AWS SageMaker Practical for Beginners | Build 6 Projects

Explore the basics of artificial intelligence, machine learning, and deep learning, including data, models, and compute power, with AWS SageMaker and S3 storage for training and deployment.

Learn ai and ml data lingo, distinguish labeled from unlabeled data, and compare good data versus bad data to train reliable models.

Explore the basics of Amazon Web Services and cloud computing, including pay-as-you-go compute, storage, and machine learning with SageMaker. Learn how AWS unlocks agility, elasticity, and global deployment.

Understand AWS regions and availability zones as geographically separated data centers that improve data compliance and reduce latency, and learn how to select regions and zones in the console.

Get a high-level look at Amazon SageMaker, a fully managed machine learning workflow that handles data labeling, model training and tuning, deployment to scalable endpoints, and production monitoring.

Explore Amazon SageMaker Studio's intuitive interface, create and run notebook-based experiments, upload data to S3, and train multiple models with Autopilot before deploying endpoints for production.

Master AWS SageMaker model training and deployment using S3 data and ECR training images. Create endpoints for inferences and leverage batch transforms for bulk predictions.

Learn the basics of simple linear regression, predicting a salary (Y) from experience (X) by fitting a line with slope M and intercept B, for future predictions in AWS SageMaker.

Learn to load and run Jupyter notebooks in AWS SageMaker by instantiating a notebook or using SageMaker Studio, train models, deploy endpoints, and generate predictions.

Explore Amazon SageMaker Studio, the first fully integrated ml ide, to code, upload notebooks, track experiments, visualize data, and train a linear regression model with scikit-learn.

Train a linear regression model in scikit-learn using train/test splits and evaluate with model score. Inspect coefficients and intercept; compare fit intercept true versus false to see the effect.

Explore regression metrics and KPIs, including RMSE, MSE, MAE, and MAPE, to assess model fit and error in regression predictions.

Calculate R-squared, the coefficient of determination, to quantify how much of the variance in insurance cost is explained by age; use adjusted R-squared to penalize useless predictors in code.

Perform exploratory data analysis on an insurance dataset. Check for nulls with a heatmap, summarize with info and describe, and compare region charges using grouped means and dummy variables.

Train a linear learner model in AWS SageMaker by converting data to record IO, uploading to S3, and configuring a built-in estimator with key hyperparameters to run the training job.

Analyze activation functions sigmoid, relu, and tanh, showing sigmoid for output probabilities, relu for non-linearity in hidden layers and regression, and tanh for zero-centered classification outputs.

Explore gradient boosting and XGBoost, learn how residuals drive ensemble trees, apply learning rate and regularization to improve generalization and reduce overfitting.

Apply XGBoost to regression tasks using Amazon SageMaker notebook instances, not SageMaker Studio, tune hyperparameters, train and deploy models, and forecast weekly sales from multiple datasets.

Merge sales, feature, and stores data frames with pandas on store, date, and is_holiday; then extract the month from date into a new months column.

Explore the merged dataset, check for nulls with a heat map, fill nulls with zeros, describe statistics, and convert boolean holidays to binary.

Learn to run a hyperparameters tuning job in SageMaker, compare random and Bayesian searches, and optimize XGBoost hyperparameters like learning rate, max depth, and rounds using training and validation data.

Leverage principal component analysis for dimensionality reduction and apply XGBoost to classify cardiovascular disease using AWS SageMaker. Analyze age, blood pressure, cholesterol, and glucose to assess risk.

Explore XGBoost for classification tasks, an ensemble gradient-boosted tree method that combines multiple models for robust predictions, with applications like fraud detection and retirement eligibility.

Explain the difference between overfitting and underfitting, show how simple linear models underfit nonlinear data, and complex models memorize training data but fail to generalize, guiding toward balanced generalization.

Perform dimensionality reduction with the built-in PCA in AWS SageMaker, apply unsupervised learning, prepare data, upload to S3, and train a PCA model with components.

Deploy a trained PCA model as an endpoint, feed input data to obtain six principal components from eleven features, and retrieve JSON-formatted predictions to prepare for the next task.

Train a built-in XGBoost classifier in SageMaker for binary classification using training and validation splits, plus PCA dimensionality reduction features and CSV data pipelines.

Learn to build and deploy a multi-class image classifier using convolutional neural networks in SageMaker, applying Fashion-MNIST and German Traffic Sign data to recognize 10 and 43 classes.

Explore the confusion matrix as a visual tool to evaluate a multi-class traffic sign classifier, detailing true/false positives and negatives, and calculating accuracy, precision, and recall.

Import SageMaker and Boto3, create a session and execution role, prepare training and validation data as npz files, and upload them to S3 under the traffic sign classifier prefix.

Train a Lunette CNN on SageMaker to build and train a 43-class classifier with 32×32×3 inputs, five-by-five filters, pooling, and dense layers.

Explore how ai transforms the insurance industry—from risk assessment, marketing, sales, and service operations—and learn to predict health insurance costs using aws SageMaker Studio Auto Pilot and regression models.

Explore simple and multiple linear regression to predict insurance costs from factors like age and other inputs, learning the equations, slope and intercept, and least-squares estimation.

Discover how AWS S3 provides scalable storage with buckets and 11 nines durability, and how EC2 delivers resizable compute power for training models with SageMaker.

Explore creating an AWS SageMaker Autopilot experiment, upload your insurance dataset to S3, specify the target and output locations, and review multiple model runs to find the best model.

Run an autopilot workflow in AWS SageMaker Studio, review the candidate generation and data exploration notebooks, evaluate models by mean squared error, and deploy scalable endpoints.