AWS SageMaker Practical for Beginners | Build 6 Projects

Course Curriculum

Chapter 1: Introduction, Success Tips & Best Practices and Key Learning Outcomes

Lecture 1: Course Introduction and Welcome Message

Lecture 2: Updates on Udemy Reviews

Lecture 3: Course Key Tips and Best Practices

Lecture 4: Course Outline and Key Learning Outcomes

Lecture 5: EXTRA: Learning Path

Chapter 2: Introduction to AI/ML, AWS and Cloud Computing

Lecture 1: AWS Free Tier Account Setup and Overview

Lecture 2: Introduction to AI, Machine Learning and Deep Learning

Lecture 3: Introduction to AI, Machine Learning and Deep Learning – Part #2

Lecture 4: Good Data Vs. Bad Data

Lecture 5: Introduction to AWS and Cloud Computing

Lecture 6: Key Machine Learning Components and AWS Management Console Tour

Lecture 7: AWS Regions and Availability Zones

Lecture 8: Amazon S3

Lecture 9: Amazon EC2 and IAM

Lecture 10: AWS SageMaker Overview

Lecture 11: AWS SageMaker Walk-through

Lecture 12: AWS SageMaker Studio Overview

Lecture 13: AWS SageMaker Studio Walk-through

Lecture 14: SageMaker Models Deployment

Chapter 3: Project #1 – Employee Salary Predictions Using AWS SageMaker Linear Learner

Lecture 1: Project Overview

Lecture 2: Simple Linear Regression Intuition

Lecture 3: Least Sum of Squares

Lecture 4: AWS SageMaker Linear Learner Overview

Lecture 5: Coding Task #1A – Instantiate AWS SageMaker Notebook Instance (Method #1)

Lecture 6: Coding Task #1B – Using AWS SageMaker Studio (Method #2)

Lecture 7: Coding Task #2 – Import Key libraries and dataset

Lecture 8: Coding Task #3 – Perform Exploratory Data Analysis

Lecture 9: Coding Task #4 – Create Training and Testing Dataset

Lecture 10: Coding Task #5 – Train a Linear Regression Model in SkLearn

Lecture 11: Coding Task #6 – Evaluate Trained Model Performance

Lecture 12: Coding Task #7 – Train a Linear Learner Model in AWS SageMaker

Lecture 13: Coding Task #8 – Deploy Model & invoke endpoint in SageMaker

Chapter 4: Project #2 – Medical Insurance Premium Prediction

Lecture 1: Project Overview and Introduction

Lecture 2: Multiple Linear Regression Intuition

Lecture 3: Regression Metrics and KPIs – RMSE, MSE, MAE, MAPE

Lecture 4: Regression Metrics and KPIs – R2 and Adjusted R2

Lecture 5: Coding Task #1 & #2 – Import Dataset and Key Libraries

Lecture 6: Coding Task #3 – Perform Exploratory Data Analysis

Lecture 7: Coding Task #4 – Perform Data Visualization

Lecture 8: Coding Task #5 – Create Training and Testing Datasets

Lecture 9: Coding Task #6 – Train a Machine Learning Model Locally

Lecture 10: Coding Task #7 – Train a Linear Learner Model in AWS SageMaker

Lecture 11: Coding Task #8 – Deploy Trained Model and Invoke Endpoint

Lecture 12: Artificial Neural Networks for Regression Tasks

Lecture 13: Activation Functions – Sigmoid, RELU and Tanh

Lecture 14: Multilayer Perceptron Networks

Lecture 15: How do Artificial Neural Networks Train?

Lecture 16: Gradient Descent Algorithm

Lecture 17: Backpropagation Algorithm

Lecture 18: Coding Task #9 – Train Artificial Neural Networks for Regression Tasks

Chapter 5: Project #3 – Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)

Lecture 1: Introduction to Case Study

Lecture 2: Basics: What is the difference between Bias & Variance

Lecture 3: Basics: L1 & L2 Regularization – Part #1

Lecture 4: Basics: L1 & L2 Regularization – Part #2

Lecture 5: Introduction to XGBoost (Extreme Gradient Boosting) algorithm

Lecture 6: What is Boosting?

Lecture 7: Decision Trees and Ensemble Learning

Lecture 8: Gradient Boosted Trees – Deep Dive – Part #1

Lecture 9: Gradient Boosted Trees – Deep Dive – Part #2

Lecture 10: AWS SageMaker XGBoost Algorithm

Lecture 11: Project Introduction and Notebook Instance Instantiation

Lecture 12: Coding Task #1 #2 #3 – Load Dataset/Libraries and Perform Data Exploration

Lecture 13: Coding Task #4 – Merge and Manipulate DataFrame Using Pandas

Lecture 14: Coding Task #5 – Explore Merged Datasets

Lecture 15: Coding Task #6 #7 – Visualize Dataset

Lecture 16: Coding Task #8 – Prepare the Data To Perform Training

Lecture 17: Coding Task #9 – Train XGBoost Locally

Lecture 18: Coding Task #10 – Train XGBoost Using SageMaker

Lecture 19: Coding Task #11 – Deploy XGBoost endpoint and Make Predictions

Lecture 20: Coding Task #12 – Perform Hyperparameters Tuning

Lecture 21: Coding Task #13 – Retrain the Model Using best (optimized) Hyperparameters

Chapter 6: Project #4 – Predict Cardiovascular Disease Using PCA & XGBoost (Classification)

Lecture 1: Introduction and Project Overview

Lecture 2: Principal Component Analysis (PCA) Intuition

Lecture 3: XGBoost for Classification Tasks (Review Lecture)

Lecture 4: Confusion Matrix

Lecture 5: Precision, Recall, and F1-Score

Lecture 6: Area Under Curve (AUC) and Receiver Operating Characteristics (ROC) Metrics

Lecture 7: Overfitting and Under fitting Models

Lecture 8: Coding Task #1 – SageMaker Studio Notebook Setup

Lecture 9: Coding Task #2 & #3 – Import Data/Libraries & Perform Exploratory data analysis

Lecture 10: Coding Task #4 & #5 – Visualize Datasets & Prepare Training/Testing Data

Lecture 11: Coding Task #6 – Train & Test XGboost and Perform Grid Search (Local Mode)

Lecture 12: Coding Task #7 – Train a PCA Model in AWS SageMaker

Lecture 13: Coding Task #8 – Deploy Trained PCA Model Endpoint & Envoke endpoint

Lecture 14: Coding Task #9 – Train XGBoost (SageMaker Built-in) to do Classification Tasks

Lecture 15: Coding Task #10 – Deploy Endpoint, Make Inference @ Test Model

Chapter 7: Project #5 – Deep Learning for Traffic Sign Classification Using AWS SageMaker

Lecture 1: Project Overview and Introduction

Lecture 2: What are Convolutional Neural Networks and How do they Learn? – Part #1

Lecture 3: What are Convolutional Neural Networks and How do they Learn? – Part #2

Lecture 4: How to Improve CNNs Performance?

Lecture 5: Confusion Matrix

Lecture 6: LeNet Network Architecture

Lecture 7: Request AWS SageMaker Service Limit Increase