Aws Certified Machine Learning – Specialty (Mls-C01) - 2023 by Manifold AI Learning

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

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