Google Cloud for Data Scientists Course

Course Summary

The Google Cloud for Data Scientists training course is designed to prepare beginner data scientists and machine learning practitioners to implement regression and classification models in TensorFlow using both structured and unstructured data and then serve the models, elastically and resiliently, with Google Cloud.

The course begins with students getting to know the core data science and machine learning concepts that will be important throughout the course. Next, students will use Google Cloud Jupyter notebook hosting environment, Colab, to prepare a structured dataset sourced from a publicly accessible and Serverless data warehouse. Students will use the dataset to explore basic features of TensorFlow, including its various Application Programming Interfaces (APIs), and learn how to use Google Cloud Machine Learning Engine for distributed training, hyperparameter tuning, and serving of your model as a web service based API. Students will learn how to avoid the training-serving skew problem with an effective feature processing pipeline and explore the importance of feature engineering for building high-performance machine learning systems based on case studies and best practices. The remainder of the course will leverage previous learning about using Google Cloud for structured data and apply it to unstructured data and image classification. The course concludes with students working with convolutional neural networks, implementing changes to TensorFlow models to use convolutional layers, batch normalization, dropout, transfer learning, and applying image-specific data augmentation techniques.

This course targets beginner data scientists and machine learning engineers who have some experience developing with Python, SQL, and Linux Shell. The course will be conducted on Google Cloud Platform. All students need is a reasonably powerful laptop running an up-to-date browser (preferably Chrome).

Purpose

Learn how to create and deploy high-performance data science and machine learning systems on Google Cloud for regression and classification use cases leveraging both structured and unstructured datasets.

Audience

Developers and developer teams looking to use Google Cloud in a data science context.

Role

Data Engineer - Data Scientist - Software Developer - Technical Manager

Skill Level

Introduction

Style

Fast Track - Hack-a-thon - Learning Spikes - Workshops

Duration

3 Days

Related Technologies

Google Cloud

Tensorflow

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Productivity Objectives:

Adopt TensorFlow to create regression and classification models.
Deploy statistical and deep learning models to Google Cloud for training and serving.
Apply feature engineering to structured and unstructured datasets.
Optimize performance metrics of regression and classification models.
Evaluate and use end-to-end data science and machine learning pipelines.

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about our training

Real-World Content

Project-focused demos and labs using your tool stack and environment, not some canned "training room" lab.
Expert Practitioners

Industry experts with 15+ years of industry experience that bring their battle scars into the classroom.
Experiential Learning

More coding than lecture, coupled with architectural and design discussions.
Fully Customized

One-size-fits-all doesn't apply to training teams. That's where we come in!

What You'll Learn

In the Google Cloud for Data Scientists training course, you'll learn:

Google Cloud Basics
- Why Google Cloud for Data Science
- Managed Virtual Infrastructure vs. Serverless
Data Science with Google Cloud
- Tensors as Data Structures
- Machine Learning for Data Science
- Regression vs. Classification Use Cases
- Reproducibility in Data Science
Google Colaboratory (Colab)
- Jupyter Notebooks on Google Cloud
- Using Google Cloud Services from Colab
Regression with Structured Data
- BigQuery for Structured Data Warehousing
- Python, Pandas, and SQL for Data Preparation
- Seaborn for Data Visualization
- Reproducible Datasets with Hashing
- BigQuery Structured Dataset for Regression
- Regression Loss vs. Metric
- Benchmark Loss and Metric for a Dataset
Model Training and Evaluation
- Gradient Descent vs. Alternatives for Training
- Netflix Prize Model Evaluation Case Study
- Best Practices for Model Evaluation
TensorFlow
- TensorFlow Models and Frameworks
- Distributed Training Support
- Core Python API
- Eager vs. Lazy Evaluation
Classification with Structured Data
- Deep Neural Network Models
- Activation Functions
- TensorFlow Playgrounds
TensorFlow Estimator API
- Use Cases for TensorFlow Keras and Estimator APIs
- Regression and Classification with TensorFlow Estimator API
- Processing Sharded Datasets with TensorFlow Data
- Fault-Tolerant Distributed Training
- TensorBoard for Monitoring and Analysis
Cloud Storage (GCS)
- Object Storage and Buckets
- Integration with GCP
- Web-based and Command Line Interfaces
Cloud Machine Learning Engine (MLE)
- End-to-end Machine Learning Pipeline with Cloud MLE
- Compute and Parameter Node Capacity
- Distributed Model Training
- Distributed Model Hyperparameter Tuning
- Autoscalable/elastic Model Serving
Feature Engineering
- Five Criteria for Effective Features
- Case Studies and Best Practices
- Feature Crosses, Quantization, Hot-one Encoding with TensorFlow
- Feature Pre-processing and Engineering in a Machine Learning Pipeline
- Features for Wide-and-Deep Machine Learning Models
Batch and Streaming Data
- Cloud Pub/Sub for Streaming Data
- BigQuery and DataFlow for Feature Engineering
- Data Pipelines and Feature Engineering
Classification with Unstructured Image Data
- Fashion-MNIST and Flowers Image Datasets
- Cross-Entropy Loss and Precision, Recall, ROC, AUC Metrics
- Google Machine Learning APIs
- Cloud AutoML Vision for Benchmark Image Classification Models
- Deep Neural Networks for Image Classification
- Convolutional Neural Networks for Image Classification
- Convolutional and Maxpooling Layers
Training Convolutional Neural Networks
- TensorFlow Image API
- L1, L2, and Dropout Regularization
- Batch Normalization
- Data Augmentation
- Transfer Learning