AWS Certified Machine Learning – Specialty (MLS-C01) – 2023

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AWS Certified Machine Learning – Specialty (MLS-C01) - 2023 ,Sagemaker , AWS MLOps, Data Engineering, Exam Ready Updated

1 - About Certification Exam Course

1 - About the Course Instructor Best Practices to Succeed

2 - Checklist of Domain 1 Data Engineering

2 - Domain 1 Data Engineering

3 - Domain 1 Hands On Attachment Files.html

3 - Domain-1-Data-Engineering.zip

4 - Introduction to Data Engineering Data Ingestion Tools

5 - Data Engineering Tools

6 - Working with S3 and Storage Classes

7 - Creating the S3 Bucket from Console

8 - Setting up the AWS CLI

9 - Create Bucket from AWS CLI Lifecycle Events

10 - S3 Intelligent Tiering Hands On

11 - Cleanup Activity 2

12 - S3 Data Replication for Recovery Point

13 - Security Best Practices and Guidelines for Amazon S3

14 - Introduction to Amazon Kinesis Service

15 - Ingest Streaming data using Kinesis Stream Hands On

16 - Build a streaming system with Amazon Kinesis Data Streams Hands On

17 - Streaming data to Amazon S3 using Kinesis Data Firehose Hands On

18 - Hands On Generate Kinesis Data Analytics

19 - Work with Amazon Kinesis Data Stream and Kinesis Agent

20 - Understanding AWS Glue

21 - Discover the Metadata using AWS Glue Crawlers

22 - Data Transformation wth AWS Glue DataBrew

23 - Perform ETL operation in Glue with S3

24 - Understanding Athena

25 - Querying S3 data using Amazon Athena

26 - Understanding AWS Batch

27 - Data Engineering with AWS Step

28 - Working with AWS Step Functions

29 - Create Serverless workflow with AWS Step

30 - Working with states in AWS Step function

31 - Machine Learning and AWS Step Functions

32 - Feature Engineering with AWS Step and AWS Glue

33 - Summary and Key topics to Focus on Module 1

3 - Domain 2 Exploratory Data Analysis

34 - Domain 2 Hands On Attachment Files.html

34 - Domain-2-EDA.zip

35 - Introduction to Exploratory Data Analysis

36 - Hands On EDA

37 - Types of Data the respective analysis

38 - Statistical Analysis

39 - Descriptive Statistics Understanding the Methods

40 - Definition of Outlier

41 - EDA Hands on Data Acquisition Data Merging

42 - EDA Hands on Outlier Analysis and Duplicate Value Analysis

43 - Missing Value Analysis

44 - Fixing the ErrorsTypos in dataset

45 - Data Transformation

46 - Dealing with Categorical Data

47 - Scaling the Numerical data

48 - Visualization Methods for EDA

49 - Imbalanced Dataset

50 - Dimensionality Reduction PCA

51 - Dimensionality Reduction LDA

52 - Amazon QuickSight

53 - Apache Spark EMR

4 - Domain 3 Modelling

54 - Domain 3 Hands On Attachment files.html

54 - Domain-3-Modeling.zip

55 - Introduction to Domain 3 Modelling

56 - Introduction to Machine Learning

57 - Types of Machine Learning

58 - Linear Regression Evaluation Functions

59 - Regularization and Assumptions of Linear Regression

60 - Logistic Regression

61 - Gradient Descent

62 - Logistic Regression Implementation and EDA

63 - Evaluation Metrics for Classification

64 - Decision Tree Algorithms

65 - Loss Functions of Decision Trees

66 - Decision Tree Algorithm Implementation

67 - Overfit Vs Underfit Kfold Cross validation

68 - Hyperparameter Optimization Techniques

69 - Quick Checkin on the Syllabus

70 - KNN Algorithm

71 - SVM Algorithm

72 - Ensemble Learning Voting Classifier

73 - Ensemble Learning Bagging Classifier Random Forest

74 - Ensemble Learning Boosting Adabost and Gradient Boost

75 - Emsemble Learning XGBoost

76 - Clustering Kmeans

77 - Clustering Hierarchial Clustering

78 - Clustering DBScan

79 - Time Series Analysis

80 - ARIMA Hands On

81 - Reccommendation Amazon Personalize

82 - Introduction to Deep Learning

83 - Introduction to Tensorflow Create first Neural Network

84 - Intuition of Deep Learning Training

85 - Activation Function

86 - Architecture of Neural Networks

87 - Deep Learning Model Training Epochs Batch Size

88 - Hyperparameter Tuning in Deep Learning

89 - Vanshing Exploding Gradients Initializations Regularizations

90 - Introduction to Convolutional Neural Networks

91 - Implementation of CNN on CatDog Dataset

92 - Transfer Learning for Computer Vision

93 - Feed Forward Neural Network Challenges

94 - RNN Types of Architecture

95 - LSTM Architecture

96 - Attention Mechanism

97 - Transfer Learning for Natural Language Data

98 - Transformer Architecture Overview

5 - Domain 4 Machine Learning Implementation and Operations

99 - Domain 4 Attachment Files.html

99 - Domain-4-Machine-learning-Deployment.zip

100 - Introduction to Domain 4 Machine Learning Implementation and Operations

101 - Serverless AWS Lambda Part 1

102 - Introduction to Docker Creating the Dockerfile

103 - Serverless AWS Lambda Part 2

104 - Cloudwatch

105 - End to End Deployment with AWS Sagemaker End Point

106 - AWS Sagemaker JumpStart

107 - AWS Polly

108 - AWS Transcribe

109 - AWS Lex

110 - Retrain Pipelines

111 - Model Lineage in Machine Learning

112 - Amazon Augmented AI

113 - Amazon CodeGuru

114 - Amazon Comprehend Amazon Comprehend Medical

115 - AWS DeepComposer

116 - AWS DeepLens

117 - AWS DeepRacer

118 - Amazon DevOps Guru

119 - Amazon Forecast

120 - Amazon Fraud Detector

121 - Amazon HealthLake

122 - Amazon Kendra

123 - Amazon Lookout for equipment Metrics Vision

124 - Amazon Monitron

125 - AWS Panorama

126 - Amazon Rekognition

127 - Amazon Translate

128 - Amazon Textract

129 - Next Steps

6 - Machine Learning for Projects

130 - ML Deployment Files.html

130 - end-to-end-deployment-v1.zip

131 - Machine learning Deployment Part 1 Model Prep End to End

132 - Machine learning Deployment Part 2 Deploy Flask App End to End

133 - Streamlit Tutorial

133 - app.zip

7 - Optional Topics for Additional Learning Text Analytics

134 - Note to Learners on this section.html

135 - Attachment for NLP Pipeline.html

135 - NLP-Attachment-1.zip

136 - NLP Pipeline

137 - Data Extraction and Text Cleaning hands On

138 - Introduction to NLTK library

139 - Tokenization bigrams trigrams and N gram Hands on

140 - POS Tagging Stop Words Removal

141 - Stemming Lemmatization

142 - NER and Wordsense Ambiguation

143 - Introduction to Spacy Library

144 - Hands On Spacy

145 - Summary

146 - NLP Attachment 2.html

146 - NLP-Attachment-2.zip

147 - Vector Representation of Text One Hot Encoding

148 - Understanding BoW Technique

149 - BoW Hands On

150 - Text Representation TFIDF

151 - TFIDF Hands On

152 - Introduction to Word Embeddings

153 - TFIDF Hands On

154 - Understanding the Importance of Vectors Intuition

155 - Hands On Word Embeddings Usage of Pretrained models

156 - Skipgram Word Embeddings Understanding Data Preperation

157 - Skip Gram Model Architecture

158 - Skip Gram Implementation from Scratch

159 - CBOW Model Architecture Hands On

160 - Hyperparameters Negative Sampling and Sub Sampling

161 - Practical Difference between CBOW and Skipgram

8 - Optional Topics for Additional Learning Inferential Statistics

162 - Inferential-Statistics-Attachment.zip

162 - Source code for Inferential Statistics.html

163 - Introduction to Inferential Statistics

164 - Key Terminology of Inferential Statistics

165 - Hands On Population Sample

166 - Types of Statistical Inference

167 - Confidence Interval Margin of Error Confidence Interval Estimation Constru

168 - Demo Margin of Error and Confidence Interval

169 - Hypothesis Testing Steps of Hypothesis testing

170 - ZTest and Example Problem

171 - ZTest Solution Hands On

9 - APPENDIX Other References for Learners

172 - Linux Basics

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