AWS Certified Machine Learning Specialty - Machine Learning - Data engineer - Amazon Web Services (AWS) - Python - Artificial Intelligence

Na inschrijving van de Award Winning E-learning training AWS Certified Machine Learning – Specialty, ontvangt u per e-mail een link om in te loggen in uw leeromgeving waar u een persoonlijk wachtwoord aanmaakt. Eenmaal ingelogd in uw persoonlijke leeromgeving ziet u een overzicht van de cursusonderdelen. Via de inhoudsopgave schakelt u automatisch over naar elk gewenst onderdeel van de training.

Er zit tevens een Voortgangsbewaking bij om eenvoudig te zien hoe ver u bent binnen uw training. U kunt daarbij 1 jaar lang (365 dagen), 24/7 (elke dag en nacht) inloggen om verder te gaan met de training.

Deze Engelstalige training met ondertiteling heeft interactieve, eenvoudig te volgen video's in HD beeldkwaliteit met heldere audio kwaliteit. Daarnaast biedt de training Support en/of een Online Mentor aan als u problemen ondervindt. De training is beschikbaar in elke browser voor zowel PC, Mac, Tablet én Smartphone. Dus zelfs via uw mobiel kunt u handig de training volgen.

De training is inclusief lees- en/of praktijkopdrachten met trainingstest, mits noodzakelijk voor de training. Na afronding van de training krijgt u een Certificaat van Deelname en sluit u zich aan bij de reeds duizenden tevreden cursisten.

Cursusinhoud

AWS Certified Machine Learning: Data Engineering, Machine Learning, & AWS

Course: 36 Minutes

• Course Overview
• What Is Data Engineering?
• Hierarchy of Data Needs
• What Is Machine Learning?
• Machine Learning Pipelines, Approaches, & Workflows
• Machine Learning, AI, Data Mining, and Statistics
• Data Repositories and Data Warehouses
• Data Pipelines and Data Ingestion
• Data Processing and Transformations
• What Is Amazon Web Services (AWS)?
• Machine Learning with AWS
• Course Summary

AWS Certified Machine Learning: Amazon S3 Simple Storage Service

Course: 23 Minutes

• Course Overview
• What Is Amazon S3?
• Amazon S3 Organization
• Amazon S3 Features: Storage Lens
• Amazon S3 Features: Intelligent Tiering
• Amazon S3 Features: Access Points
• Amazon S3 Features: Batch Operations
• Amazon S3 Features: Block Public Access
• Working with the Amazon S3 Management Console
• Amazon S3 Use Cases: Archives, Data Lakes, Analytics
• Amazon S3 Case Studies
• Course Summary

AWS Certified Machine Learning: Data Movement

Course: 34 Minutes

• Course Overview
• What is AWS Glue?
• AWS Glue Extract, Transform, and Load Pipelines
• Working with Amazon Glue
• Applications and Benefits of Data Streaming
• Stream Processing vs. Batch Processing
• Amazon Kinesis: Benefits and Use Cases
• AWS Kinesis: Capabilities
• AWS Kinesis: Video Streams
• AWS Kinesis: Data Streams
• AWS Kinesis: Data Firehouse
• AWS Kinesis: Data Analytics
• Working with AWS Kinesis
• Course Summary

AWS Certified Machine Learning: Data Pipelines & Workflows

Course: 41 Minutes

• Course Overview
• What Is AWS Data Pipeline?
• Configuring AWS Data Pipeline
• AWS Data Pipeline vs. AWS Glue
• What Is AWS Batch?
• AWS Batch Use Cases
• What Is AWS Step Functions?
• AWS Step Functions Use Cases
• Working with AWS Batch and Step Functions
• Real-time and Video Data Pipelines
• Batch Processing and Analytics Pipelines
• Course Summary

AWS Certified Machine Learning: Jupyter Notebook & Python

Course: 39 Minutes

• Course Overview
• What Is Jupyter Notebook?
• Python for Data Science
• Python Packages - NumPy
• Python Packages - Pandas
• Working with NumPy Packages
• Working with Pandas Packages
• Python Packages - Matplotlib
• Python Packages - Seaborn and Bokeh
• Using Matplotlib, Seaborn, & Bokeh for Data Analysis
• Machine Learning in Python with scikit-learn
• Implementing Machine Learning Models Using Python
• Course Summary

AWS Certified Machine Learning: Data Analysis Fundamentals

Course: 34 Minutes

• Course Overview
• Categorical and Numerical Data Types
• Bernoulli, Uniform, and Binomial Data Distributions
• Normal, Poisson, and Exponential Data Distributions
• The Primary Role of Data Visualization
• Data Visualization - Traditional Graphic Types
• Data Visualization - Modern Graphic Types
• Implementing Data Visualization with Python
• Time Series Analysis in Data Science
• Advanced Time Series Analysis Concepts
• Implementing Time Series Analysis in Python
• Course Summarys

AWS Certified Machine Learning: Athena, QuickSight, & EMR

Course: 37 Minutes

• Course Overview
• What Is Amazon Athena?
• Tables, Databases, and Data Catalogs in Athena
• Working with Athena to Create Tables and Run Queries
• What Is Amazon QuickSight?
• Amazon QuickSight Terminology
• Creating Multi-visual Analyses in Amazon QuickSight
• Working with Amazon QuickSight for Multiple ML Tasks
• Data Processing: Amazon Elastic MapReduce (EMR)
• Amazon Elastic MapReduce (EMR) Use Cases
• Amazon Elastic MapReduce (EMR) and Apache Hadoop
• Amazon Elastic MapReduce (EMR) and Apache Spark
  Course Summary

AWS Certified Machine Learning: Feature Engineering Overview

Course: 35 Minutes

• Course Overview
• What Is Feature Engineering?
• Features and the Dimensionality Dilemma
• Amazon SageMaker for Feature Engineering
• How Amazon SageMaker Feature Store Works
• Working with Amazon SageMaker Feature Store
• How Amazon SageMaker Ground Truth Works
• Working with Amazon SageMaker Ground Truth
• Missing Data Imputation in ML Models
• Imbalanced Data in ML Classification
• How Data Outliers Impact Data Analysis
• Course Summary

AWS Certified Machine Learning: Feature Engineering Techniques

Course: 28 Minutes

• Course Overview
• Feature Engineering: One-hot Encoding
• Feature Engineering: Binning
• Feature Engineering: Data Transformations
• Feature Engineering: Data Scaling and Normalization
• Feature Engineering: Data Shuffling
• Working with Feature Engineering Techniques
• Text Feature Engineering
• Text Mining: TF-IDF
• Bag-of-Words Model vs. TF-IDF
• What are N-Grams?
• Using Spark and EMR Workflows for Data Preparation
• Course Summary

AWS Certified Machine Learning: Problem Framing & Algorithm Selection

Course: 1 Hour, 13 Minutes

• Course Overview
• Machine Learning Mindset and Project Life Cycle
• Machine Learning (ML) Solvable Problems
• Difficult Problems in Machine Learning
• Identifying Machine Learning Use Cases and Metrics
• Identifying Expected Outcome
• Formulating Machine Learning Questions
• Data Sources and Data Preparation for ML
• Learning Ability and Potential Bias
• Considerations for Algorithm Selection
• Machine Learning Refresher
• Course Summary

AWS Certified Machine Learning: Machine Learning in SageMaker

Course: 1 Hour, 32 Minutes

• Course Overview
• Introducing Amazon SageMaker
• Getting Started with SageMaker Studio
• Working with Common Tasks in SageMaker Studio
• Building ML Solution with SageMaker JumpStart
• Linear Learner & XGBoost in SageMaker
• Classifying Images with SageMaker
• Object Detection with SageMaker
• Semantic Segmentation with SageMaker
• How to Perform Model Tuning with SageMaker
• Performing Model Tuning and Training with SageMaker
• Course Summary

AWS Certified Machine Learning: ML Algorithms in SageMaker

Course: 1 Hour, 43 Minutes

• Course Overview
• SageMaker Sequence-to-sequence Algorithm
• Working with BlazingText in SageMaker
• Object to Vector (Object2Vec) in SageMaker
• DeepAR Forecasting in SageMaker
• Working with Random Cut Forest (RCF) in SageMaker
• Topic Modelling in SageMaker
• PCA and Factorization Machine in SageMaker
• K-means Clustering in SageMaker
• K-NN and IP Insights in SageMaker
• Using Image Clustering in SageMaker
• Fundamentals of Reinforcement Learning
• Implementing Reinforcement Learning in SageMaker
• Monitoring & Analyzing Training Jobs using Metrics
• Course Summary

AWS Certified Machine Learning: Advanced SageMaker Functionality

Course: 1 Hour, 29 Minutes

• Course Overview
• Amazon SageMaker Supported Frameworks
• Training Keras/Tensorflow model in SageMaker
• Connecting Amazon EMR with SageMaker Notebooks
• Using SageMaker for Incremental Spot Training
• Distributed Training in SageMaker
• Tackling Distributed Training with PyTorch
• Working with SageMaker Autopilot
• Working with SageMaker Debugger
• Working with SageMaker Experiments
• Building ML Explainability in SageMaker
• Performing Bias Detection in SageMaker
• Course Summary

AWS Certified Machine Learning: AI/ML Services

Course: 1 Hour, 14 Minutes

• Course Overview
• Working with Amazon Comprehend
• Using Amazon Kendra
• Using Amazon Transcribe
• Working with Amazon Polly
• Exploring Amazon Rekognition
• Using Amazon Personalize
• Working with Amazon Forecast
• Working with Amazon Textract
• Other Amazon AI/ML Services
• Case Studies of AI/ML Services
• Course Summary

AWS Certified Machine Learning: Problem Formulation & Data Collection

Course: 43 Minutes

• Course Overview
• Applications of Machine Learning Solutions
• Business Problem and Success Evaluation Metrics
• Problem Formulation
• Amazon Review Problem Formulation
• Recommender Systems
• Recommender Systems Collaborative Filtering
• Storage Services on AWS: EBS, EFS, and S3
• Reading Data from Amazon S3
• Analyzing Data Readiness and Appropriateness
• Built-in Algorithms in Amazon SageMaker
• Course Summary

AWS Certified Machine Learning: Data Preparation & SageMaker Security

Course: 42 Minutes

• Course Overview
• Working with Summary Statistics
• Performing Visualization
• Data Formats for Amazon SageMaker
• Converting a Dataset to Sparse Matrix
• Creating S3 Buckets
• Labelling Data with Amazon Ground Truth
• SageMaker Security: Encryption
• Exploring Amazon SageMaker Security
• Characteristics and Advantages of Amazon CloudWatch
• Characteristics and Advantages of Amazon CloudTrail
• Course Summary

AWS Certified Machine Learning: Model Training & Evaluation

Course: 36 Minutes

• Course Overview
• Factorization Machine
• EC2 Instances
• Training with EC2 Instances
• Training with EC2 Spot Instances
• Evaluating Machine Learning Models
• Deploying Machine Learning Models
• Monitoring Machine Learning Models
• Working with Feature Engineering
• Performing Hyperparameter Tuning
• Using Production Variants
• Course Summary

AWS Certified Machine Learning: AI Services & SageMaker Applications

Course: 54 Minutes

• Course Overview
• Using Amazon Rekognition
• Working with Amazon Polly
• Exploring Amazon Transcribe and Translate
• Working with Seq2seq in SageMaker
• Working with DeepAR in SageMaker
• Using BlazingText in SageMaker5
• Working with Object2vec in SageMaker
• Working with Object Detection in SageMaker
• Classifying Images in SageMaker
• Performing Semantic Segmentation in SageMaker
• Course Summary