AWS SageMaker Practical for Beginners | Build 6 Projects
4.2 (5 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
127 students enrolled

AWS SageMaker Practical for Beginners | Build 6 Projects

Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Image Classification) & Learn SageMaker Studio & AutoML
Hot & New
4.2 (5 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
127 students enrolled
Last updated 5/2020
English
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Price: $199.99
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This course includes
  • 14.5 hours on-demand video
  • 2 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
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What you'll learn
  • Train and deploy AI/ML models using AWS SageMaker
  • Optimize model parameters using hyperparameters optimization search.
  • Develop, train, test and deploy linear regression model to make predictions.
  • Deploy production level multi-polynomial regression model to predict store sales based on the given features.
  • Develop a deploy deep learning-based model to perform image classification.
  • Develop time series forecasting models to predict future product prices using DeepAR.
  • Develop and deploy sentiment analysis model using SageMaker.
  • Deploy trained NLP model and interact/make predictions using secure API.
  • Train and evaluate Object Detection model using SageMaker built-in algorithms.
Course content
Expand all 98 lectures 14:43:33
+ Bonus Materials (Download now!)
1 lecture 00:32
Link to Download Bonus Package
00:32
+ Introduction to AI/ML, AWS and Cloud Computing
14 lectures 02:12:59
AWS Free Tier Account Setup and Overview
05:47
Introduction to AI, Machine Learning and Deep Learning
11:35
Good Data Vs. Bad Data
06:51
Introduction to AWS and Cloud Computing
08:53
Key Machine Learning Components and AWS Management Console Tour
09:25
AWS Regions and Availability Zones
06:19
Amazon S3
14:32
Amazon EC2 and IAM
12:41
AWS SageMaker Overview
09:13
AWS SageMaker Walk-through
10:46
AWS SageMaker Studio Overview
08:41
AWS SageMaker Studio Walk-through
06:59
SageMaker Models Deployment
11:03
+ Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner
13 lectures 02:17:44
Project Overview
05:34
Simple Linear Regression Intuition
09:20
Least Sum of Squares
07:17
Coding Task #1A - Instantiate AWS SageMaker Notebook Instance (Method #1)
12:40
Coding Task #2 - Import Key libraries and dataset
06:32
Coding Task #3 - Perform Exploratory Data Analysis
14:49
Coding Task #4 - Create Training and Testing Dataset
09:02
Coding Task #5 - Train a Linear Regression Model in SkLearn
06:15
Coding Task #6 - Evaluate Trained Model Performance
05:27
Coding Task #8 - Deploy Model & invoke endpoint in SageMaker
08:07
+ Project #2 - Medical Insurance Premium Prediction
18 lectures 02:46:09
Multiple Linear Regression Intuition
04:48
Regression Metrics and KPIs - R2 and Adjusted R2
08:32
Coding Task #1 & #2 - Import Dataset and Key Libraries
11:15
Coding Task #3 - Perform Exploratory Data Analysis
13:36
Coding Task #4 - Perform Data Visualization
09:52
Coding Task #5 - Create Training and Testing Datasets
06:55
Coding Task #6 - Train a Machine Learning Model Locally
07:39
Coding Task #7 - Train a Linear Learner Model in AWS SageMaker
21:14
Artificial Neural Networks for Regression Tasks
09:51
Activation Functions - Sigmoid, RELU and Tanh
04:55
Multilayer Perceptron Networks
05:49
Gradient Descent Algorithm
11:27
Backpropagation Algorithm
03:41
Coding Task #9 - Train Artificial Neural Networks for Regression Tasks
15:59
+ Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)
21 lectures 03:08:51
Basics: L1 & L2 Regularization - Part #1
07:39
Basics: L1 & L2 Regularization - Part #2
04:07
Introduction to XGBoost (Extreme Gradient Boosting) algorithm
06:08
Decision Trees and Ensemble Learning
06:39
Gradient Boosted Trees - Deep Dive - Part #1
16:30
Gradient Boosted Trees - Deep Dive - Part #2
05:17
AWS SageMaker XGBoost Algorithm
05:14
Project Introduction and Notebook Instance Instantiation
09:55
Coding Task #1 #2 #3 - Load Dataset/Libraries and Perform Data Exploration
16:15
Coding Task #4 - Merge and Manipulate DataFrame Using Pandas
07:35
Coding Task #5 - Explore Merged Datasets
06:06
Coding Task #6 #7 - Visualize Dataset
18:11
Coding Task #8 - Prepare the Data To Perform Training
05:05
Coding Task #9 - Train XGBoost Locally
06:33
Coding Task #11 - Deploy XGBoost endpoint and Make Predictions
06:18
Coding Task #13 - Retrain the Model Using best (optimized) Hyperparameters
06:01
+ Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)
15 lectures 02:13:31
XGBoost for Classification Tasks (Review Lecture)
05:52
Confusion Matrix
08:15
Precision, Recall, and F1-Score
17:15
Coding Task #1 - SageMaker Studio Notebook Setup
05:01
Coding Task #2 & #3 - Import Data/Libraries & Perform Exploratory data analysis
10:59
Coding Task #4 & #5 - Visualize Datasets & Prepare Training/Testing Data
08:17
Coding Task #6 - Train & Test XGboost and Perform Grid Search (Local Mode)
19:09
Coding Task #7 - Train a PCA Model in AWS SageMaker
09:50
Coding Task #9 - Train XGBoost (SageMaker Built-in) to do Classification Tasks
09:25
Coding Task #10 - Deploy Endpoint, Make Inference @ Test Model
06:18
+ Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker
11 lectures 01:36:29
Project Overview and Introduction
08:05
What are Convolutional Neural Networks and How do they Learn? - Part #1
15:53
What are Convolutional Neural Networks and How do they Learn? - Part #2
10:40
How to Improve CNNs Performance?
02:54
Confusion Matrix
06:11
Request AWS SageMaker Service Limit Increase
02:05
Coding Part #1 #2 - Import Images and Visualize Them
14:52
Coding #3 #4 - Upload Training/Testing Data to S3
03:54
Coding Task #6 - Deploy Trained Model Using SageMaker
06:11
Requirements
  • Basic knowledge of programming
  • Basic knowledge in AWS
  • Basic knowledge in machine learning
Description

Machine and deep learning are the hottest topics in tech! Diverse fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology.

AWS is one of the most widely used ML cloud computing platforms worldwide – several Fortune 500 companies depend on AWS for their business operations.

SageMaker is a fully managed service within AWS that allows data scientists and AI practitioners to train, test, and deploy AI/ML models quickly and efficiently.

In this course, students will learn how to create AI/ML models using AWS SageMaker.

Projects will cover various topics from business, healthcare, and Tech. In this course, students will be able to master many topics in a practical way such as: (1) Data Engineering and Feature Engineering, (2) AI/ML Models selection, (3) Appropriate AWS SageMaker Algorithm selection to solve business problem, (4) AI/ML models building, training, and deployment, (5) Model optimization and Hyper-parameters tuning.


The course covers many topics such as data engineering, AWS services and algorithms, and machine/deep learning basics in a practical way:

  • Data engineering: Data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib, and Seaborn), data distributions and feature engineering (imputation, binning, encoding, and normalization).

  • AWS services and algorithms: Amazon SageMaker, Linear Learner (Regression/Classification), Amazon S3 Storage services, gradient boosted trees (XGBoost), image classification, principal component analysis (PCA), SageMaker Studio and AutoML.

  • Machine and deep learning basics: Types of artificial neural networks (ANNs) such as feedforward ANNs, convolutional neural networks (CNNs), activation functions (sigmoid, RELU and hyperbolic tangent), machine learning training strategies (supervised/ unsupervised), gradient descent algorithm, learning rate, backpropagation, bias, variance, bias-variance trade-off, regularization (L1 and L2), overfitting, dropout, feature detectors, pooling, batch normalization, vanishing gradient problem, confusion matrix, precision, recall, F1-score, root mean squared error (RMSE), ensemble learning, decision trees, and random forest.


We teach SageMaker’s vast range of ML and DL tools with practice-led projects. Delve into:

  • Project #1: Train, test and deploy simple regression model to predict employees’ salary using AWS SageMaker Linear Learner

  • Project #2: Train, test and deploy a multiple linear regression machine learning model to predict medical insurance premium.

  • Project #3: Train, test and deploy a model to predict retail store sales using XGboost regression and optimize model hyperparameters using SageMaker Hyperparameters tuning tool.

  • Project #4: Perform Dimensionality reduction Using SageMaker built-in PCA algorithm and build a classifier model to predict cardiovascular disease using XGBoost Classification model.

  • Project #5: Develop a traffic sign classifier model using Sagemaker and Tensorflow.

  • Project #6: Deep Dive in AWS SageMaker Studio, AutoML, and model debugging.

The course is targeted towards beginner developers and data scientists wanting to get fundamental understanding of AWS SageMaker and solve real world challenging problems. Basic knowledge of Machine Learning, python programming and AWS cloud is recommended. Here’s a list of who is this course for:

  • Beginners Data Science wanting to advance their careers and build their portfolio.

  • Seasoned consultants wanting to transform businesses by leveraging AI/ML using SageMaker.

  • Tech enthusiasts who are passionate and new to Data science & AI and want to gain practical experience using AWS SageMaker.

Enroll today and I look forward to seeing you inside.

Who this course is for:
  • AI practitioners
  • Aspiring data scientists
  • Tech enthusiasts
  • Data science consultants