AWS SageMaker Practical for Beginners | Build 6 Projects

Learn how to create a free AWS account, understand the AWS free tier limits for SageMaker, EC2, and S3, and explore the console to build, train, and deploy ML models.

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.

Explore the fundamentals of AI, machine learning, and deep learning, including supervised, unsupervised, and reinforcement learning. See practical examples like classification, regression, clustering, and reward-based learning, with upcoming data lingo.

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.

Discover the three core machine learning components—data, model, and compute—using AWS SageMaker, S3 storage, and EC2 compute in a guided management console tour.

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.

Explore EC2's resizable cloud compute with standard, memory, compute, and GPU instances for AI training. Learn pricing (on-demand, spot, reserved), elastic inference, and IAM basics for SageMaker security.

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.

Explore the basics of regression with SageMaker by building, training, deploying, and using a linear regression model to predict employee salaries from years of experience.

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.

Apply the least squares technique to obtain the best fit line by minimizing squared residuals. Split data into training and testing to ensure generalization.

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.

Import pandas, numpy, and matplotlib in SageMaker Studio, load salary.csv into salary_df, view the first and last seven rows with head and tail, and find the maximum salary.

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.

train a linear learner model in AWS SageMaker using S3 for training data and model artifacts. tune hyperparameters and use spot training to improve performance and cut costs.

Explore intuition behind multiple linear regression, extending simple regression to multiple features X1..Xn with coefficients B1..Bn and a bias, and learn to optimize parameters and evaluate with MSE, MAE, R-squared.

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.

Build, train, and deploy a regression model to predict insurance charges using SageMaker Studio; import libraries, explore data, and evaluate linear and neural network models.

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.

Visualize the insurance dataset with histograms, regplot, pair plots, and a correlation heat map to reveal relationships among age, BMI, charges, and other features.

Create training and testing datasets for a regression model by selecting inputs and the charges as output, scaling with standard scaler, and applying a train-test split to evaluate performance.

Practice training and evaluating a linear regression model locally with scikit-learn, including 80/20 train-test split, data scaling, fitting, predicting, inverse transforming, and metrics such as RMSE, MSE, MAE, and R-squared.

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.

deploy a trained linear learner model in AWS SageMaker, create and test an endpoint for inference with text csv input, and review predictions and metrics while noting potential hyperparameter tuning.

Explore artificial neural networks, their basic neuron model with inputs, weights, bias, and activation functions, and train them for regression tasks.

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 multilayer perceptron networks by building two-neuron and multi-layer dense nets. Learn matrix multiplication of inputs and weights, biases, activation functions, and training to minimize error.

Learn how backpropagation trains neural networks by performing forward propagation, computing error, propagating it backward, and updating weights with a learning rate to adjust gradients and deltas.

Learn to forecast weekly retail store sales with XGBoost regression in SageMaker, using a three-file dataset of stores, features, and sales, and tune hyperparameters for accuracy.

Understand bias-variance trade-off by comparing simple and complex models, training vs testing data, and how to seek an optimal balance to improve generalization and avoid overfitting.

Explore l1 regularization (lasso) in regression, adding alpha times absolute slope to reduce overfitting and enable feature selection, and compare it with l2 regularization for when to drop features.

Explore XGBoost, an extreme gradient boosting algorithm using an ensemble of weak models to improve regression and classification, with SageMaker hyperparameter tuning and distributed training.

Explore boosting, an ensemble machine learning approach that learns from previous mistakes by training sequential weak models, reweighting data based on residuals, and combining predictions for stronger outcomes.

Explore decision trees and ensemble learning, including boosting and XGBoost, and learn how CART trees split data at decision nodes and leaves to combine predictions for robust outcomes.

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

Discover XGBoost, a scalable gradient boosting system with sparse data handling and weighted quantile sketch, enabling fast, end-to-end training on billions of examples with SageMaker.

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.

Load and explore stores, features, and sales datasets with Pandas, NumPy, and visualization tools to forecast weekly sales as a regression target, review holidays and markdowns, and prepare datetime features.

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.

Prepare regression data by splitting inputs from weekly sales, dropping the date, one-hot encoding categoricals with drop-first, and converting to float32 arrays for an 85/15 train-test split for XGBoost.

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.

Explore precision, recall, and f1-score, and learn why accuracy can mislead on unbalanced data. Use real-world examples—from fraud detection to spam filtering—to understand when to prioritize each metric.

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.

Set up a SageMaker Studio notebook, perform exploratory data analysis, and apply PCA for dimensionality reduction before training and deploying an XGBoost classifier with endpoint inference.

Learn to import Pandas, NumPy, Seaborn, and Matplotlib, load dataset, drop id column, convert age from days to years, and perform exploratory data analysis for a cardiovascular disease classifier.

Visualize the dataset with histograms and a correlation matrix, then prepare training and testing data by separating features from the cardio target for modeling.

Train and test an XGBoost classifier in local mode, explore default settings, and perform grid search hyperparameter optimization across gamma, subsample, colsample_bytree, and max_depth.

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.

Learn how convolutional neural networks extract features with convolution, relu, pooling, and flattening, then classify via dense layers for image recognition.

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 AWS as a leading cloud platform, learn essentials of storage, compute, and machine learning with SageMaker Autopilot, and explore building AI models in the cloud without upfront hardware costs.

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.

Create an AWS SageMaker Studio AutoPilot experiment by specifying your data in S3 and the target column charges, then let AutoPilot perform preprocessing, feature engineering, and model tuning for regression.

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.