Machine Learning on AWS SageMaker for Beginners

Machine Learning on AWS SageMaker for Beginners, available at $44.99, has an average rating of 4.55, with 47 lectures, 3 quizzes, based on 20 reviews, and has 124 subscribers.

You will learn about Learn basics of Machine Learning Types of Machine Learning Cloud Computing Basics Machine Learning in Cloud AWS Account setup AWS SageMaker Basics Train and deploy AI/ML models using AWS SageMaker Reduce the Billing while training Models Develop, train, test and deploy linear regression model to make predictions. This course is ideal for individuals who are Beginners in Machine Learning or Students at initial stage of learning AWS SageMaker or Students willing to learn AWS platform for ML projects It is particularly useful for Beginners in Machine Learning or Students at initial stage of learning AWS SageMaker or Students willing to learn AWS platform for ML projects.

Enroll now: Machine Learning on AWS SageMaker for Beginners

Summary

Title: Machine Learning on AWS SageMaker for Beginners

Price: $44.99

Average Rating: 4.55

Number of Lectures: 47

Number of Quizzes: 3

Number of Published Lectures: 47

Number of Published Quizzes: 3

Number of Curriculum Items: 50

Number of Published Curriculum Objects: 50

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will Learn

Learn basics of Machine Learning
Types of Machine Learning
Cloud Computing Basics
Machine Learning in Cloud
AWS Account setup
AWS SageMaker Basics
Train and deploy AI/ML models using AWS SageMaker
Reduce the Billing while training Models
Develop, train, test and deploy linear regression model to make predictions.

Who Should Attend

Beginners in Machine Learning
Students at initial stage of learning AWS SageMaker
Students willing to learn AWS platform for ML projects

Target Audiences

Beginners in Machine Learning
Students at initial stage of learning AWS SageMaker
Students willing to learn AWS platform for ML projects

This course is designed for the students who are at their initial stage or at the beginner level in learning the Machine Learning concepts integrated with cloud computing using the Amazon AWS Cloud Services.

This course focuses on what cloud computing is, followed by some essential concepts of Machine Learning. It also has practical hands-on lab exercises which covers a major portion of setting up the basic requirements to run projects on SageMaker

This course covers five (5) projects of different machine learning algorithms to help students learn about the concepts of ML and how they can run such projects in the AWS SageMaker environment. Below is list of projects that are covered in this course:

1- Titanic Survival Prediction

2- Boston House Price Prediction

3- Population Segmentation using Principal Component Analysis (PCA)

4- Population Segmentation using KMeans Clustering

5- Handwritten Digit Classification (MNIST Dataset)

Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.

Amazon SageMaker helps data scientists and developers to prepare, build, train, and deploy high-quality machine learning (ML) models quickly by bringing together a broad set of capabilities purpose-built for ML.

Amazon SageMaker is a fully managed service that provides every developer and data scientist with the ability to prepare build, train, and deploy machine learning (ML) models quickly. SageMaker removes the heavy lifting from each step of the machine learning process to make it easier to develop high quality models. SageMaker provides all of the components used for machine learning in a single toolset so models get to production faster with much less effort and at lower cost.

Look forward to see you enroll in this class to learn Machine Learning in AWS SageMaker platform. Best of luck!

Course Curriculum

Chapter 1: Introduction to Cloud

Lecture 1: Introduction to Cloud Computing

Lecture 2: What is Cloud Computing

Lecture 3: Cloud Computing Services

Lecture 4: Why Cloud Computing

Chapter 2: Introduction to Machine Learning

Lecture 1: What is Machine Learning?

Lecture 2: Machine Learning vs Traditional Programming

Lecture 3: Basic workflow of Machine Learning

Lecture 4: Applications of Machine Learning

Chapter 3: Types of Machine Learning

Lecture 1: Supervised Learning

Lecture 2: Unsupervised learning

Lecture 3: Reinforcement Learning

Chapter 4: Getting Started with AWS SageMaker

Lecture 1: Creating AWS Account

Lecture 2: AWS Web Console Overview

Lecture 3: Create a Notebook Instance

Lecture 4: Spinning the Jupyter Notebook in SageMaker

Lecture 5: Upload Dataset to S3 Bucket

Lecture 6: Import Dataset from S3 to Jupyter Notebook

Chapter 5: PROJECT1 : Titanic Survival ( Linear Learner & Binary Classification )

Lecture 1: Problem Statement

Lecture 2: Importing Dataset to Notebook

Lecture 3: Exploratory Data Analysis

Lecture 4: Data Cleaning Part 1

Lecture 5: Data Cleaning Part 2

Lecture 6: Splitting Dataset into Train and Test Data

Lecture 7: Training Model in SageMaker

Lecture 8: Deploying Model

Lecture 9: Survival Prediction and Deleting the Endpoints

Chapter 6: PROJECT2 : Boston House Prediction

Lecture 1: Problem Statement and Data Import

Lecture 2: Exploratory Data Analysis

Lecture 3: Univariate and Multivariate Analysis – I

Lecture 4: Univariate and Multivariate Analysis – II

Lecture 5: Splitting Dataset into Train and Test Set

Lecture 6: Model Training Job

Lecture 7: Price Prediction and Deleting the Endpoints

Chapter 7: PROJECT3: Population Segmentation – (Principle Component Analysis)

Lecture 1: Problem Statement and Data Import

Lecture 2: Exploratory Data Analysis

Lecture 3: Model Training Job

Lecture 4: Accessing PCA Model Attributes

Lecture 5: Deploy the PCA Model and perform Conclusions

Chapter 8: PROJECT4: Population Segmentation – (KMeans Clustering with PCA)

Lecture 1: Data Modeling – K Means Algorithm

Lecture 2: Accessing K Means Model Attributes

Lecture 3: Conclusion and Deleting Endpoint

Chapter 9: PROJECT5: Digit Classification – MNIST Handwritten

Lecture 1: Problem Statement and Environment Setup

Lecture 2: Download and Import the Dataset

Lecture 3: Exploring the Training Dataset

Lecture 4: XGBoost and Training Dataset Transformation

Lecture 5: Training the Model

Lecture 6: Model Deployment and Validation

Instructors

SKILL CURB
TECHNOLOGY MADE EASY

Rating Distribution

1 stars: 1 votes
2 stars: 1 votes
3 stars: 3 votes
4 stars: 6 votes
5 stars: 9 votes

Frequently Asked Questions

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