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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

Introduction to BigQuery ML

Shweta Rawat
Shweta Rawat Last Updated : 14 Feb, 2023
7 min read

Introduction

With regard to educating its community about data science, Analytics Vidhya has long been at the forefront. We periodically hold “DataHour” events to increase community interest in studying data science. These webinars are hosted by top industry experts, and they teach and democratize data science knowledge. Here is the knowledge session by Shanthababu Pandian on “Introduction to BigQuery ML.”

About Speaker: Shanthababu Pandian, AI & Data Analytics Lead at Cognizant, has 20+ years of experience in Information Technology (IT). Expertise in ML & Data Architect and Program Delivery, including liaising with clients, gathering, eliciting requirements, architecting, and devising cost-effective solutions as per delivery frameworks and mitigating project/delivery risks.

Source: Analytics Vidhya

BigQuery ML Overview

As you know, we have so many Cloud platforms available to us. But platforms like Azure, AWS, and Google Cloud Platform (GCP) dominate the all-digital transformation program. They all are competitors and always provide outstanding services to their users. We will talk about the Google Cloud Platform (GCP) here. Google is different and best compared to other champion companies. About 99% of online users use Google in some way, whether chrome, Gmail, cloud, etc.

Speaker's Presentation

Source: Analytics Vidhya

Now, if we see Google analytics now it is very straightforward and creates the data chart in a very understandable manner. Google analytics can handle massive data very well.

BigQuery comes under Big Data Analytics, as seen in the above picture.

What is BigQuery ML?

BigQuery is the data warehouse to work with large amounts of data. With BigQuery, we can:

  • Collect data from various sources
  • Analyze data
  • Visualize the analysis in multiple ways.

BigQuery can be divided into below parts:

Speaker's Presentation

 As you can see in the above diagram, first, you need to collect Raw data. It could be collected from cloud storage, google sheets, databases etc. All the data collected can be inserted into BigQuery space, and we will get the refined data. In BigQuery, you can store data as well as create multiple queries. With the help of SQL statements, we can analyze the data and perform machine learning operations. You can connect obtained data to the locker, data studio, cloud AI etc., so that you have a separate cloud platform. We can do lots of things with this. BigQuery is affordable and compatible with using a massive amount of data.

Features of BigQuery ML

  1. Multi-cloud functionality: It allows data analysis across multiple cloud platforms. It can run the computation on the data right where it is located. You can even execute the queries from foreign cloud platforms like AWS, IBM, and Azure. We can gain insights into the data with consistent data experience across clouds.
  2. Built-in ML integration (BQ ML): The best thing about this is that developers can use simple SQL statements to build the ML. It is used for creating and executing Machine learning models in BigQuery using simple SQL queries. It eliminates the need to understand ML-specific knowledge and programming skills (like Python, R, or Java). It allows all SQL practitioners to build ML models using their existing skills.Speaker's Presentation
  3. Foundation for BI (BQ BI engine): This engine is an In-memory analysis solution. You analyze the data stored in BigQuery with High Concurrency and Response Times. It comes with an SQL interface, which really helps us to interact with other BI tools like Locker, Tableau, Power BI, etc. 
  4. Geospatial Analysis (BQ GIS): BigQuery Geographic Information Systems (GIS) provides information about location and mapping. It converts Latitudes and Longitudes columns into geographical points.
  5. Automated Data Transfer (BQ Data Transfer Service): This Service is automated, and data moves into BigQuery regularly. No special coding is required. It takes care of Data Backfills to compensate for any gaps or outages during ingestion.

Speaker's Presentation

Below is the simple architecture diagram that shows the data warehouse we are implementing. Without Completing this, we cannot execute our ML operations.

Speaker's Presentation

What is Special in BigQuery ML?

Imagine there are 2 friends, David and Paul,  talking about ML projects. Paul learned the python language and essential ML aspects and started writing codes. Paul was feeling proud of this and bragging to David that no one can work on ML projects without knowing python language. 

Speaker's Presentation

On the other hand, David has multiple years of experience in writing SQL queries. David doesn’t know any programming language. He wants to work on ML projects but is afraid it might be difficult for him as he doesn’t know any programming language like python, R, or Java.

Paul tells him that it’s impossible to work on ML aspects without the knowledge of primary programming languages.

Like David, many people are proficient in SQL but still can’t work on ML projects.

People like David must explore Big Data BigQuery ML, and it will help them enough to work on ML projects because most of the clients want to implement BigQuery ML on their projects. If you know SQL, it is very easy to implement that. Below is the machine learning process in 5 easy steps.

 5 Crucial Steps to Nail Machine Learning Process

As mentioned before, you need to collect the data from various sources. Then you need to do data cleaning and future engineering. This is very important to get the Golden dataset. Next, you require Model building for selecting the correct ML algorithm. We will evaluate the model after that. Lastly, you are going to deploy the model on the deployment production. The significant libraries supporting these steps are NumPy, SciPy, Pandas, Matplotlib, Keras, TensorFlow, SciKit-Learn, PyTorch, Scrapy, and Beautiful Soup.

Speaker's Presentation

David is worried that he has to find and learn from so many libraries. And he assumes that he is not a fit person for doing ML projects. He concludes that Paul was correct. 

big query

David only knows CRUD (Create, Read, Update, Delete), a basic procedure. He is thinking, can he use this approach in ML? We will find the answer ahead.

What is BigQuery ML (Machine Learning)?

The BigQuery from google is a serverless, highly scalable, cost-effective cloud data warehouse. It is designed to help you make informed decisions quickly to easily transform your business. BigQuery provides ML capabilities on top of the data that is being stored in it using the standard SQL syntax.

Now, David should get relaxed; yes, he can work on ML projects using SQL knowledge. 

So, if you want anything to learn, forget about complexity and focus on your interest. Anything could be possible with dedication and basic knowledge.

BigQuery ML supports running models using SQL queries, bridging the gap between data analysts and scientists.

It supports various models like Linear regression binary, logistic regression, multiclass logistic regression, K-means clustering, etc.

Speaker's Presentation

As you can see, below is the complete workflow of the BigQuery ML data available in the warehouse. 

Speaker's Presentation

Steps to implement BigQuery ML data: 

ETL stands for Extracting, Transforming, and Loading data into our BigQuery. One very crucial step is to create a model, then evaluate it and lastly, predict the model. 

For model creation, the speaker has used his own dataset, as you can see below i.e. mydataset.mymodel. You can also give options in this as below. For feature information, you need to give a description.

Speaker's Presentation

More steps:

Speaker's Presentation

Now, log in to BigQuery using your Gmail account. It will also ask for your Credit/debit card details. Below on the screen, you can see the projects created on BigQuery. You can also create new projects in it. Once the project is created, you have the specific ID.

Speaker's Presentation

Click on the option “ADD DATA” and upload your excel csv file here. Now click on the “Editor” option, you can see your executed datasets.

Speaker's Presentation

When you click on Editor2, you’ll see all the executed models like precision, recall, accuracy, loss etc.

Speaker's Presentation

Click on travel_insurance_model, and then “Evaluation”, you will see all the required and necessary information like below.

Speaker's Presentation

When you are dealing with python code or implementing ML, we separate the entire dataset into a Training set and test like 75% and 25%, respectively.

Speaker's Presentation

We can say that David should get excited now, as he can work on ML projects. He already knows SQL, and with the help of BigQuery, he can get the opportunity to work on some ML tasks. 

Conclusion

So, we learned that you could work on ML projects without processing language knowledge. With the basic knowledge of SQL and the help of BigQuery, you are good to go. For BIgQuery ML, A basic understanding of ML discipline is required, and then Data Analyst can enter the world of Machine learning. I hope this article erases your doubts.

The media shown in this article is not owned by Analytics Vidhya and is used from the presenter’s presentation. 

Shweta Rawat
Shweta Rawat

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