Course Introduction, About the Training Architect, About the Exam
Machine Learning Fundamentals, Artificial Intelligence, What Is Machine Learning?, What Is Deep Learning?
Section Introduction, Machine Learning Lifecycle, Supervised, Unsupervised, and Reinforcement, Optimization, Regularization, Hyperparameters, Validation
Section Introduction, Feature Selection and Engineering, Principal Component Analysis (PCA), Missing and Unbalanced Data, Label and One Hot Encoding, Splitting and Randomization
RecordIO Format
Machine Learning Algorithms, Section Introduction, Logistical Regression, Linear Regression, Support Vector Machines, Decision Trees, Random Forests, K-Means, K-Nearest Neighbour
Latent Dirichlet Allocation (LDA) Algorithm
Deep Learning Algorithms, Section Introduction, Neural Networks, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN)
Model Performance and Optimization, Section Introduction, Confusion Matrix, Sensitivity and Specificity, Accuracy and Precision, ROC/AUC, Gini Impurity, F1 Score
Machine Learning Tools and Frameworks, Section Introduction, Introduction to Jupyter Notebooks, ML and DL Frameworks, TensorFlow, PyTorch, MXNet, Scikit-learn, HANDS-ON LAB Introduction to Jupyter Notebooks (AWS SageMaker), HANDS-ON LAB TensorFlow/Keras Basic Image Classifier (AWS SageMaker), HANDS-ON LAB MXNet Basic Classification (AWS SageMaker)
HANDS-ON LAB Scikit-Learn Random Forest Classifier (AWS SageMaker)
AWS Services, Section Introduction, S3, Glue, Athena, QuickSight, Kinesis, Streams, Firehose, Video, and Analytics, EMR with Spark, EC2 for ML, Amazon ML, HANDS-ON LAB Using Kinesis Data Firehose and Kinesis Data Analytics
AWS Application Services AI/ML, Section Introduction, Amazon Rekognition (Images) Part 1, Amazon Rekognition (Images) Part 2 - the API, Amazon Rekognition (Video), Amazon Polly,
Amazon Transcribe, Amazon Translate, Amazon Comprehend, Amazon Lex, Amazon Service Chaining with AWS Step Functions, HANDS-ON LAB Trigger an AWS Lambda Function from an S3 Event, HANDS-ON LAB Using AWS Step Functions to Manage a Long-Running Process, HANDS-ON LAB Perform Parallel Execution in AWS Step Functions
Introduction, Section Introduction, What is Amazon SageMaker?, The Three Stages, Control (Console/SDK/Notebooks), SageMaker Notebooks
Build, Data Preprocessing, Ground Truth, Preprocessing Image Data (Pinehead NotPinehead), Algorithms
Train, SageMaker Algorithms - Architecture 1, SageMaker Algorithms - Architecture 2, SageMaker Algorithms - Architecture 3, Training an Image Classifier - Part 1 (Pinehead NotPinehead), Training an Image Classifier - Part 2 (Pinehead NotPinehead), Hyperparameter Tuning
Deploy, inference Pipelines, Real-Time and Batch Inference, Deploy an Image Classifier (Pinehead, NotPinehead), Accessing Inference from Apps, Create a custom API for inference - Part 1 (Pinehead NotPinehead), Create a custom API for inference - Part 2 (Pinehead NotPinehead)
Security, Securing SageMaker Notebooks, SageMaker and the VPC
Other AWS Services, Section Introduction, DeepLens - Part 1, DeepLens - Part 2, DeepRacer - Part 1, DeepRacer - Part 2
The Exam, How to Answer Questions, How to Prepare, PRACTICE EXAM AWS Certified Machine Learning-Specialty (MLS-C01) Final Practice Exam
