AWS Certified Machine Learning - Specialty: AWS Certified Machine Learning - Specialty (MLS-C01)

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$14.99 / $24.99

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.

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Video Courses

Introduction

Lectures	Duration
1. Course Introduction: What to Expect	6m

1. Course Introduction: What to Expect

Data Engineering

Lectures	Duration
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

1. Section Intro: Data Engineering

2. Amazon S3 - Overview

3. Amazon S3 - Storage Tiers & Lifecycle Rules

4. Amazon S3 Security

5. Kinesis Data Streams & Kinesis Data Firehose

6. Lab 1.1 - Kinesis Data Firehose

7. Kinesis Data Analytics

8. Lab 1.2 - Kinesis Data Analytics

9. Kinesis Video Streams

10. Kinesis ML Summary

11. Glue Data Catalog & Crawlers

12. Lab 1.3 - Glue Data Catalog

13. Glue ETL

14. Lab 1.4 - Glue ETL

15. Lab 1.5 - Athena

16. Lab 1 - Cleanup

17. AWS Data Stores in Machine Learning

18. AWS Data Pipelines

19. AWS Batch

20. AWS DMS - Database Migration Services

21. AWS Step Functions

22. Full Data Engineering Pipelines

Exploratory Data Analysis

Lectures	Duration
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

1. Section Intro: Data Analysis

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

5. Data Distributions

6. Time Series: Trends and Seasonality

7. Introduction to Amazon Athena

8. Overview of Amazon Quicksight

9. Types of Visualizations, and When to Use Them.

10. Elastic MapReduce (EMR) and Hadoop Overview

11. Apache Spark on EMR

10m

12. EMR Notebooks, Security, and Instance Types

13. Feature Engineering and the Curse of Dimensionality

14. Imputing Missing Data

15. Dealing with Unbalanced Data

16. Handling Outliers

17. Binning, Transforming, Encoding, Scaling, and Shuffling

18. Amazon SageMaker Ground Truth and Label Generation

19. Lab: Preparing Data for TF-IDF with Spark and EMR, Part 1

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	Duration
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

1. Section Intro: Modeling

2. Introduction to Deep Learning

3. Convolutional Neural Networks

12m

4. Recurrent Neural Networks

11m

5. Deep Learning on EC2 and EMR

6. Tuning Neural Networks

7. Regularization Techniques for Neural Networks (Dropout, Early Stopping)

8. Grief with Gradients: The Vanishing Gradient problem

9. L1 and L2 Regularization

10. The Confusion Matrix

11. Precision, Recall, F1, AUC, and more

12. Ensemble Methods: Bagging and Boosting

13. Introducing Amazon SageMaker

14. Linear Learner in SageMaker

15. XGBoost in SageMaker

16. Seq2Seq in SageMaker

17. DeepAR in SageMaker

18. BlazingText in SageMaker

19. Object2Vec in SageMaker

20. Object Detection in SageMaker

21. Image Classification in SageMaker

22. Semantic Segmentation in SageMaker

23. Random Cut Forest in SageMaker

24. Neural Topic Model in SageMaker

25. Latent Dirichlet Allocation (LDA) in SageMaker

26. K-Nearest-Neighbors (KNN) in SageMaker

27. K-Means Clustering in SageMaker

28. Principal Component Analysis (PCA) in SageMaker

29. Factorization Machines in SageMaker

30. IP Insights in SageMaker

31. Reinforcement Learning in SageMaker

12m

32. Automatic Model Tuning

33. Apache Spark with SageMaker

34. Amazon Comprehend

35. Amazon Translate

36. Amazon Transcribe

37. Amazon Polly

38. Amazon Rekognition

39. Amazon Forecast

40. Amazon Lex

41. The Best of the Rest: Other High-Level AWS Machine Learning Services

42. Putting them All Together

43. Lab: Tuning a Convolutional Neural Network on EC2, Part 1

44. Lab: Tuning a Convolutional Neural Network on EC2, Part 2

45. Lab: Tuning a Convolutional Neural Network on EC2, Part 3

ML Implementation and Operations

Lectures	Duration
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

1. Section Intro: Machine Learning Implementation and Operations

2. SageMaker's Inner Details and Production Variants

11m

3. SageMaker On the Edge: SageMaker Neo and IoT Greengrass

4. SageMaker Security: Encryption at Rest and In Transit

5. SageMaker Security: VPC's, IAM, Logging, and Monitoring

6. SageMaker Resource Management: Instance Types and Spot Training

7. SageMaker Resource Management: Elastic Inference, Automatic Scaling, AZ's

8. SageMaker Inference Pipelines

9. Lab: Tuning, Deploying, and Predicting with Tensorflow on SageMaker - Part 1

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	Duration
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

1. Section Intro: Wrapping Up

2. More Preparation Resources

3. Test-Taking Strategies, and What to Expect

10m

4. You Made It!

5. Save 50% on your AWS Exam Cost!

6. Get an Extra 30 Minutes on your AWS Exam - Non Native English Speakers only