Aws Certified Machine Learning – Specialty (Mls-C01) 2023 By Manifold AI Learning
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Published 5/2023
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AWS Certified Machine Learning – Specialty (MLS-C01) - 2023 ,Sagemaker , AWS MLOps, Data Engineering, Exam Ready Updated
Free Download What you'll learn
Select and justify the appropriate ML approach for a given business problem
Identify appropriate AWS services to implement ML solutions
Design and implement scalable, cost-optimized, reliable, and secure ML solutions
The ability to express the intuition behind basic ML algorithms
Performing hyperparameter optimisation
Machine Learning and deep learning frameworks
The ability to follow model-training best practices
The ability to follow deployment best practices
The ability to follow operational best practices
Requirements
Basic knowledge of AWS
Basic knowledge of Python Programming
Basic understanding of Data Science
Basic knowledge of Machine Learning
Description
The AWS Certified Machine Learning – Specialty (MLS-C01) exam is intended for individuals who perform an artificial intelligence/machine learning (AI/ML) development or data science role. This exam validates a candidate's ability to design, build, deploy, optimize, train, tune, and maintain ML solutions for given business problems by using the AWS Cloud.Implement ML Ops Starategy on cloud with AWSAccording to AWS, below are the tasks where candidate's ability is validated:· Select and justify the appropriate ML approach for a given business problem· Identify appropriate AWS services to implement ML solutions· Design and implement scalable, cost-optimized, reliable, and secure ML solutions.Also, Candidates are expected to have below skillset :· The ability to express the intuition behind basic ML algorithms· Experience performing basic hyperparameter optimisation· Experience with ML and deep learning frameworks· The ability to follow model-training best practices· The ability to follow deployment best practices· The ability to follow operational best practicesAnd the Certification examination is designed and split to validate the candidate's expertise in 4 Domains :1. Domain 1: Data Engineering 20% Weightage2. Domain 2: Exploratory Data Analysis 24% Weightage3. Domain 3: Modeling 36% Weightage4. Domain 4: Machine Learning Implementation and Operations 20%In our certification learning journey of this course, we will follow the same pattern, and cover the topics in a Sequential and logical way so that, as a practitioner, you can excel on the certification examination.Domain 1: Data Engineering· Create data repositories for machine learning. ·o Identify data sources (e.g., content and location, primary sources such as user data)o Determine storage mediums (e.g., DB, Data Lake, S3, EFS, EBS)· Identify and implement a data ingestion solution.o Data job styles/types (batch load, streaming)o Data ingestion pipelines (Batch-based ML workloads and streaming-based ML workloads)§ Kinesis§ Kinesis Analytics§ Kinesis Firehose§ EMR§ Glueo Job Scheduling· Identify and implement a data transformation solution.o Transforming data transit (ETL: Glue, EMR, AWS Batch)o Handle ML-specific data using map-reduce (Hadoop, Spark, Hive)Domain 2 : Exploratory Data Analysis· Sanitize and prepare data for modeling.o Identify and handle missing data, corrupt data, stop words, etc.o Formatting, normalizing, augmenting, and scaling datao Labeled data (recognizing when you have enough labeled data and identifying mitigation strategies[Data labeling tools (Mechanical Turk, manual labor)])· Perform feature engineering.o Identify and extract features from data sets, including from data sources such as text, speech, image, public datasets, etc.o Analyze/evaluate feature engineering concepts (binning, tokenization, outliers, synthetic features, 1 hot encoding, reducing dimensionality of data) 2.3· Analyze and visualize data for machine learning.o Graphing (scatter plot, time series, histogram, box plot)o Interpreting descriptive statistics (correlation, summary statistics, p value)o Clustering (hierarchical, diagnosing, elbow plot, cluster size)Domain 3 : Modeling· Frame business problems as machine learning problems.o Determine when to use/when not to use MLo Know the difference between supervised and unsupervised learningo Selecting from among classification, regression, forecasting, clustering, recommendation, etc.· Select the appropriate model(s) for a given machine learning problem.o Xgboost, logistic regression, K-means, linear regression, decision trees, random forests, RNN, CNN, Ensemble, Transfer learningo Express intuition behind models· Train machine learning models.o Train validation test split, cross-validationo Optimizer, gradient descent, loss functions, local minima, convergence, batches, probability, etc.o Compute choice (GPU vs. CPU, distributed vs. non-distributed, platform[Spark vs. non-Spark])o Model updates and retraining§ Batch vs. real-time/online· Perform hyperparameter optimization.o Regularization§ Drop out§ L1/L2o Cross validationo Model initializationo Neural network architecture (layers/nodes), learning rate, activation functionso Tree-based models (# of trees, # of levels)o Linear models (learning rate)· Evaluate machine learning models.o Avoid overfitting/underfitting (detect and handle bias and variance)o Metrics (AUC-ROC, accuracy, precision, recall, RMSE, F1 score)o Confusion matrixo Offline and online model evaluation, A/B testingo Compare models using metrics (time to train a model, quality of model, engineering costs)o Cross validationDomain 4: Machine Learning Implementation and Operations· Build machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance.o AWS environment logging and monitoring§ CloudTrail and CloudWatch§ Build error monitoringo Multiple regions, Multiple AZso AMI/golden imageo Docker containerso Auto Scaling groupso Rightsizing§ Instances§ Provisioned IOPS§ Volumeso Load balancingo AWS best practices· Recommend and implement the appropriate machine learning services and features for a given problem.o ML on AWS (application services)§ Poly o Lex o Transcribeo AWS service limitso Build your own model vs. SageMaker built-in algorithmso Infrastructure: (spot, instance types), cost considerations§ Using spot instances to train deep learning models using AWS Batch· Apply basic AWS security practices to machine learning solutions.o IAMo S3 bucket policieso Security groupso VPCo Encryption/anonymization· Deploy and operationalize machine learning solutions.o Exposing endpoints and interacting with themo ML model versioningo A/B testingo Retrain pipelineso ML debugging/troubleshooting§ Detect and mitigate drop in performance o Monitor performance of the modeBelow are the Tools, Technologies and Concepts covered as part of this examination:· Ingestion/Collection· Processing/ETL· Data analysis/visualization· Model training· Model deployment/inference· Operational· AWS ML application services· Language relevant to ML (Python)· Notebooks and integrated development environments (IDEs)AWS services and features Analytics:· Amazon Athena· Amazon EMR· Amazon Kinesis Data Analytics· Amazon Kinesis Data Firehose· Amazon Kinesis Data Streams· Amazon QuickSightCompute:· AWS Batch· Amazon EC2Containers:· Amazon Elastic Container Registry (Amazon ECR)· Amazon Elastic Container Service (Amazon ECS)· Amazon Elastic Kubernetes Service (Amazon EKS)Database:· AWS Glue· Amazon RedshiftInternet of Things (IoT):· AWS IoT Greengrass VersionMachine Learning:· Amazon Comprehend· AWS Deep Learning AMIs (DLAMI)· AWS DeepLens· Amazon Forecast· Amazon Fraud Detector· Amazon Lex· Amazon Polly· Amazon Rekognition· Amazon SageMaker· Amazon Textract· Amazon Transcribe· Amazon TranslateManagement and Governance:· AWS CloudTrail· Amazon CloudWatchNetworking and Content Delivery:· Amazon VPC Security, Identity, and Compliance:· AWS Identity and Access Management (IAM)Serverless:· AWS Fargate· AWS LambdaStorage:· Amazon Elastic File System (Amazon EFS)· Amazon FSx· Amazon S3
Overview
Section 1: About Certification Exam & Course
Lecture 1 About the Course Instructor & Best Practices to Succeed
Lecture 2 Checklist of Domain 1 : Data Engineering
Section 2: Domain 1 : Data Engineering
Lecture 3 Domain 1 - Hands On Attachment Files
Lecture 4 Introduction to Data Engineering & Data Ingestion Tools
Lecture 5 Data Engineering Tools
Lecture 6 Working with S3 and Storage Classes
Lecture 7 Creating the S3 Bucket from Console
Lecture 8 Setting up the AWS CLI
Lecture 9 Create Bucket from AWS CLI & Lifecycle Events
Lecture 10 S3 - Intelligent Tiering Hands On
Lecture 11 Cleanup - Activity 2
Lecture 12 S3 - Data Replication for Recovery Point
Lecture 13 Security Best Practices and Guidelines for Amazon S3
Lecture 14 Introduction to Amazon Kinesis Service
Lecture 15 Ingest Streaming data using Kinesis Stream - Hands On
Lecture 16 Build a streaming system with Amazon Kinesis Data Streams- Hands On
Lecture 17 Streaming data to Amazon S3 using Kinesis Data Firehose - Hands On
Lecture 18 Hands On Generate Kinesis Data Analytics
Lecture 19 Work with Amazon Kinesis Data Stream and Kinesis Agent
Lecture 20 Understanding AWS Glue
Lecture 21 Discover the Metadata using AWS Glue Crawlers
Lecture 22 Data Transformation wth AWS Glue DataBrew
Lecture 23 Perform ETL operation in Glue with S3
Lecture 24 Understanding Athena
Lecture 25 Querying S3 data using Amazon Athena
Lecture 26 Understanding AWS Batch
Lecture 27 Data Engineering with AWS Step
Lecture 28 Working with AWS Step Functions
Lecture 29 Create Serverless workflow with AWS Step
Lecture 30 Working with states in AWS Step function
Lecture 31 Machine Learning and AWS Step Functions
Lecture 32 Feature Engineering with AWS Step and AWS Glue
Lecture 33 Summary and Key topics to Focus on Module 1
Section 3: Domain 2 : Exploratory Data Analysis
Lecture 34 Domain 2 - Hands On Attachment Files
Lecture 35 Introduction to Exploratory Data Analysis
Lecture 36 Hands On EDA
Lecture 37 Types of Data & the respective analysis
Lecture 38 Statistical Analysis
Lecture 39 Descriptive Statistics - Understanding the Methods
Lecture 40 Definition of Outlier
Lecture 41 EDA Hands on - Data Acquisition & Data Merging
Lecture 42 EDA Hands on - Outlier Analysis and Duplicate Value Analysis
Lecture 43 Missing Value Analysis
Lecture 44 Fixing the Errors/Typos in dataset
Lecture 45 Data Transformation
Lecture 46 Dealing with Categorical Data
Lecture 47 Scaling the Numerical data
Lecture 48 Visualization Methods for EDA
Lecture 49 Imbalanced Dataset
Lecture 50 Dimensionality Reduction - PCA
Lecture 51 Dimensionality Reduction - LDA
Lecture 52 Amazon QuickSight
Lecture 53 Apache Spark - EMR
Section 4: Domain 3 : Modelling
Lecture 54 Domain 3 - Hands On Attachment files
Lecture 55 Introduction to Domain 3 - Modelling
Lecture 56 Introduction to Machine Learning
Lecture 57 Types of Machine Learning
Lecture 58 Linear Regression & Evaluation Functions
Lecture 59 Regularization and Assumptions of Linear Regression
Lecture 60 Logistic Regression
Lecture 61 Gradient Descent
Lecture 62 Logistic Regression Implementation and EDA
Lecture 63 Evaluation Metrics for Classification
Lecture 64 Decision Tree Algorithms
Lecture 65 Loss Functions of Decision Trees
Lecture 66 Decision Tree Algorithm Implementation
Lecture 67 Overfit Vs Underfit - Kfold Cross validation
Lecture 68 Hyperparameter Optimization Techniques
Lecture 69 Quick Check-in on the Syllabus
Lecture 70 KNN Algorithm
Lecture 71 SVM Algorithm
Lecture 72 Ensemble Learning - Voting Classifier
Lecture 73 Ensemble Learning - Bagging Classifier & Random Forest
Lecture 74 Ensemble Learning - Boosting Adabost and Gradient Boost
Lecture 75 Emsemble Learning XGBoost
Lecture 76 Clustering - Kmeans
Lecture 77 Clustering - Hierarchial Clustering
Lecture 78 Clustering - DBScan
Lecture 79 Time Series Analysis
Lecture 80 ARIMA Hands On
Lecture 81 Reccommendation Amazon Personalize
Lecture 82 Introduction to Deep Learning
Lecture 83 Introduction to Tensorflow & Create first Neural Network
Lecture 84 Intuition of Deep Learning Training
Lecture 85 Activation Function
Lecture 86 Architecture of Neural Networks
Lecture 87 Deep Learning Model Training. - Epochs - Batch Size
Lecture 88 Hyperparameter Tuning in Deep Learning
Lecture 89 Vanshing & Exploding Gradients - Initializations, Regularizations
Lecture 90 Introduction to Convolutional Neural Networks
Lecture 91 Implementation of CNN on CatDog Dataset
Lecture 92 Transfer Learning for Computer Vision
Lecture 93 Feed Forward Neural Network Challenges
Lecture 94 RNN & Types of Architecture
Lecture 95 LSTM Architecture
Lecture 96 Attention Mechanism
Lecture 97 Transfer Learning for Natural Language Data
Lecture 98 Transformer Architecture Overview
Section 5: Domain 4 : Machine Learning Implementation and Operations
Lecture 99 Domain 4 - Attachment Files
Lecture 100 Introduction to Domain 4 - Machine Learning Implementation and Operations
Lecture 101 Serverless AWS Lambda - Part 1
Lecture 102 Introduction to Docker & Creating the Dockerfile
Lecture 103 Serverless AWS Lambda - Part 2
Lecture 104 Cloudwatch
Lecture 105 End to End Deployment with AWS Sagemaker End Point
Lecture 106 AWS Sagemaker JumpStart
Lecture 107 AWS Polly
Lecture 108 AWS Transcribe
Lecture 109 AWS Lex
Lecture 110 Retrain Pipelines
Lecture 111 Model Lineage in Machine Learning
Lecture 112 Amazon Augmented AI
Lecture 113 Amazon CodeGuru
Lecture 114 Amazon Comprehend & Amazon Comprehend Medical
Lecture 115 AWS DeepComposer
Lecture 116 AWS DeepLens
Lecture 117 AWS DeepRacer
Lecture 118 Amazon DevOps Guru
Lecture 119 Amazon Forecast
Lecture 120 Amazon Fraud Detector
Lecture 121 Amazon HealthLake
Lecture 122 Amazon Kendra
Lecture 123 Amazon Lookout for equipment , Metrics & Vision
Lecture 124 Amazon Monitron
Lecture 125 AWS Panorama
Lecture 126 Amazon Rekognition
Lecture 127 Amazon Translate
Lecture 128 Amazon Textract
Lecture 129 Next Steps
Section 6: Machine Learning for Projects
Lecture 130 ML Deployment Files
Lecture 131 Machine learning Deployment Part 1 - Model Prep - End to End
Lecture 132 Machine learning Deployment Part 2 - Deploy Flask App - End to End
Lecture 133 Streamlit Tutorial
Section 7: Optional Topics for Additional Learning - Text Analytics
Lecture 134 Note to Learners on this section
Lecture 135 Attachment for NLP Pipeline
Lecture 136 NLP Pipeline
Lecture 137 Data Extraction and Text Cleaning hands On
Lecture 138 Introduction to NLTK library
Lecture 139 Tokenization , bigrams, trigrams, and N gram - Hands on
Lecture 140 POS Tagging & Stop Words Removal
Lecture 141 Stemming & Lemmatization
Lecture 142 NER and Wordsense Ambiguation
Lecture 143 Introduction to Spacy Library
Lecture 144 Hands On Spacy
Lecture 145 Summary
Lecture 146 NLP Attachment 2
Lecture 147 Vector Representation of Text - One Hot Encoding
Lecture 148 Understanding BoW Technique
Lecture 149 BoW Hands On
Lecture 150 Text Representation : TF-IDF
Lecture 151 TF-IDF Hands On
Lecture 152 Introduction to Word Embeddings
Lecture 153 TF-IDF Hands On
Lecture 154 Understanding the Importance of Vectors - Intuition
Lecture 155 Hands On Word Embeddings - Usage of Pre-trained models
Lecture 156 Skip-gram Word Embeddings - Understanding Data Preperation
Lecture 157 Skip Gram Model Architecture
Lecture 158 Skip Gram Implementation from Scratch
Lecture 159 CBOW Model Architecture & Hands On
Lecture 160 Hyperparameters - Negative Sampling and Sub Sampling
Lecture 161 Practical Difference between CBOW and Skip-gram
Section 8: Optional Topics for Additional Learning - Inferential Statistics
Lecture 162 Source code for Inferential Statistics
Lecture 163 Introduction to Inferential Statistics
Lecture 164 Key Terminology of Inferential Statistics
Lecture 165 Hands On - Population & Sample
Lecture 166 Types of Statistical Inference
Lecture 167 Confidence Interval - Margin of Error - Confidence Interval Estimation - Constru
Lecture 168 Demo - Margin of Error and Confidence Interval
Lecture 169 Hypothesis Testing & Steps of Hypothesis testing
Lecture 170 ZTest and Example Problem
Lecture 171 ZTest Solution Hands On
Section 9: APPENDIX - Other References for Learners
Lecture 172 Linux Basics
Anyone interested in AWS cloud-based machine learning and data science,Anyone preparing for AWS Certified Machine Learning - Specialty Examination,Anyone looking to learn the best practices to deploy the Machine Learning Models on Cloud
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