Google Certified Professional Machine Learning Engineer

Master ML Algorithms, Data Modeling, TensorFlow & Google Cloud AI/ML Services. 137 Questions, Answers with Explanations

What you will learn

Framing ML problems

Architecting ML solutions

Designing data preparation and processing systems

Developing ML models

Automating and orchestrating ML pipelines

Monitoring, optimizing, and maintaining ML solutions

Description

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Introduction

How to Improve Data Quality

Exploratory Data Analysis (EDA)

How EDA is Used in Machine Learning

Data analysis and visualization

Supervised Learning

Linear Regression

Logistic Regression

Machine Learning Vs. Deep Learning

Automated Machine Learning

Evaluating AutoML Models

ML Model Using BigQuery ML

BigQuery ML Model Types

Introduction to Neural Networks and Deep Learning

Gradient Descent

Loss Functions

Activation Functions

Ensemble Methods

Tensorflow, Tensorflow on Google Cloud

Introduction to Tensorflow

Tensorflow – Scalar, Vector, Matrix, 4D Tensors

Tensorflow APIs

Tensorflow’s tf.data.Dataset APIs

TensorFlow Data Handling

Embeddings

TensorFlow 2 and the Keras Functional API

TensorFlow Extended (TFX) Overview

Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud

Vertex AI

Create Custom Training Jobs

Export model artifacts for prediction

Vertex AI Feature Store

Vertex AI Model Monitoring

Vertex Explainable AI

Vertes AI Vizier

BigQuery ML

Feature Engineering in BigQuery

Practice Questions & Answers

Part 1 – 10 Questions

Part 2 – 10 Questions

Part 3 – 10 Questions

Part 4 – 10 Questions

Part 5 – 10 Questions

Part 6 – 10 Questions

Part 7 – 10 Questions

Part 8 – 10 Questions

Part 9 – 10 Questions

Part 10 – 10 Questions

Part 11 – 10 Questions

Part 12 – 10 Questions

Part 13 – 10 Questions

Part 14 – 7 Questions

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