AWS Certified Machine Learning Specialty E-Learning Training

Bestel deze unieke E-Learning AWS Certified Machine Learning – Specialty cursus online!
✔️ 1 jaar 24/7 toegang tot interactieve video's, spraakopdrachten, voortgangsbewaking via rapportages en hoofdstuktesten.
✔️ Ontwikkel specialistische vaardigheden in machine learning binnen het AWS-ecosysteem.

Waarom kiezen voor deze opleiding?

Amazon Web Services (AWS) is het toonaangevende cloud computing-platform dat krachtige mogelijkheden biedt aan bedrijven en individuen. Deze training helpt je om AWS-diensten te begrijpen en te benutten om machine learning-oplossingen te ontwikkelen en toe te passen.

De AWS Certified Machine Learning – Specialty cursus biedt:

• Diepgaande kennis over machine learning-diensten binnen AWS, zoals SageMaker.
• Inzicht in cloud computing en hoe je AI- en ML-projecten kunt optimaliseren met AWS-tools.
• Praktische oefeningen om je voor te bereiden op real-life toepassingen en het officiële AWS-certificeringsexamen.
• Toegang tot expertinhoud, zodat je leert hoe je kosten kunt optimaliseren en prestaties kunt maximaliseren in de cloud.

Wie zou moeten deelnemen?

Deze training is geschikt voor:

• Data Scientists die gebruik willen maken van AWS-tools om machine learning-modellen te implementeren.
• AI-engineers die specialistische kennis willen opdoen over AWS-diensten.
• Cloud-professionals die hun vaardigheden willen uitbreiden met AI en machine learning.
• IT-beheerders die AWS willen gebruiken om schaalbare en efficiënte oplossingen te bouwen.
• Beginners in machine learning met een basiskennis van cloud computing en een ambitie om gecertificeerd AWS-specialist te worden.

Demo AWS Certified Machine Learning - Specialty (MLS-C01) Training

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

Ga aan de slag met AWS Certified Machine Learning – Specialty!

✔️ Flexibele training die je toegang geeft tot hoogwaardige inhoud op jouw tempo.
✔️ Bereid je voor op certificering en word een erkende specialist in machine learning binnen AWS.
✔️ Behaal jouw doelen door praktische vaardigheden te ontwikkelen en jouw carrière naar een hoger niveau te tillen.

Bestel nu jouw cursus en begin vandaag nog met jouw reis naar AWS-certificering!