Big Query ML | Machine Learning using SQL

Making data-driven recommendations and predictions using ML models is now really key for almost all organizations now

Simply put, machine learning allows the user to feed a computer algorithm an immense amount of data and have the computer analyze and make data-driven recommendations and decisions based on only the input data.

Chapters:
0:00 Intro
0:51 Machine Learning Concept
1:03 Common machine learning workflows and its problems
2:20 Introduction to Google Big Query and its features
4:07 Typical workflow using Big Query ML and SQL
5:34 Building ML model using SQL Tutorial Start
6:39 Step1: Exploring the dataset
8:30 Step 2: Select features and create your training dataset
10:00 Step3: Create ML Model using SQL
11:26 Step4: Evaluate your model
12:43 Outro

Links:
Repo Link: https://github.com/syalanuj/youtube/t...
Google Cloud: https://cloud.google.com/
Big Query: https://cloud.google.com/bigquery

Introduction to Google BigQuery

BigQuery is a fully-managed enterprise data warehouse that assists you with overseeing and
break down your information with worked-in highlights.

Common machine learning workflows and their problems

if you've worked with ml models before you know that building and training them can be very very time-intensive
for a common data scientist you must first export small amounts of data from your data store into your ipython notebook and into data handling frameworks like pandas for python
if you're building a custom model you first need to transform and pre-process all that data and perform all that feature engineering before you can even feed the model data in
then finally after you've built your model and say tensorflow another similar library then you train it locally on your laptop or on a vm doing that with a small model then requires you to go back and create more new data features and improve performance and you repeat and repeat and repeat and it's hard so you stop after a few iterations

-- if you weren't familiar with these technologies ml was left to the data scientists of your team and out of your reach but now you can do ml on your structured data sets inside of bigquery using sql in just a few minutes

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#machinelearning #sql #bigquery #googlecloud #model #ml #ai