Aws Certified Machine Learning Specialty 2024 - Mastery
Last updated 7/2024
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Upgrade with AWS Certified Machine Learning Specialty and Master Machine Learning on AWS to clear Examination

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

Prepare for the AWS Certified Machine Learning – Specialty (MLS-C01) exam in 2024 with our comprehensive and updated course. Dive deep into machine learning concepts and applications on the AWS platform, equipping yourself with the skills needed to excel in real-world scenarios. Master techniques, data preprocessing, and utilize popular AWS services such as Amazon SageMaker, AWS Lambda, AWS Glue, and more.Our structured learning journey aligns with the exam's domains, ensuring thorough preparation for certification success and practical application of machine learning principles.Key Skills and Topics Covered:Choose and justify ML approaches for business problemsIdentify and implement AWS services for ML solutionsDesign scalable, cost-optimized, reliable, and secure ML solutionsSkillset requirements: ML algorithms intuition, hyperparameter optimization, ML frameworks, model-training, deployment, and operational best practicesDomains and Weightage:Data Engineering (20%): Create data repositories, implement data ingestion, and transformation solutions using AWS services like Kinesis, EMR, and Glue.Exploratory Data Analysis (24%): Sanitize and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.Modeling (36%): Frame business problems, select appropriate models, train models, perform hyperparameter optimization, and evaluate ML models using various metrics.Machine Learning Implementation and Operations (20%): Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services like CloudWatch, SageMaker, and security best practices.Detailed Learning Objectives:Data Engineering: Create data repositories, implement data ingestion and transformation solutions using AWS services like Kinesis, EMR, and Glue.Exploratory Data Analysis: Sanitize and prepare data, perform feature engineering, and analyze/visualize data for ML using techniques such as clustering and descriptive statistics.Modeling: Frame business problems, select appropriate models, train models, perform hyperparameter optimization, and evaluate ML models using various metrics.ML Implementation and Operations: Build ML solutions for performance, availability, scalability, and fault tolerance using AWS services like CloudWatch, SageMaker, and security best practices.Tools, Technologies, and Concepts Covered:Ingestion/Collection, Processing/ETL, Data analysis/visualization, Model training, Model deployment/inference, OperationalAWS ML application services, Python language for ML, Notebooks/IDEsAWS Services Covered:Analytics: Amazon Athena, Amazon EMR, Amazon QuickSight, etc.Compute: AWS Batch, Amazon EC2, etc.Containers: Amazon ECR, Amazon ECS, Amazon EKS, etc.Database: AWS Glue, Amazon Redshift, etc.IoT: AWS IoT GreengrassMachine Learning: Amazon SageMaker, AWS Deep Learning AMIs, Amazon Comprehend, etc.Management and Governance: AWS CloudTrail, Amazon CloudWatch, etc.Networking and Content Delivery, Security, Identity, and Compliance: Various AWS services.Serverless: AWS Fargate, AWS LambdaStorage: Amazon S3, Amazon EFS, Amazon FSxFor the learners who are new to AWS, we have also added basic tutorials to get it up and running.Unlock unlimited potential in 2024! Master AI-powered insights on AWS with our Machine Learning Specialty course. Get certified and elevate your career!

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

Lecture 3 Command Line Interface Setup for Windows Users

Section 2: Domain 1 : Data Engineering

Lecture 4 Domain 1 - Hands On Attachment Files

Lecture 5 Introduction to Data Engineering & Data Ingestion Tools

Lecture 6 Data Engineering Tools

Lecture 7 Working with S3 and Storage Classes

Lecture 8 Creating the S3 Bucket from Console

Lecture 9 Setting up the AWS CLI

Lecture 10 Create Bucket from AWS CLI & Lifecycle Events

Lecture 11 S3 - Intelligent Tiering Hands On

Lecture 12 Cleanup - Activity 2

Lecture 13 S3 - Data Replication for Recovery Point

Lecture 14 Security Best Practices and Guidelines for Amazon S3

Lecture 15 Introduction to Amazon Kinesis Service

Lecture 16 Ingest Streaming data using Kinesis Stream - Hands On

Lecture 17 Build a streaming system with Amazon Kinesis Data Streams- Hands On

Lecture 18 Streaming data to Amazon S3 using Kinesis Data Firehose - Hands On

Lecture 19 Hands On Generate Kinesis Data Analytics

Lecture 20 Work with Amazon Kinesis Data Stream and Kinesis Agent

Lecture 21 Understanding AWS Glue

Lecture 22 Discover the Metadata using AWS Glue Crawlers

Lecture 23 Data Transformation wth AWS Glue DataBrew

Lecture 24 Perform ETL in Glue with S3

Lecture 25 Understanding Athena

Lecture 26 Querying S3 data using Amazon Athena

Lecture 27 Understanding AWS Batch

Lecture 28 Data Engineering with AWS Step

Lecture 29 Working with AWS Step Functions

Lecture 30 Create Serverless workflow with AWS Step

Lecture 31 Working with states in AWS Step function

Lecture 32 Machine Learning and AWS Step Functions

Lecture 33 Feature Engineering with AWS Step and AWS Glue

Lecture 34 Summary and Key topics to Focus on Module 1

Section 3: Domain 2 : Exploratory Data Analysis

Lecture 35 Domain 2 - Hands On Attachment Files

Lecture 36 Introduction to Exploratory Data Analysis

Lecture 37 Hands On EDA

Lecture 38 Types of Data & the respective analysis

Lecture 39 Statistical Analysis

Lecture 40 Descriptive Statistics - Understanding the Methods

Lecture 41 Definition of Outlier

Lecture 42 EDA Hands on - Data Acquisition & Data Merging

Lecture 43 EDA Hands on - Outlier Analysis and Duplicate Value Analysis

Lecture 44 Missing Value Analysis

Lecture 45 Fixing the Errors/Typos in dataset

Lecture 46 Data Transformation

Lecture 47 Dealing with Categorical Data

Lecture 48 Scaling the Numerical data

Lecture 49 Visualization Methods for EDA

Lecture 50 Imbalanced Dataset

Lecture 51 Dimensionality Reduction - PCA

Lecture 52 Dimensionality Reduction - LDA

Lecture 53 Amazon QuickSight

Lecture 54 Apache Spark - EMR

Section 4: Domain 3 : Modelling

Lecture 55 Domain 3 - Hands On Attachment files

Lecture 56 Introduction to Domain 3 - Modelling

Lecture 57 Introduction to Machine Learning

Lecture 58 Types of Machine Learning

Lecture 59 Linear Regression & Evaluation Functions

Lecture 60 Regularization and Assumptions of Linear Regression

Lecture 61 Logistic Regression

Lecture 62 Gradient Descent

Lecture 63 Logistic Regression Implementation and EDA

Lecture 64 Evaluation Metrics for Classification

Lecture 65 Decision Tree Algorithms

Lecture 66 Loss Functions of Decision Trees

Lecture 67 Decision Tree Algorithm Implementation

Lecture 68 Overfit Vs Underfit - Kfold Cross validation

Lecture 69 Hyperparameter Optimization Techniques

Lecture 70 Quick Check-in on the Syllabus

Lecture 71 KNN Algorithm

Lecture 72 SVM Algorithm

Lecture 73 Ensemble Learning - Voting Classifier

Lecture 74 Ensemble Learning - Bagging Classifier & Random Forest

Lecture 75 Ensemble Learning - Boosting Adabost and Gradient Boost

Lecture 76 Emsemble Learning XGBoost

Lecture 77 Clustering - Kmeans

Lecture 78 Clustering - Hierarchial Clustering

Lecture 79 Clustering - DBScan

Lecture 80 Time Series Analysis

Lecture 81 ARIMA Hands On

Lecture 82 Reccommendation Amazon Personalize

Lecture 83 Introduction to Deep Learning

Lecture 84 Introduction to Tensorflow & Create first Neural Network

Lecture 85 Intuition of Deep Learning Training

Lecture 86 Activation Function

Lecture 87 Architecture of Neural Networks

Lecture 88 Deep Learning Model Training. - Epochs - Batch Size

Lecture 89 Hyperparameter Tuning in Deep Learning

Lecture 90 Vanshing & Exploding Gradients - Initializations, Regularizations

Lecture 91 Introduction to Convolutional Neural Networks

Lecture 92 Implementation of CNN on CatDog Dataset

Lecture 93 Transfer Learning for Computer Vision

Lecture 94 Feed Forward Neural Network Challenges

Lecture 95 RNN & Types of Architecture

Lecture 96 LSTM Architecture

Lecture 97 Attention Mechanism

Lecture 98 Transfer Learning for Natural Language Data

Lecture 99 Transformer Architecture Overview

Section 5: Domain 4 : Machine Learning Implementation and Operations

Lecture 100 Domain 4 - Attachment Files

Lecture 101 Introduction to Domain 4 - Machine Learning Implementation and Operations

Lecture 102 Serverless AWS Lambda - Part 1

Lecture 103 Introduction to Docker & Creating the Dockerfile

Lecture 104 Serverless AWS Lambda - Part 2

Lecture 105 Cloudwatch

Lecture 106 End to End Deployment with AWS Sagemaker End Point

Lecture 107 AWS Sagemaker JumpStart

Lecture 108 AWS Polly

Lecture 109 AWS Transcribe

Lecture 110 AWS Lex

Lecture 111 Retrain Pipelines

Lecture 112 Model Lineage in Machine Learning

Lecture 113 Amazon Augmented AI

Lecture 114 Amazon CodeGuru

Lecture 115 Amazon Comprehend & Amazon Comprehend Medical

Lecture 116 AWS DeepComposer

Lecture 117 AWS DeepLens

Lecture 118 AWS DeepRacer

Lecture 119 Amazon DevOps Guru

Lecture 120 Amazon Forecast

Lecture 121 Amazon Fraud Detector

Lecture 122 Amazon HealthLake

Lecture 123 Amazon Kendra

Lecture 124 Amazon Lookout for equipment , Metrics & Vision

Lecture 125 Amazon Monitron

Lecture 126 AWS Panorama

Lecture 127 Amazon Rekognition

Lecture 128 Amazon Translate

Lecture 129 Amazon Textract

Lecture 130 Next Steps

Section 6: Machine Learning for Projects

Lecture 131 ML Deployment Files

Lecture 132 Machine learning Deployment Part 1 - Model Prep - End to End

Lecture 133 Machine learning Deployment Part 2 - Deploy Flask App - End to End

Lecture 134 Streamlit Tutorial

Section 7: Optional Topics for Additional Learning - Text Analytics

Lecture 135 Note to Learners on this section

Lecture 136 Attachment for NLP Pipeline

Lecture 137 NLP Pipeline

Lecture 138 Data Extraction and Text Cleaning hands On

Lecture 139 Introduction to NLTK library

Lecture 140 Tokenization , bigrams, trigrams, and N gram - Hands on

Lecture 141 POS Tagging & Stop Words Removal

Lecture 142 Stemming & Lemmatization

Lecture 143 NER and Wordsense Ambiguation

Lecture 144 Introduction to Spacy Library

Lecture 145 Hands On Spacy

Lecture 146 Summary

Lecture 147 NLP Attachment 2

Lecture 148 Vector Representation of Text - One Hot Encoding

Lecture 149 Understanding BoW Technique

Lecture 150 BoW Hands On

Lecture 151 Text Representation : TF-IDF

Lecture 152 TF-IDF Hands On

Lecture 153 Introduction to Word Embeddings

Lecture 154 Understanding the Importance of Vectors - Intuition

Lecture 155 Understanding the Importance of Vectors - Intuition

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: Basics of AWS - For New Learners

Lecture 172 Note to the Learners

Lecture 173 Create AWS Account

Lecture 174 Setting up MFA on Root Account

Lecture 175 Create IAM Account and Account Alias

Lecture 176 Setup CLI with Credentials

Lecture 177 IAM Policy

Lecture 178 IAM Policy generator & attachment

Lecture 179 Delete the IAM User

Lecture 180 Bonus: Understanding Transformer Architecture

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