Machine Learning videos index

Curated videos

Curated videos are a great way to learn about Machine Learning. You can use them one at a time to reinforce what you have learnt from the study guides, or binge watch loads to give you an overview of the subject. Each video has been chosen with care to illustrate a specific topic covered by the study guides. Longer videos have a timeline added so you can zero in on the specific information you need.

Video: AWS Certified Machine Learning – Specialty

Video index

Domain 1 Data Engineering

• 1.1 Create data repositories for machine learning
  • Amazon FSx for Lustre, Persistent Storage Overview
• 1.2 Identify and implement a data-ingestion solution.
  • Amazon Kinesis Data Streams Fundamentals
  • Apache Kafka on AWS
• 1.3 Identify and implement a data-transformation solution
  • Getting started with AWS Glue Data Catalog
  • Getting started with AWS Glue ETL

Domain 2 Exploratory Data Analysis

• 2.1 Sanitize and prepare data for modeling
  • Build Highly Accurate Training Datasets Using Amazon SageMaker Ground Truth
• 2.2 Perform feature engineering
  • PCA main ideas
  • t-SNE, Clearly Explained
• 2.3 Analyze and visualize data for machine learning
  • Making sense of the confusion matrix

Domain 3 Modeling

• 3.1 Frame the business problem
  • Classification and Regression in Machine Learning
  • Making Friends with Machine Learning: Regression
  • Amazon SageMaker’s Built-in Algorithm Webinar Series: Clustering with K Means
  • Neural Networks and Deep Learning
  • What is Reinforcement Learning?
• 3.2 Select the appropriate models
  • Built-in Machine Learning Algorithms with Amazon SageMaker
  • Choose the right machine learning algorithm in Amazon SageMaker
• 3.3 Train the models
  • Gradient Descent
  • Serialization formats: JSON and Protobuf
  • What Is Overfitting in Machine Learning?
  • Machine Learning Overfitting
  • Train Your ML Models Accurately with Amazon SageMaker
  • Machine Learning with Containers and Amazon SageMaker
  • Introduction to Amazon EC2 P3 Instances
  • Machine Learning Models with TensorFlow Using Amazon SageMaker
• 3.4 Tune the models
  • Hyperparameter Tuning with Amazon SageMaker’s Automatic Model Tuning
  • Tune Your ML Models to the Highest Accuracy with Amazon SageMaker Automatic Model Tuning
  • Improve model quality with Amazon SageMaker Automatic Model Tuning
• 3.5 Evaluate the models
  • Detect machine learning model drift in production
  • Machine Learning Model Evaluation Metrics
  • Machine Learning Fundamentals: The Confusion Matrix
  • Machine Learning: Testing and Error Metrics

Domain 4 Machine Learning Implementation and Operations

• 4.1 Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.
  • Deploy Your ML Models to Production at Scale with Amazon SageMaker
  • Amazon ECS: Autoscaling for Containers
  • Amazon EMR Notebooks
• 4.2 Recommend and implement the appropriate machine learning services and features for a given problem.
  • An Overview of AI and Machine Learning Services From AWS
  • Build Intelligent Apps Using AI Services
  • Why TensorFlow?
  • Deep learning with Apache MXNet
• 4.3 Apply basic AWS security practices to machine learning solutions.
  • Secure your workloads with NAT Gateway
  • IAM federation
  • What is an Interface VPC Endpoint and how can I create Interface Endpoint for my VPC?
  • An Overview of Amazon SageMaker Security
• 4.4 Deploy and operationalize machine learning solutions.
  • An introduction to MLOps on Google Cloud
  • Deploy Your ML Models to Production at Scale with Amazon SageMaker
  • Inawisdom: Machine Learning and Automated Model Retraining with SageMaker
  • Introducing Amazon SageMaker Clarify, Bias detection

Whizlab’s AWS Certified Machine Learning Specialty practice exams

Practice Exams with 271 questions, Video Lectures and Hands-on Labs from Whizlabs

Whizlab’s AWS Certified Machine Learning Specialty Practice tests are designed by experts to simulate the real exam scenario. The questions are based on the exam syllabus outlined by official documentation. These practice tests are provided to the candidates to gain more confidence in exam preparation and self-evaluate them against the exam content.

Practice test content

• Free Practice test – 15 questions
• Practice test 1 – 65 questions
• Practice test 2 – 65 questions
• Practice test 3 – 65 questions

Section test content

• Core ML Concepts – 10 questions
• Data Engineering – 11 questions
• Exploratory Data Analysis – 13 questions
• Modeling – 15 questions
• Machine Learning Implementation and Operations – 12 questions

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