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Фото видео монтаж » Видео уроки » Видео уроки web-design » Google Certified Professional Machine Learning Engineer

Google Certified Professional Machine Learning Engineer

 
Google Certified Professional Machine Learning Engineer

Google Certified Professional Machine Learning Engineer

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

What you'll 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

Requirements
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.

Description
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

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