Aws Certified Machine Learning Associate Mla-C01 - Updated

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.