Google-Cloud-Machine-Learning-Engineer

Explore my self-taught journey of Google Cloud Machine Learning Engineer Path

This repository documents my self-directed learning journey as I embark on the Machine Learning Engineer path with Google Cloud Skills Boost. Here, you'll find a curated collection of resources, code snippets, and insights gained through hands-on practice on Google Cloud's powerful tools.

View my Badges in:

https://www.cloudskillsboost.google/public_profiles/a8702504-2772-45bd-9d39-5a9fa63248fb

Projects

1. Perform Foundational Data, ML, and AI Tasks in Google Cloud

Description: Learn the basic features for the following machine learning and AI technologies

Tools Used:

BigQuery

Master data warehousing and SQL queries for efficient data exploration and analysis.

Dataflow

Orchestrate complex data pipelines for pre-processing, ETL, and feature engineering.

Dataproc

Leverage distributed computing power for scalable data processing and analysis with Spark and Hadoop.

Vertex AI

Discover Google's unified platform for training, deploying, and managing ML models.

Cloud Natural Language API

Let the text speak for itself. Extract entities, categorize topics, and even translate languages – your AI companion for understanding the nuances of human language.

AI Platform

Your one-stop ML workshop. Train, deploy, and manage your custom models with ease, all on a unified platform. From pre-trained solutions to cutting-edge research, it's your key to unlocking ML's potential.

Cloud Dataprep by Trifacta

Say goodbye to messy data. Visually explore, clean, and prepare your datasets without writing a single line of code. This intuitive tool empowers everyone to become a data champion.

Video Intelligence API

See smarter, analyze deeper. Give your videos the power of sight and sound. Automatically identify objects, actions, and emotions, unlocking a treasure trove of insights and enriching your video experiences.

Cloud Speech AI

Listen closely, understand deeply. Transcribe audio with precision, analyze sentiment, and unlock the hidden meaning within spoken words.

2. Predicting Visitor Purchases with BigQuery ML

Description: Your data analyst team exported the Google Analytics logs for an ecommerce website into BigQuery and created a new table of all the raw ecommerce visitor session data for you to explore. Using this data, you'll try to answer a few questions.

Tools used: BigQueryML, SQL

• Query and explore the ecommerce dataset
• Create a training and evaluation dataset to be used for batch prediction
• Create a classification (logistic regression) model in BigQuery ML
• Evaluate the performance of your machine learning model
• Predict and rank the probability that a visitor will make a purchase

3. Entity and Sentiment Analysis with the Natural Language API

Description: The Cloud Natural Language API lets extract entities from text, perform sentiment and syntactic analysis, and classify text into categories. Learn how to use the Natural Language API to analyze entities, sentiment, and syntax by:

• Creating a Natural Language API request and calling the API with curl
• Extracting entities and running sentiment analysis on text with the Natural Language API
• Performing linguistic analysis on text with the Natural Language API
• Creating a Natural Language API request in a different language

Tool used:

• API KEY
• SHH (from Compute Engine Instance)
• Natural Language API
  • Multilingual natural language processing
  • Analyzing syntax and parts of speech
  • Analyzing entity sentiment
  • Sentiment analysis with the Natural Language API