Google Certified Professional Machine Learning Engineer

Learn to categorize data into qualitative and quantitative types, distinguishing nominal, ordinal, and binary categories, and analyze discrete versus continuous data with interval and ratio scales.

Categorize data into structured, unstructured, and semi-structured, explaining their formats, searchability with SQL, and representative examples like logs, emails, XML, JSON, or YAML.

Improve data quality to boost model performance by cleansing data, handling missing values, and converting data types and categorical features, then train, evaluate, deploy.

Explore exploratory data analysis (EDA) with visual methods like scatter plots, box plots, and histograms to reveal trends and outliers. Learn how EDA guides data-driven model development before formal modeling.

Explore how exploratory data analysis identifies data structure, outliers, and data-driven model suggestions through univariate, bivariate, and multivariate techniques using Pandas and Seaborn.

Explore data analysis and visualization with histograms, scatter plots, and heatmaps to reveal correlations, data shape, and insights for machine learning.

Explore how features and labels drive machine learning, using housing prices as an example; define features (input variables), the label (target), and training, including supervised and unsupervised settings.

Explore feature engineering, transforming raw data into informative features to improve model performance. Apply techniques such as one hot encoding, scaling, binning, aggregation, derived, extraction, interaction, and polynomial features.

Explore accuracy, precision, recall, F1-score, and the confusion matrix, then learn ROC and ROC-AUC with threshold effects and practical examples like cancer and spam detection.

Understand how overfitting arises from high variance and bias, and how loss functions plus L1 and L2 regularization balance model complexity, with strategies like cross-validation and early stopping.

Explore how hyperparameters—set before training—control a model's learning process, bias-variance tradeoff, and generalization, with tuning methods like grid search, random search, and bayesian optimization.

Data science overlaps with machine learning, artificial intelligence, and deep learning to extract insights from structured and unstructured data using statistical and computational techniques.

Get an overview of Google Cloud machine learning services and APIs, including AutoML, Vertex AI, Vision, Language, Translation, Speech, data labeling, and Dialogflow.

Identify the three core types of machine learning—supervised, unsupervised, and reinforcement—based on labeled data, unlabeled data, and environment interactions, with key examples like regression, clustering, and Q-learning.

Learn how supervised learning uses labeled data to train models for prediction, covering regression, classification, and choosing models for discrete or continuous labels.

Master linear regression and classification by predicting continuous labels like tip amount, using structured data from data warehouses, and exploring loss functions and feature engineering.

Explore logistic regression for binary classification with sigmoid probability outputs, regularization techniques (L1, L2) and early stopping, learn thresholding, ROC/AUC evaluation, and feature engineering to prevent overfitting.

Compare machine learning and deep learning, and explain automated machine learning, data wrangling, EDA, and the ML pipeline from problem to production.

Explore automated machine learning with Vertex AI, covering data readiness, feature engineering, model training, hyperparameter tuning, and deployment. See how Vizier optimization and feature store drive faster value.

Evaluate AutoML models for structured data using regression and classification metrics. Interpret data splits, feature attributions, endpoints, and deployment choices for online and batch predictions.

Explore five popular machine learning algorithms: linear regression, logistic regression, decision tree, random forest, and support vector machines.

K-means partitions data into K clusters by assigning points to the centroid with Euclidean distance and updating centroids by the mean, outputting centroids and assignments; elbow point helps choose K.

Understand how neural networks and deep learning mimic brain neurons to recognize patterns, learn from data, and drive vision, language, and speech tasks.

Discover gradient descent as an optimization method that minimizes cost in machine learning, guided by learning rate and derivatives, with batch, stochastic, and mini-batch variants.

Learn loss functions for regression—mean squared error, mean absolute error, huber loss, rmse, binary and multiclass cross-entropy, and hinge loss—and their role in gradient-based optimization and regularization.

Explore activation functions from linear identity to non-linear options such as sigmoid, tanh, and Relu variants. Learn how these choices affect gradient flow, model capacity, and classification performance.

Explore ensemble methods that combine multiple base models to improve accuracy and robustness. Cover bagging, boosting, and stacking, with practical finance, healthcare, and NLP applications.

Explore TensorFlow, a high performance numerical computation library from Google, and learn how tensors and directed acyclic graphs power scalable machine learning from model building to edge deployment.

Explore how to define scalars, vectors, matrices, and 4d tensors in TensorFlow using constants and variables, inspect shapes, convert to numpy, and stack to build higher dimensions.

Explore the TensorFlow API hierarchy from high level to hardware, highlighting Keras as the recommended high level API, with mid-level tf.data, tf.feature_column, tf.train, and low level core and c++ APIs.

Explore tf.data.Dataset APIs to build efficient data pipelines with ingestion methods such as from_tensor_slices, from_generator, list files, and TFRecord, and transformations like map, shuffle, batch, and prefetch.

Explore TensorFlow data handling with tf.data.dataset, from_tensors, and from_tensor_slices. Learn text line dataset loading, CSV parsing, map and flatMap transformations, and prefetching for efficient data pipelines.

Explore embeddings, dense vector representations that convert categorical data to numeric form, capturing similarity and relationships for recommendations, nlp, image recognition, and bioinformatics.

Explore TensorFlow 2 and the Keras functional API to build flexible wide and deep networks, handling multiple inputs and outputs while balancing memorization and generalization.

Explore TensorFlow Extended (TFX), an end-to-end platform for building and deploying ML pipelines with components like example gen, statistics gen, transform, trainer, and pusher.

Explore an MLOps architecture centered on TensorFlow Extended, with CI/CD and continuous training via Cloud Build and Vertex Pipelines, covering data validation, transformation, training, evaluation, and serving.

Vertex AI unifies data science and machine learning for data and model management, training, deployment, and continuous monitoring with pre-trained APIs and AutoML.

Create custom training jobs on Vertex AI by packaging your Python training application or a custom container image and configuring worker pools, environment variables, and training settings.

Export trained models as compliant artifacts for prediction using Vertex AI pre-built containers for TensorFlow, PyTorch, Xgboost, and scikit learn.

Explore Vertex AI feature store, a managed container for sharing and reusing ML features at scale to accelerate model training and deployment. It enables online feature solving and drift detection.

Explore Vertex AI model monitoring to detect training serving skew and prediction drift in auto and custom trained models, with alerts for categorical and numerical features.

Explore Vertex explainable AI, comparing example-based and feature-based explanations, including nearest neighbor search, feature attributions, and methods like Shapley values and integrated gradients to debug and trust models.

Explore hyperparameter tuning with Vertex AI Vizier on Google Cloud to optimize model accuracy through trials, study configuration, and target variables using Bayesian optimization, grid search, or random search.

Learn how BigQuery ML lets SQL practitioners build, evaluate, and deploy machine learning models in BigQuery with standard SQL, from linear and logistic regression to deep neural networks.

Explore BigQuery model types, including classification and regression (logistic and linear), TensorFlow-based DNNs, boosted trees, matrix factorization, K-means clustering, time series, AutoML tables, and TensorFlow model importing.

Explore feature engineering in BigQuery ML, covering automatic and manual preprocessing, feature info statistics, and transformations such as imputation, one hot encoding, timestamp components, and category encoding for GLM models.

Demonstrate building a logistic regression model with BigQuery ML on Google Analytics sample data, including dataset creation, training, evaluation, and predictions.

Learn to access practice questions and answers via video, Q&A forms, and explanations in the resources, and set aside at least one week before the exam to review Q&A.

Provide ten practice questions and expert explanations for the Google professional machine learning engineer exam, covering Vertex AI batch prediction, validation strategies, transfer learning, and edge deployment.

Practice questions and explanations for Google Certified Professional Machine Learning Engineer part two, featuring ten questions on production readiness, distributed training, bias mitigation, BigQuery workflows, Dataflow, and Kubeflow pipelines.

Explore practical machine learning strategies through ten Q&A questions, covering feature crosses for city-level sales, precision-recall tuning, latency optimization, data drift, time series with RNNs, robust pipelines, and regularization techniques.

Explore practical Google certified professional machine learning engineer topics through ten questions on tf.data optimization for tpus, end-to-end Kubeflow pipelines, retraining, and continuous evaluation of models.

Develop cloud-native ml engineering skills through ten practice questions on data integration, serverless pipelines, model deployment, and machine learning on Google Cloud.

Tackle practical machine learning engineering questions on batch predictions with trained TensorFlow models in BigQuery, AutoML Tables, and Data Catalog. Learn scalable deployment, data governance, model evaluation, and detection optimization.

Review ten Google professional machine learning engineer practice questions, covering data normalization for gradient descent, imbalance handling, using a TPU, and deployment with Vertex AI and AutoML natural language.

Explore practical Google Cloud ML strategies across ten questions, including reducing false positives with Vertex AI, batch predictions with BigQuery ML, and Explainable AI insights.

Explore Google Cloud ML engineering with real-time evaluation of precision and recall, metadata management, and explanations using the Shapley method for feature attributions, plus Core ML export for on-device inference.

Explore practical, step-by-step Google Cloud ML workflows through ten questions, covering vertex pipelines with Kubeflow Pipelines SDK, hyperparameter tuning, deployment, distributed training, and model monitoring.

Explore part 11 of the Google professional machine learning engineer practice Q&A, with ten questions on data splitting, real-time ingestion, normalization, validation, and content-based recommendations.

Explore practical strategies for building end-to-end ML pipelines with TensorFlow, TFX and Vertex AI, covering image segmentation, AUC, missing data, and latency optimizations.

Explore ten practice questions on ML engineering topics such as hyperparameter tuning on Vertex AI, Kubeflow pipelines, data augmentation, monitoring, retraining, and deployment.

Solve seven practice questions on Google professional ML engineer topics, from scalable Vertex AI training and recall-focused model evaluation to data privacy with DLP and region-aware GPU availability.