AWS SageMaker Practical for Beginners | Build 6 Projects
Master AWS SageMaker Algorithms (Linear Learner, XGBoost, PCA, Image Classification) & Learn SageMaker Studio & AutoML
Lo que serás capaz de hacer
# Update 22/04/2021 - Added a new case study on AWS SageMaker Autopilot. # Update 23/04/2021 - Updated code scripts and addressed Q&A bugs. 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.
- 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.
Temario
UNIDAD 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes · 5 leccionesUNIDAD 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes
UNIDAD 1 - Introduction, Success Tips & Best Practices and Key Learning Outcomes
- Course Introduction and Welcome Message
- Updates on Udemy Reviews
- Course Key Tips and Best Practices
- Course Outline and Key Learning Outcomes
- EXTRA: Learning Path
UNIDAD 2 - Introduction to AI/ML, AWS and Cloud Computing · 14 leccionesUNIDAD 2 - Introduction to AI/ML, AWS and Cloud Computing
UNIDAD 2 - Introduction to AI/ML, AWS and Cloud Computing
- AWS Free Tier Account Setup and Overview
- Introduction to AI, Machine Learning and Deep Learning
- Introduction to AI, Machine Learning and Deep Learning - Part #2
- Good Data Vs. Bad Data
- Introduction to AWS and Cloud Computing
- Key Machine Learning Components and AWS Management Console Tour
- AWS Regions and Availability Zones
- Amazon S3
- Amazon EC2 and IAM
- AWS SageMaker Overview
- AWS SageMaker Walk-through
- AWS SageMaker Studio Overview
- AWS SageMaker Studio Walk-through
- SageMaker Models Deployment
UNIDAD 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner · 13 leccionesUNIDAD 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner
UNIDAD 3 - Project #1 - Employee Salary Predictions Using AWS SageMaker Linear Learner
- Project Overview
- Simple Linear Regression Intuition
- Least Sum of Squares
- AWS SageMaker Linear Learner Overview
- Coding Task #1A - Instantiate AWS SageMaker Notebook Instance (Method #1)
- Coding Task #1B - Using AWS SageMaker Studio (Method #2)
- Coding Task #2 - Import Key libraries and dataset
- Coding Task #3 - Perform Exploratory Data Analysis
- Coding Task #4 - Create Training and Testing Dataset
- Coding Task #5 - Train a Linear Regression Model in SkLearn
- Coding Task #6 - Evaluate Trained Model Performance
- Coding Task #7 - Train a Linear Learner Model in AWS SageMaker
- Coding Task #8 - Deploy Model & invoke endpoint in SageMaker
UNIDAD 4 - Project #2 - Medical Insurance Premium Prediction · 18 leccionesUNIDAD 4 - Project #2 - Medical Insurance Premium Prediction
UNIDAD 4 - Project #2 - Medical Insurance Premium Prediction
- Project Overview and Introduction
- Multiple Linear Regression Intuition
- Regression Metrics and KPIs - RMSE, MSE, MAE, MAPE
- Regression Metrics and KPIs - R2 and Adjusted R2
- Coding Task #1 & #2 - Import Dataset and Key Libraries
- Coding Task #3 - Perform Exploratory Data Analysis
- Coding Task #4 - Perform Data Visualization
- Coding Task #5 - Create Training and Testing Datasets
- Coding Task #6 - Train a Machine Learning Model Locally
- Coding Task #7 - Train a Linear Learner Model in AWS SageMaker
- Coding Task #8 - Deploy Trained Model and Invoke Endpoint
- Artificial Neural Networks for Regression Tasks
- Activation Functions - Sigmoid, RELU and Tanh
- Multilayer Perceptron Networks
- How do Artificial Neural Networks Train?
- Gradient Descent Algorithm
- Backpropagation Algorithm
- Coding Task #9 - Train Artificial Neural Networks for Regression Tasks
UNIDAD 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression) · 21 leccionesUNIDAD 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)
UNIDAD 5 - Project #3 - Retail Sales Prediction Using AWS SageMaker XGBoost (Regression)
- Introduction to Case Study
- Basics: What is the difference between Bias & Variance
- Basics: L1 & L2 Regularization - Part #1
- Basics: L1 & L2 Regularization - Part #2
- Introduction to XGBoost (Extreme Gradient Boosting) algorithm
- What is Boosting?
- Decision Trees and Ensemble Learning
- Gradient Boosted Trees - Deep Dive - Part #1
- Gradient Boosted Trees - Deep Dive - Part #2
- AWS SageMaker XGBoost Algorithm
- Project Introduction and Notebook Instance Instantiation
- Coding Task #1 #2 #3 - Load Dataset/Libraries and Perform Data Exploration
- Coding Task #4 - Merge and Manipulate DataFrame Using Pandas
- Coding Task #5 - Explore Merged Datasets
- Coding Task #6 #7 - Visualize Dataset
- Coding Task #8 - Prepare the Data To Perform Training
- Coding Task #9 - Train XGBoost Locally
- Coding Task #10 - Train XGBoost Using SageMaker
- Coding Task #11 - Deploy XGBoost endpoint and Make Predictions
- Coding Task #12 - Perform Hyperparameters Tuning
- Coding Task #13 - Retrain the Model Using best (optimized) Hyperparameters
UNIDAD 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification) · 15 leccionesUNIDAD 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)
UNIDAD 6 - Project #4 - Predict Cardiovascular Disease Using PCA & XGBoost (Classification)
- Introduction and Project Overview
- Principal Component Analysis (PCA) Intuition
- XGBoost for Classification Tasks (Review Lecture)
- Confusion Matrix
- Precision, Recall, and F1-Score
- Area Under Curve (AUC) and Receiver Operating Characteristics (ROC) Metrics
- Overfitting and Under fitting Models
- Coding Task #1 - SageMaker Studio Notebook Setup
- Coding Task #2 & #3 - Import Data/Libraries & Perform Exploratory data analysis
- Coding Task #4 & #5 - Visualize Datasets & Prepare Training/Testing Data
- Coding Task #6 - Train & Test XGboost and Perform Grid Search (Local Mode)
- Coding Task #7 - Train a PCA Model in AWS SageMaker
- Coding Task #8 - Deploy Trained PCA Model Endpoint & Envoke endpoint
- Coding Task #9 - Train XGBoost (SageMaker Built-in) to do Classification Tasks
- Coding Task #10 - Deploy Endpoint, Make Inference @ Test Model
UNIDAD 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker · 11 leccionesUNIDAD 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker
UNIDAD 7 - Project #5 - Deep Learning for Traffic Sign Classification Using AWS SageMaker
- Project Overview and Introduction
- What are Convolutional Neural Networks and How do they Learn? - Part #1
- What are Convolutional Neural Networks and How do they Learn? - Part #2
- How to Improve CNNs Performance?
- Confusion Matrix
- LeNet Network Architecture
- Request AWS SageMaker Service Limit Increase
- Coding Part #1 #2 - Import Images and Visualize Them
- Coding #3 #4 - Upload Training/Testing Data to S3
- Coding Task #5 - Build and Train CNNs
- Coding Task #6 - Deploy Trained Model Using SageMaker
UNIDAD 8 - Project #6 - SageMaker Studio DeepDive and AutoML · 13 leccionesUNIDAD 8 - Project #6 - SageMaker Studio DeepDive and AutoML
UNIDAD 8 - Project #6 - SageMaker Studio DeepDive and AutoML
- Case Study Overview: AI in Business
- Introduction to AI in business with AWS
- Reading Material: AI Applications in Business
- Quiz: AI Applications in Business
- Insurance Premium Project Overview
- Simple and Multiple Linear Regression
- AWS 101 [Review Lecture/Skip If Content is Familiar]
- S3 and EC2 [Review Lecture/Skip If Content is Familiar]
- AWS SageMaker [Review Lecture/Skip If Content is Familiar]
- Regression Metrics [Review Lecture/Skip if Content is Familiar]
- AWS SageMaker AutoPilot Demo #1
- AWS SageMaker AutoPilot Demo #2
- AWS SageMaker AutoPilot Demo #3
Tu certificado lo emite la SEP, no solo una plataforma
Al demostrar la competencia recibes un certificado emitido por la Secretaría de Educación Pública a través del Gobierno del Estado de México: un documento oficial que puedes presentar con confianza ante cualquier empleador o institución.
Tu certificado con INEC
- Lo emite la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México
- Documento oficial con folio verificable en línea
- Lo otorga la Universidad Tecnológica Fidel Velázquez
Otras plataformas en línea
- Constancia privada emitida por la misma plataforma
- Solo acredita horas cursadas o asistencia
- Sin respaldo de una autoridad educativa
Tu itinerario, de la inscripción al certificado
- 1
Inscripción con pago único
Pagas $2,800 MXN una sola vez y entras de inmediato al curso.
- 2
Estudias a tu ritmo
8 unidades con videos, lecturas y actividades, disponibles 24/7 durante 12 meses.
- 3
Cuestionarios por unidad
1 cuestionarios para comprobar lo aprendido.
- 4
Certificado
Emitido por la SEP a través del Gobierno del Estado de México, con folio verificable.
Un certificado oficial de la SEP
Muestra · verificable por folio y QR
- Emitido por la Secretaría de Educación Pública (SEP), a través del Gobierno del Estado de México. No es una constancia privada: es un documento oficial, y ese respaldo marca la diferencia frente a otras plataformas en línea.
- Contenido impartido por Prof. Ryan Ahmed, PhD, MBA.
- Folio y QR verificables para compartirlo con quien te contrate.
Empieza hoy, a tu ritmo
Un solo pago y acceso inmediato al curso completo durante 12 meses.
- 8 unidades · 110 lecciones · 40 h
- 12 meses de acceso
- 1 cuestionarios de evaluación
- Certificado emitido por la SEP (Gob. del Estado de México) con folio verificable
Sobre tu inscripción
¿Cómo es el pago?
Un solo pago de $2,800 MXN. No hay mensualidades ni cargos recurrentes.
¿Cuánto tiempo tengo acceso?
12 meses desde tu inscripción, 24/7, para avanzar a tu ritmo.
¿Quién imparte el contenido?
El curso es impartido por Prof. Ryan Ahmed, PhD, MBA; INEC gestiona tu inscripción, evaluación y certificado.
¿Qué pasa cuando vence mi acceso?
Puedes renovar tu cuenta tú mismo desde la plataforma, sin trámites ni esperas.
