AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty (MLS-C01)
Get ready for your exam by enrolling in our comprehensive training course. This course includes a full set of instructional videos designed to equip you with in-depth knowledge essential for passing the certification exam with flying colors.
What’s included
- 106 : Lectures
- 9h 8m : Duration
$14.99/24.99
Introduction
| Lectures |
|---|
| 1. Course Introduction: What to Expect- 6m |
Data Engineering
| Lectures |
|---|
| 1. Section Intro: Data Engineering- 1m |
| 2. Amazon S3 - Overview- 5m |
| 3. Amazon S3 - Storage Tiers & Lifecycle Rules- 4m |
| 4. Amazon S3 Security- 8m |
| 5. Kinesis Data Streams & Kinesis Data Firehose- 9m |
| 6. Lab 1.1 - Kinesis Data Firehose- 6m |
| 7. Kinesis Data Analytics- 4m |
| 8. Lab 1.2 - Kinesis Data Analytics- 7m |
| 9. Kinesis Video Streams- 3m |
| 10. Kinesis ML Summary- 1m |
| 11. Glue Data Catalog & Crawlers- 3m |
| 12. Lab 1.3 - Glue Data Catalog- 4m |
| 13. Glue ETL- 2m |
| 14. Lab 1.4 - Glue ETL- 6m |
| 15. Lab 1.5 - Athena- 1m |
| 16. Lab 1 - Cleanup- 2m |
| 17. AWS Data Stores in Machine Learning- 3m |
| 18. AWS Data Pipelines- 3m |
| 19. AWS Batch- 2m |
| 20. AWS DMS - Database Migration Services- 2m |
| 21. AWS Step Functions- 3m |
| 22. Full Data Engineering Pipelines- 5m |
Exploratory Data Analysis
| Lectures |
|---|
| 1. Section Intro: Data Analysis- 1m |
| 2. Python in Data Science and Machine Learning- 12m |
| 3. Example: Preparing Data for Machine Learning in a Jupyter Notebook.- 10m |
| 4. Types of Data- 5m |
| 5. Data Distributions- 6m |
| 6. Time Series: Trends and Seasonality- 4m |
| 7. Introduction to Amazon Athena- 5m |
| 8. Overview of Amazon Quicksight- 6m |
| 9. Types of Visualizations, and When to Use Them.- 5m |
| 10. Elastic MapReduce (EMR) and Hadoop Overview- 7m |
| 11. Apache Spark on EMR- 10m |
| 12. EMR Notebooks, Security, and Instance Types- 4m |
| 13. Feature Engineering and the Curse of Dimensionality- 7m |
| 14. Imputing Missing Data- 8m |
| 15. Dealing with Unbalanced Data- 6m |
| 16. Handling Outliers- 9m |
| 17. Binning, Transforming, Encoding, Scaling, and Shuffling- 8m |
| 18. Amazon SageMaker Ground Truth and Label Generation- 4m |
| 19. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 1- 6m |
| 20. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 2- 10m |
| 21. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 3- 14m |
Modeling
| Lectures |
|---|
| 1. Section Intro: Modeling- 2m |
| 2. Introduction to Deep Learning- 9m |
| 3. Convolutional Neural Networks- 12m |
| 4. Recurrent Neural Networks- 11m |
| 5. Deep Learning on EC2 and EMR- 2m |
| 6. Tuning Neural Networks- 5m |
| 7. Regularization Techniques for Neural Networks (Dropout, Early Stopping)- 7m |
| 8. Grief with Gradients: The Vanishing Gradient problem- 4m |
| 9. L1 and L2 Regularization- 3m |
| 10. The Confusion Matrix- 6m |
| 11. Precision, Recall, F1, AUC, and more- 7m |
| 12. Ensemble Methods: Bagging and Boosting- 4m |
| 13. Introducing Amazon SageMaker- 8m |
| 14. Linear Learner in SageMaker- 5m |
| 15. XGBoost in SageMaker- 3m |
| 16. Seq2Seq in SageMaker- 5m |
| 17. DeepAR in SageMaker- 4m |
| 18. BlazingText in SageMaker- 5m |
| 19. Object2Vec in SageMaker- 5m |
| 20. Object Detection in SageMaker- 4m |
| 21. Image Classification in SageMaker- 4m |
| 22. Semantic Segmentation in SageMaker- 4m |
| 23. Random Cut Forest in SageMaker- 3m |
| 24. Neural Topic Model in SageMaker- 3m |
| 25. Latent Dirichlet Allocation (LDA) in SageMaker- 3m |
| 26. K-Nearest-Neighbors (KNN) in SageMaker- 3m |
| 27. K-Means Clustering in SageMaker- 5m |
| 28. Principal Component Analysis (PCA) in SageMaker- 3m |
| 29. Factorization Machines in SageMaker- 4m |
| 30. IP Insights in SageMaker- 3m |
| 31. Reinforcement Learning in SageMaker- 12m |
| 32. Automatic Model Tuning- 6m |
| 33. Apache Spark with SageMaker- 3m |
| 34. Amazon Comprehend- 6m |
| 35. Amazon Translate- 2m |
| 36. Amazon Transcribe- 4m |
| 37. Amazon Polly- 6m |
| 38. Amazon Rekognition- 7m |
| 39. Amazon Forecast- 2m |
| 40. Amazon Lex- 3m |
| 41. The Best of the Rest: Other High-Level AWS Machine Learning Services- 3m |
| 42. Putting them All Together- 2m |
| 43. Lab: Tuning a Convolutional Neural Network on EC2, Part 1- 9m |
| 44. Lab: Tuning a Convolutional Neural Network on EC2, Part 2- 9m |
| 45. Lab: Tuning a Convolutional Neural Network on EC2, Part 3- 6m |
ML Implementation and Operations
| Lectures |
|---|
| 1. Section Intro: Machine Learning Implementation and Operations- 1m |
| 2. SageMaker's Inner Details and Production Variants- 11m |
| 3. SageMaker On the Edge: SageMaker Neo and IoT Greengrass- 4m |
| 4. SageMaker Security: Encryption at Rest and In Transit- 5m |
| 5. SageMaker Security: VPC's, IAM, Logging, and Monitoring- 4m |
| 6. SageMaker Resource Management: Instance Types and Spot Training- 4m |
| 7. SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's- 5m |
| 8. SageMaker Inference Pipelines- 2m |
| 9. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1- 5m |
| 10. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 2- 11m |
| 11. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 3- 12m |
Wrapping Up
| Lectures |
|---|
| 1. Section Intro: Wrapping Up- 1m |
| 2. More Preparation Resources- 6m |
| 3. Test-Taking Strategies, and What to Expect- 10m |
| 4. You Made It!- 1m |
| 5. Save 50% on your AWS Exam Cost!- 2m |
| 6. Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only- 1m |
