BigQuery ML – Machine Learning in SQL using Google BigQuery
Learn machine learning in Google Cloud using BigQuery ML. Train, evaluate, and explain ML models with standard SQL. Perfect for ML engineers and data scientists.
What you’ll learn
- BigQuery ML – Learn Machine Learning in Google Cloud using BigQuery.
- Learn to Train, Evaluate, Inference, Tune and Explain Machine leaning models using standard SQL with Big Query.
- Theory + BigQuery ML implementation of many Machine learning algorithms.
- Detailed theory for each of the ML algorithm with a Real-world example implementation in BigQuery ML.
- Linear regression, Logistic regression, K-means clustering, Boosted Tree.
- Deep neural networks, ARIMA+ Time series Forecasting, Matrix Factorization, PCA.
- Hyperparameter tuning of models, Model Explainability functions, Feature pre-processing functions, model management operations in BigQuery ML.
“BigQuery ML lets you create and execute machine learning models in BigQuery using standard SQL queries.”
Big Query ML is a blessing for engineers who want to work in Machine Learning domain but lack programming language like Python, R. With Big Query ML, they can use their existing SQL knowledge to build operational production-grade Machine learning models.
What’s included in the course ?
Brief introduction to various Machine Learning services of Google Cloud.
Fundamentals of BigQuery ML and challenges which it solves.
All of the Machine Learning algorithms are explained in 2 Steps :
Step 1 : Theoretical explanation of working of an ML algorithm.
Step 2 : Practical implementation of the ML algorithm in BigQuery ML.
Each and every Machine learning algorithm is explained with HANDS-ON examples.
Hyperparameter tuning of models, Model Explainability functions, Feature pre-processing functions.
Model management operations using bq commands.
BigQuery ML pricing (Flat rate & On-demand pricing models).
Assignment for each Machine learning algorithm for self Hands-On in Big Query ML.
Learn Best practices and Optimization techniques for BigQuery ML.
Machine Learning algorithms explained:
Linear regression
Logistic regression
K-means clustering
Boosted Tree
Deep neural networks
ARIMA+ Time series Forecasting
Product Component Analysis (PCA)
Matrix Factorization
After completing this course, you can confidently start creating production-grade Machine Learning models in Real-world corporate projects using BigQuery ML.
Add-Ons
Questions and Queries will be answered very quickly.
Queries, datasets and references used in lectures are attached in the course for your convenience.
I am going to update it frequently, every time adding new components of Bigquery ML.
Who this course is for:
- Machine Learning Engineers
- Data analysts
- Data scientists
- Data Engineers
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