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Aws Certified Machine Learning Associate Mla–C01 – Updated

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Free Download Aws Certified Machine Learning Associate Mla–C01 – Updated
Published 11/2024
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Language: English | Size: 13.03 GB | Duration: 25h 30m
Master Machine Learning on AWS and Ace the MLA-C01 Exam with Confidence – Hands-On Labs, Real-World Projects, and Expert

What you'll learn
Machine Learning Fundamentals: Core principles, including supervised and unsupervised learning, data preprocessing, and feature engineering.
AWS Machine Learning Services: Hands-on expertise with tools like Amazon SageMaker, Rekognition, Comprehend, Polly, and Kinesis.
Real-World Problem Solving: Practical projects like recommendation systems, fraud detection, and NLP applications.
Advanced Topics: Hyperparameter tuning, model optimization, governance, and scaling ML pipelines on AWS.
Requirements
Basic Understanding of Machine Learning Concepts: Familiarity with terms like supervised and unsupervised learning, regression, and classification will be helpful.
Experience with Python Programming: A foundational knowledge of Python and its libraries like NumPy, pandas, or scikit-learn is beneficial.
Basic Knowledge of AWS Services: Familiarity with AWS basics like EC2, S3, or Lambda is an advantage, though not mandatory.
A Laptop or Desktop with Internet Access: To perform hands-on labs and access AWS services.
Willingness to Learn and Explore: Curiosity and a commitment to learn are the most important prerequisites!
Description
AWS Certified Machine Learning Associate (MLA-C01) – Master Your AI Journey Today!Are you ready to step into the world of cutting-edge Artificial Intelligence and Machine Learning with one of the most recognized certifications in the industry? The AWS Certified Machine Learning Associate (MLA-C01) course is your gateway to mastering machine learning on AWS. Designed for professionals and enthusiasts alike, this updated course offers everything you need to pass the exam and implement real-world AI solutions.The Story of Your TransformationImagine standing at the crossroads of opportunity. On one side, there's the booming AI industry, where machine learning experts are in high demand. On the other, there's your current reality—feeling stuck, unsure of where to begin. This course bridges the gap, empowering you to unlock the doors to a high-paying career in AI.What if you could move from confusion to clarity, from an ordinary job to a role where you're the one driving innovation? Picture yourself confidently solving problems, building machine learning models, and leading AI projects. With the AWS Certified Machine Learning Associate certification, you're not just preparing for an exam; you're preparing for a transformation.Why This Course is DifferentUnlike generic courses that bombard you with jargon, this program simplifies complex concepts with a step-by-step approach. We've updated the course to align with the latest AWS services, tools, and exam patterns, ensuring you're ahead of the curve. Here's what you can expect:Interactive Learning Modules: Engage with hands-on labs and real-world projects that simulate scenarios you'll encounter on the job.Expert-Led Content: Learn from certified instructors with years of experience in AWS and machine learning.Up-to-Date Coverage: Master the most recent updates in AWS tools, including SageMaker, Rekognition, Comprehend, and Polly.Course Highlights: The Hero's JourneyUnderstand the Fundamentals of Machine LearningStart your journey by demystifying the basics. Learn about supervised and unsupervised learning, feature engineering, and data preprocessing. No prior experience? No problem! This section is designed for beginners who want to build a strong foundation.Dive into AWS Machine Learning ServicesNavigate through the AWS ecosystem like a pro. Discover the power of Amazon SageMaker for training, tuning, and deploying ML models. Explore how Rekognition transforms image analysis and how Comprehend unlocks insights from text.Hands-On Labs and Real-World ProjectsLearning by doing is the core of this course. Practice building recommendation engines, fraud detection systems, and NLP applications. These projects don't just prepare you for the exam; they prepare you for real-world challenges.Stay Updated with AWS InnovationsThe world of AI evolves rapidly, and so does our course. Our continuous updates ensure you're always learning the latest tools, techniques, and best practices.The Stakes Are HighEvery day, businesses across industries adopt machine learning to gain a competitive edge. From predictive analytics in retail to automated systems in healthcare, the demand for skilled ML professionals has never been greater. This is your moment to shine.If you don't act now, you risk falling behind in one of the fastest-growing industries in the world. But with the AWS Certified Machine Learning Associate certification, you'll position yourself as a leader, ready to tackle complex challenges and unlock new opportunities.Who is This Course For?Aspiring machine learning engineersData scientists looking to expand their skillsCloud professionals aiming to specialize in AISoftware developers transitioning into ML rolesYour Next StepsEmbark on a journey where you're not just learning but transforming. With this course, you'll gain the technical expertise, confidence, and credentials to advance your career. Don't wait for opportunity to find you—seize it.Why Wait? Enroll Now!Your future as an AWS-certified machine learning professional starts today. Let this course be the stepping stone to the career you've always dreamed of. Join a community of learners, access world-class resources, and turn your ambitions into reality.Ready to take the leap?Enroll now and write the next chapter in your success story!
Overview
Section 1: Introduction
Lecture 1 Orientation
Lecture 2 Source code - Course Resources
Section 2: Getting Started with AWS Account
Lecture 3 Quick Note
Lecture 4 Create AWS Account
Lecture 5 Setting up MFA on Root Account
Lecture 6 Create IAM Account and Account Alias
Lecture 7 Setup CLI with Credentials
Lecture 8 IAM Policy
Section 3: Data Preperation for Machine Learning
Lecture 9 Introduction to Data Engineering & Data Ingestion Tools
Lecture 10 Data Engineering Tools
Lecture 11 Working with S3 and Storage Classes
Lecture 12 Creating the S3 Bucket from Console
Lecture 13 Setting up the AWS CLI
Lecture 14 Create Bucket from AWS CLI & Lifecycle Events
Lecture 15 S3 - Intelligent Tiering Hands On
Lecture 16 Cleanup - Activity 2
Lecture 17 S3 - Data Replication for Recovery Point
Lecture 18 Security Best Practices and Guidelines for Amazon S3
Lecture 19 Introduction to Amazon Kinesis Service
Lecture 20 Ingest Streaming data using Kinesis Stream - Hands On
Lecture 21 Build a streaming system with Amazon Kinesis Data Streams- Hands On
Lecture 22 Streaming data to Amazon S3 using Kinesis Data Firehose - Hands On
Lecture 23 Hands On Generate Kinesis Data Analytics
Lecture 24 Work with Amazon Kinesis Data Stream and Kinesis Agent
Lecture 25 Understanding AWS Glue
Lecture 26 Discover the Metadata using AWS Glue Crawlers
Lecture 27 Data Transformation wth AWS Glue DataBrew
Lecture 28 Perform ETL operation in Glue with S3
Lecture 29 Understanding Athena
Lecture 30 Querying S3 data using Amazon Athena
Lecture 31 Understanding AWS Batch
Lecture 32 Data Engineering with AWS Step
Lecture 33 Working with AWS Step Functions
Lecture 34 Create Serverless workflow with AWS Step
Lecture 35 Working with states in AWS Step function
Lecture 36 Machine Learning and AWS Step Functions
Lecture 37 Feature Engineering with AWS Step and AWS Glue
Section 4: Data Exploration, Analysis & Transformation for Machine Learning
Lecture 38 Introduction to Exploratory Data Analysis
Lecture 39 Hands On EDA
Lecture 40 Types of Data & the respective analysis
Lecture 41 Statistical Analysis
Lecture 42 Descriptive Statistics - Understanding the Methods
Lecture 43 Definition of Outlier
Lecture 44 EDA Hands on - Data Acquisition & Data Merging
Lecture 45 EDA Hands on - Outlier Analysis and Duplicate Value Analysis
Lecture 46 Missing Value Analysis
Lecture 47 Fixing the Errors/Typos in dataset
Lecture 48 Data Transformation
Lecture 49 Dealing with Categorical Data
Lecture 50 Scaling the Numerical data
Lecture 51 Visualization Methods for EDA
Lecture 52 Imbalanced Dataset
Lecture 53 Dimensionality Reduction - PCA
Lecture 54 Dimensionality Reduction - LDA
Lecture 55 Amazon QuickSight
Lecture 56 Apache Spark - EMR
Section 5: Machine Learning Model Development
Lecture 57 Introduction to Machine Learning
Lecture 58 Types of Machine Learning
Lecture 59 Linear Regression & Evaluation Metrics for Regression
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 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 6: Foundations of MLOps
Lecture 99 What & Why MLOps
Lecture 100 MLOps Fundamentals
Lecture 101 MLOps Fundamentals - Deep Dive
Lecture 102 Why DevOps alone is not Suitable for Machine Learning ?
Lecture 103 What is AWS & its Benefits
Lecture 104 Technical Stack of AWS for MLOps & Machine Learning
Lecture 105 What is Sagemaker
Lecture 106 Why Sagemaker is the most preferred tool
Section 7: Deployment and Orchestration of ML Workflows
Lecture 107 Model Deployment with Serverless AWS Lambda - Part 1
Lecture 108 Introduction to Docker & Creating the Dockerfile
Lecture 109 Serverless AWS Lambda - Part 2
Lecture 110 Cloudwatch
Lecture 111 End to End Deployment with AWS Sagemaker End Point
Section 8: AWS Services for Machine Learning
Lecture 112 AWS Sagemaker JumpStart
Lecture 113 AWS Polly
Lecture 114 AWS Transcribe
Lecture 115 AWS Lex
Lecture 116 Amazon Augmented AI
Lecture 117 Amazon CodeGuru
Lecture 118 Amazon Comprehend & Amazon Comprehend Medical
Lecture 119 AWS DeepComposer
Lecture 120 AWS DeepLens
Lecture 121 AWS DeepRacer
Lecture 122 Amazon DevOps Guru
Lecture 123 Amazon Forecast
Lecture 124 Amazon Fraud Detector
Lecture 125 Amazon HealthLake
Lecture 126 Amazon Kendra
Lecture 127 Amazon Lookout for equipment , Metrics & Vision
Lecture 128 Amazon Monitron
Lecture 129 AWS Panorama
Lecture 130 Amazon Rekognition
Lecture 131 Amazon Translate
Lecture 132 Amazon Textract
Aspiring Machine Learning Engineers: Individuals looking to kickstart their careers in AI and machine learning.,Data Scientists: Professionals seeking to expand their expertise in AWS-based machine learning services and tools.,Cloud Professionals: AWS practitioners aiming to specialize in AI and machine learning.,Software Developers: Engineers transitioning into AI/ML roles and looking to add machine learning to their skillset.,IT Professionals: Those interested in understanding and implementing AI-powered solutions in their organizations.,Students and Enthusiasts: Anyone passionate about learning cutting-edge machine learning technologies and pursuing a career in AI.

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