Free Download Aws Certified Machine Learning Specialty 2024 – MasteryLast 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
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