Google Certified Professional Machine Learning Engineer

Google Certified Professional Machine Learning Engineer - Printable Version

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Google Certified Professional Machine Learning Engineer - BaDshaH - 06-24-2023

[Image: m-Nme-UX0-Iile-Ne-SOkz-TXg3j-Dg-IWbz-Jgqy.jpg]

Published 6/2023
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: English | Size: 6.50 GB | Duration: 12h 16m

Master ML Algorithms, Data Modeling, TensorFlow & Cloud ML Services - Comprehensive Path to Google ML Certification

[b]What you'll learn[/b]
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

[b]Requirements[/b]
Some prior experience with Google Cloud and Machine Learning will help. Also if you are already certified with Google Professional Data Engineer that will help you greatly.

[b]Description[/b]
Translate business challenges into ML use casesChoose the optimal solution (ML vs non-ML, custom vs pre-packaged)Define how the model output should solve the business problemIdentify data sources (available vs ideal)Define ML problems (problem type, outcome of predictions, input and output formats)Define business success criteria (alignment of ML metrics, key results)Identify risks to ML solutions (assess business impact, ML solution readiness, data readiness)Design reliable, scalable, and available ML solutionsChoose appropriate ML services and componentsDesign data exploration/analysis, feature engineering, logging/management, automation, orchestration, monitoring, and serving strategiesEvaluate Google Cloud hardware options (CPU, GPU, TPU, edge devices)Design architectures that comply with security concerns across sectorsExplore data (visualization, statistical fundamentals, data quality, data constraints)Build data pipelines (organize and optimize datasets, handle missing data and outliers, prevent data leakage)Create input features (ensure data pre-processing consistency, encode structured data, manage feature selection, handle class imbalance, use transformations)Build models (choose framework, interpretability, transfer learning, data augmentation, semi-supervised learning, manage overfitting/underfitting)Train models (ingest various file types, manage training environments, tune hyperparameters, track training metrics)Test models (conduct unit tests, compare model performance, leverage Vertex AI for model explainability)Scale model training and serving (distribute training, scale prediction service)Design and implement training pipelines (identify components, manage orchestration framework, devise hybrid or multicloud strategies, use TFX components)Implement serving pipelines (manage serving options, test for target performance, configure schedules)Track and audit metadata (organize and track experiments, manage model/dataset versioning, understand model/dataset lineage)Monitor and troubleshoot ML solutions (measure performance, log strategies, establish continuous evaluation metrics)Tune performance for training and serving in production (optimize input pipeline, employ simplification techniques)

Overview
Section 1: Introduction
Lecture 1 Introduction
Lecture 2 How to Improve Data Quality
Lecture 3 Exploratory Data Analysis (EDA)
Lecture 4 How EDA is Used in Machine Learning
Lecture 5 Data analysis and visualization
Lecture 6 Supervised Learning
Lecture 7 Linear Regression
Lecture 8 Logistic Regression
Lecture 9 Machine Learning Vs. Deep Learning
Lecture 10 Automated Machine Learning
Lecture 11 Evaluating AutoML Models
Lecture 12 ML Model Using BigQuery ML
Lecture 13 BigQuery ML Model Types
Lecture 14 Introduction to Neural Networks and Deep Learning
Lecture 15 Gradient Descent
Lecture 16 Loss Functions
Lecture 17 Activation Functions
Lecture 18 Ensemble Methods
Section 2: Tensorflow, Tensorflow on Google Cloud
Lecture 19 Introduction to Tensorflow
Lecture 20 Tensorflow - Scalar, Vector, Matrix, 4D Tensors
Lecture 21 Tensorflow APIs
Lecture 22 Tensorflow's tf.data.Dataset APIs
Lecture 23 TensorFlow Data Handling
Lecture 24 Embeddings
Lecture 25 TensorFlow 2 and the Keras Functional API
Lecture 26 TensorFlow Extended (TFX) Overview
Lecture 27 Architecture for MLOps using TensorFlow Extended, Vertex AI Pipelines, and Cloud
Section 3: Vertex AI
Lecture 28 Create Custom Training Jobs
Lecture 29 Export model artifacts for prediction
Lecture 30 Vertex AI Feature Store
Lecture 31 Vertex AI Model Monitoring
Lecture 32 Vertex Explainable AI
Lecture 33 Vertes AI Vizier
Section 4: BigQuery ML
Lecture 34 Feature Engineering in BigQuery
Section 5: Practice Questions & Answers
Lecture 35 Part 1 - 10 Questions
Lecture 36 Part 2 - 10 Questions
Lecture 37 Part 3 - 10 Questions
Lecture 38 Part 4 - 10 Questions
Lecture 39 Part 5 - 10 Questions
Lecture 40 Part 6 - 10 Questions
Lecture 41 Part 7 - 10 Questions
Lecture 42 Part 8 - 10 Questions
Lecture 43 Part 9 - 10 Questions
Lecture 44 Part 10 - 10 Questions
Lecture 45 Part 11 - 10 Questions
Lecture 46 Part 12 - 10 Questions
Lecture 47 Part 13 - 10 Questions
Lecture 48 Part 14 - 7 Questions
Anyone wishing to get Google Cloud Certified Professional Machine Learning Engineer

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