Google Certified Professional Machine Learning Engineer
- Description
- Curriculum
- FAQ
- Reviews
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Translate business challenges into ML use cases
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Choose the optimal solution (ML vs non-ML, custom vs pre-packaged)
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Define how the model output should solve the business problem
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Identify data sources (available vs ideal)
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Define ML problems (problem type, outcome of predictions, input and output formats)
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Define business success criteria (alignment of ML metrics, key results)
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Identify risks to ML solutions (assess business impact, ML solution readiness, data readiness)
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Design reliable, scalable, and available ML solutions
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Choose appropriate ML services and components
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Design data exploration/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and serving strategies
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Evaluate Google Cloud hardware options (CPU, GPU, TPU, edge devices)
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Design architectures that comply with security concerns across sectors
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Explore data (visualization, statistical fundamentals, data quality, data constraints)
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Build data pipelines (organize and optimize datasets, handle missing data and outliers, prevent data leakage)
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Create input features (ensure data pre-processing consistency, encode structured data, manage feature selection, handle class imbalance, use transformations)
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Build models (choose framework, interpretability, transfer learning, data augmentation, semi-supervised learning, manage overfitting/underfitting)
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Train models (ingest various file types, manage training environments, tune hyperparameters, track training metrics)
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Test models (conduct unit tests, compare model performance, leverage Vertex AI for model explainability)
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Scale model training and serving (distribute training, scale prediction service)
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Design and implement training pipelines (identify components, manage orchestration framework, devise hybrid or multicloud strategies, use TFX components)
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Implement serving pipelines (manage serving options, test for target performance, configure schedules)
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Track and audit metadata (organize and track experiments, manage model/dataset versioning, understand model/dataset lineage)
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Monitor and troubleshoot ML solutions (measure performance, log strategies, establish continuous evaluation metrics)
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Tune performance for training and serving in production (optimize input pipeline, employ simplification techniques)
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2Types of Data - Qualitative,Nominal,Ordinal,Quantitative,Continuous,DiscreteVideo lesson
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3Types of Data - Structured,Unstructured,Semi-structuredVideo lesson
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4How to Improve Data QualityVideo lesson
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5Exploratory Data Analysis (EDA)Video lesson
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6How EDA is Used in Machine LearningVideo lesson
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7Data analysis and visualizationVideo lesson
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13Introduction to Data Science, Machine Learning, AI and Deep LearningVideo lesson
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14Google Cloud Machine Learning Services and APIsVideo lesson
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15Types of Machine LearningVideo lesson
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16Supervised LearningVideo lesson
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17Linear RegressionVideo lesson
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18Logistic RegressionVideo lesson
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19Machine Learning Vs. Deep LearningVideo lesson
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20Automated Machine LearningVideo lesson
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21Evaluating AutoML ModelsVideo lesson
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225 Machine Learning Algorithms Explained - LR, LR, DT, RF and SVMVideo lesson
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23K-Means AlgorithmVideo lesson
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29Introduction to TensorflowVideo lesson
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30Tensorflow - Scalar, Vector, Matrix, 4D TensorsVideo lesson
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31Tensorflow APIsVideo lesson
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32Tensorflow's tf.data.Dataset APIsVideo lesson
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33TensorFlow Data HandlingVideo lesson
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34EmbeddingsVideo lesson
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35TensorFlow 2 and the Keras Functional APIVideo lesson
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36TensorFlow Extended (TFX) OverviewVideo lesson
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37Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and CloudVideo lesson
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