to Build Powerful Machine Learning Models with Amazon Service

Tavish Srivastava
Tavish Srivastava Last Updated : 21 Dec, 2015
5 min read

Introduction

After using Azure ML last week, I received multiple emails to publish a tutorial on Amazon’s ML. Thankfully, some of my meetings got postponed and I got time to write this.

Here is some more good news for you, I present you a tool which will make it even more simpler. It will just remove all the guess work you had to do with Azure ML in choosing model and splits. Obviously, I am talking about the Amazon ML tool. Unfortunately, this time you won’t get a trial pack but have to create your account giving up your credit card information. However, the tool is free to use and your credit card information is used only in case you breach the free tier.

In this article, I’ve demonstrated a step by step tutorial to build machine learning model with Amazon. I’ve also shared a video tutorial at the end of this article. Let’s make our first machine learning model with Amazon ML tool.

amazon machine learning step by step tutorial

 

What’s New in Amazon Machine Learning ?

Amazon is known for enhanced user experience, timely innovation and developments.

Just 4 days back, Amazon added a feature for Random Data Splitting and Cross Validation. Now you can train and evaluate machine learning models based on random input data split.  This will help you to avoid overfitting and produce more accurate evaluations.

Last Month, Amazon enabled real time predictions feature which let’s users to preview real time prediction before creating the application. This features requires no code. It’s ‘push a button’ to get started feature.

 

Also Read : Amazon re:Invent 2015 ( Machine Learning Reinvented)

 

Price Breakdown

 Basically, Amazon charges you for 2 services:

Data Analysis and Model Building Fees – It depends size of input data, number of variables, types of transformation and number of computation hours. For this, you’ll be charged $0.42 per hour.

Prediction Fees – It can further be divided into Batch Predictions and Real Time Predictions. Batch Predictions are when your application obtain many predictions at once. In real time predictions, you can request predictions for immediate use via web, mobile or desktop applications. Batch Prediction costs $0.10 per 1000 predictions. Real Time Prediction costs $0.0001 per prediction.

 

Machine Learning Model using Amazon Service

Let’s get to work now!

1. Once you sign in, You’ll find this as the main page (shown below). Now, select Machine Learning models to move to the first page of ML tool.

ML1
 2. Next step is to input a data set. In case, you do not have any ready data set, you can use the one suggested in the dialogue box “banking.csv”. Select S3 as the option (aml-sample-data/banking.csv). Once the data set is loaded successfully, you’ll get a dialogue box of ‘validation successful’.

ML2
Screen Shot 2015-12-05 at 8.36.52 pm
4. Press “Continue” to move to the next screen. You’ll now find all variables and a sample data. One thing you need to make sure is the target tag. This is your dependent variable. In our current example, the target is “y”. Hence, you see a mark in the Target column. Screen Shot 2015-12-05 at 11.54.26 pm

 

5. Now press ‘continue’ and click ‘Review’. In the final tab, you’ll find a summary of all inputs. Below is a sample:

Screen Shot 2015-12-05 at 11.56.45 pm

6. Finally, you press “Finish” and you are done.

 

Checking Model Results

To check the results, go to Dashboard.

Screen Shot 2015-12-05 at 11.58.46 pm

 

In the dashboard, you can find all type of objects created. Here are some key checks you can do:

1. Check the data type : On clicking the ID of Banking.csv, you will find a dashboard to browse through the data. Screen Shot 2015-12-06 at 12.01.35 am2. Now, Click Target Visualization. You’ll find the distribution of each column.  For instance, following is the distribution of the target variable (y): Screen Shot 2015-12-06 at 12.03.02 am

3. Check Performance Metrics : To check performance metrics, click ID of Evaluation type. Below is the dashboard you get:Screen Shot 2015-12-06 at 12.04.57 am

4. As you can see, our model has a AUC of 0.94 . Also, this tool gives you an option to adjust score threshold. This is a very interesting simulation to witness the trade off between false positive and true positive. Here is an instance :

Screen Shot 2015-12-06 at 12.07.12 amIn this chart, you can move the threshold score which gives you % correct and % error. The grey line is for distribution of 0s and black line is the distribution of 1s. The shaded portions represent type 1 and type 2 errors depending on which side of the cut off line the area falls. You also have a tool kit which is called the advanced metrics. These are other levers which can be adjusted to simulate the same graph. Here is a snapshot of this tool kit :

 Screen Shot 2015-12-06 at 12.08.02 am

Additional Resource: You may also be interested in this 53mins tutorial delivered at AWS re:invent 2015:

End Notes

Amazon ML tool is a really good tool for visualisation of data and results. The time which the tool take is slightly on the higher side when I compare it with H2O or other similar tool kits. However, the entire process is exceptionally simple to execute.

In this article, I’ve demonstrated a step by step process to build a machine learning model using Amazon ML service. As you have seen, it’s quite simple and ‘codeless’ process. So, people who find coding to be intimidating should use such services often.

Did you find this article helpful ? Share with us your experience with Amazon Machine Learning tool.

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.

Amazon MLAUCcross-validationmachine learningROC
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

Rick
Rick

Nice article and very easy to follow. The whole tutorial takes a max of 10 mins of ML processing time. Unfortunately the ML module kicks off other ongoing stats processes which keep billing you in the background with no obvious visibility.

123

shaw38
shaw38

Hi Tavish, Could you publish a tutorial on how to use H2O? I'm an absolute beginner when it comes to H2O but have tried Azure ML before, if this info helps. Thanks!

123

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