AWS Machine Learning Certification Exam | Complete Guide

Explore Amazon S3 fundamentals, including buckets and objects, tagging and fine-grained access controls, data lake use, and partitioning to cut query costs; leverage lifecycle rules.

Explore Amazon S3 storage tiers, from S3 standard and intelligent-tiering to S3 Glacier, and learn how lifecycle policies automate transitions and expirations to optimize cost and data retention.

Explore Amazon S3 security within the shared security model, covering data protection, durability, versioning, server-side and in-transit encryption, Macie, and access controls via resource-based and user-based policies.

Explore monitoring and logging for Amazon S3 with CloudWatch alarms, CloudTrail logs, S3 access logs, and Trusted Advisor recommendations, plus network security via VPC endpoints, tagging, and Macie guidance.

Explore the data engineering domain of the AWS machine learning certification exam (20% weight), covering data migration with AWS Glue, pipeline, step, and batch, plus data streaming with Kinesis.

Explore AWS Glue, a fully managed ETL service that extracts, transforms, and loads data, builds the AWS Glue Data Catalog, and integrates with Amazon Quicksight for analytics.

Explore how AWS Glue crawlers scan S3 to build a data catalog, enabling Athena, Redshift, and Quicksight queries and visualizations with built-in and machine learning transformations.

Learn how AWS Batch runs batch computing jobs on AWS, optimizes resources, and provides serverless scheduling and execution using EC2 and Spot Instances.

Learn how AWS step functions orchestrate serverless, graphically designed workflows into debuggable state machines with robust error handling, enabling ML pipelines and cross-service orchestration.

Explore AWS Kinesis for data streaming, ingestion, and real-time analytics. The section covers Kinesis overview and its four services—video streams, data streams, firehose, and analytics—plus practical use cases.

See how AWS Kinesis Video Streams ingests real-time video from multiple devices, durably and securely stores it, and enables real-time analytics and inference with Rekognition, SageMaker, and deep learning models.

Explore Amazon Kinesis Data Streams real-time ingestion of click streams and IoT data. Enable analytics and dashboards with scalable streaming and integration to AWS Lambda, Firehose, and Kinesis Data Analytics.

Learn AWS Kinesis data streams basics: producers, data streams, shards, partition keys, immutable records, 24-hour retention (up to seven days), shard capacity, and CPL, KCL, or SDK API.

Amazon Kinesis Firehose enables near real-time, serverless loading of streaming data into S3, Redshift, Elasticsearch, and Splunk with optional Lambda transformations.

Explore AWS Kinesis Data Analytics for real-time streaming insights. Build end-to-end pipelines from Kinesis Data Streams or Firehose to analytics, using SQL templates or Java libraries, with serverless, pay-per-use scalability.

Explore real-time data analytics with AWS Kinesis data analytics, including streaming ETL for IoT devices, real-time log analytics with SQL, and anomaly detection with random cut forest and hotspots.

Master exploratory data analysis fundamentals for the AWS ML exam, including data cleaning, normalization, and feature engineering with Pandas, NumPy, and Seaborn for high-level visualizations.

Explore numpy for numerical operations and array creation, pandas for data frames and csv file input/output, and matplotlib and seaborn for plotting, heatmaps, and pair plots.

Explore time series and its level, trend, seasonality, and noise, and compare additive and multiplicative models for forecasting, illustrated with a Kaggle avocado price case study.

Dive into exploratory data analysis with Amazon Athena, Amazon Quicksight, and EMR to prepare for the AWS machine learning certification exam, covering security, cost, glue integration, and machine learning insights.

EMR, a scalable AWS big data platform, runs Apache Spark, Apache Hive, Apache Flink, and Presto on elastic clusters with S3 storage, Jupyter notebooks, autoscaling, and strong security.

Explore elastic mapreduce fundamentals, spot instances for cost savings, and EMR storage options like HDFS and EMRFS with S3, plus EMR notebooks and security.

Leverage Apache Hadoop on Amazon EMR to process data with MapReduce, Yarn, and HDFS, using EMRFS to store on S3. Enable analytics and ETL with Hive and Spark on AWS.

Explore feature engineering within exploratory data analysis for the AWS machine learning certification. Master techniques like one-hot encoding, scaling, imputation, and text features such as tf-idf and bag of words.

Master feature engineering to clean unstructured data, handle missing values and outliers, encode categorical features, and create new features with pca, using SageMaker and Glue.

Use feature selection to clean data by removing useless features and noise, then apply pca for dimensionality reduction to improve model training.

Explore imputation of missing values in real datasets like Titanic, using mean, median, and mode replacements, selective column drops, feature engineering, and deep learning methods including Deep AR for accuracy.

Visualize data to identify outliers. Detect them using standard deviation beyond two to four sigma or percentile methods, including box plots and random cut forest in AWS services.

Learn one hot encoding to convert nominal categorical data into numerical features for machine learning, using colors like red, yellow, and green and distinguishing ordinal from nominal data.

Explore binning to convert numeric values into categories, like ages 0–20, 20–70, and 70–100, revealing non-linear relationships, improving model robustness and reducing overfitting with quantile binning and one-hot encoding.

Learn how shuffling data prevents bias, split features to improve model insights, and handle unbalanced datasets with undersampling, oversampling, and SMOTE for AWS machine learning certification exam.

Explore text feature engineering techniques such as bag of words, tf-idf, and n-grams, plus punctuation removal, dates retrieval, orthogonal sparse bigrams, and Cartesian product.

Learn how bag of words tokenizes text into numeric word counts and how punctuation removal and date feature engineering shape these features for modeling.

Master tf-idf, a weighting of term frequency and inverse document frequency used in text search and mining to highlight document-specific keywords.

Explore n-gram text transformation, converting text into sliding windows of 1, 2, or 3 words, i.e., unigrams, bigrams, and trigrams, for machine learning, with punctuation treated as part of words.

Learn how a single neuron processes inputs with weights and a bias, applies an activation function, and produces an output, forming the basis for artificial neural networks.

Explore activation functions used in neural networks, including sigmoid for probability outputs, relu to avoid vanishing gradients in hidden layers, and hyperbolic tangent as a zero centered alternative.

Learn how supervised neural network training uses labeled data, iterates over epochs to minimize error and achieve generalization, with training, validation, and testing splits.

Explore the difference between parameters and hyperparameters, focusing on learning rate and batch size; learn how gradient descent seeks the global minimum and how batch size influences training dynamics.

Explore TensorFlow Playground to build a fully connected neural network with two inputs, hidden layers, and weights, training for classification and regression with learning rate, epochs, and activation functions.

Learn the differences between underfitting and overfitting, how complexity and training affect performance, and how to build a generalized model for training and testing data.

Explore techniques to prevent overfitting, including early stopping, L1/L2 regularization, adding data, feature selection, ensemble learning, dropout, and reducing model complexity to improve generalization.

Explore the bias-variance trade-off through training vs testing data, showing how model complexity affects bias, variance, and generalization to prevent overfitting.

Explore L1 regularization (lasso) which uses an absolute slope penalty to reduce overfitting and enable feature selection, and compare it with L2 regularization (ridge) that keeps all features with weights.

Explore artificial neural networks, convolutional and recurrent networks, and LSTM to address vanishing gradients, then assess models with cross-validation, transfer learning, and ensemble methods for the AWS certification exam.

Explore architectures of artificial neural networks, including feedforward and convolutional nets, with recurrent variants for temporal data. Learn about backpropagation during training and LSTM/GRU approaches.

Explore the basics of convolutional neural networks, from feature detectors and relu activation to pooling and flattening, to understand image classification and detecting and classifying objects.

Explore recurrent neural networks, their memory state and time dependency, and apply them to one-to-one, many-to-many, and one-to-many tasks like translation, captioning, and text generation.

Explore the vanishing gradient problem in deep recurrent networks, and learn how long short-term memory networks, activation choices, residual networks, and gradient checking help maintain effective learning.

Explore how long short-term memory networks overcome the vanishing gradient with a memory cell and gates. Understand forget, input, and output gates and training via backpropagation through time.

Explore how the confusion matrix assesses model performance, identify true/false positives and negatives, type I/II errors, and metrics like accuracy, misclassification rate, precision, and recall.

Examine precision, recall, and F1-score within confusion matrices on imbalanced data, with fraud and spam examples; cover specificity, per-class F1, multi-class matrices, ROC AUC, and heatmaps.

Transfer learning reuses a pretrained network to tackle a new task and dataset, freezing base layers or retraining with a small learning rate, especially with limited data or resources.

Learn ensemble learning techniques like bagging and boosting that improve accuracy by combining multiple models, such as random forests and XGBoost, via voting, while boosting uses sequential reweighting for predictions.

Learn how k-fold cross validation assesses a model's generalization for AWS machine learning certification by training on k-1 folds and testing on the remaining fold across shuffled folds.

Introduce the modeling section of the AWS machine learning certification course, highlighting SageMaker, built-in algorithms, and deep learning, plus topics like object detection and image classification.

Explore AWS SageMaker, a fully managed machine learning platform for data labeling, model building, training, tuning, and deployment, with end-to-end workflow on S3 and EC2.

Train and deploy machine learning models on AWS SageMaker, detailing training data, S3 buckets, compute resources, artifacts, inference endpoints, batch transforms, and auto scaling.

Master deep learning on AWS using EC2 with deep learning AMIs preinstalled with MXNet, TensorFlow, Keras, and more on GPU instances; learn EMR for big data processing with dynamic resizing.

Learn how SageMaker object detection draws bounding boxes, identifies objects with confidence scores, and uses vgg or resnet with ssd, transfer learning, and image augmentation for training and inference.

Explore image classification as a supervised, multi-class task without bounding boxes, powered by ResNet and transfer learning, with options for full training or fine-tuning on ImageNet-pretrained models.

Explore semantic segmentation and learn how pixel-level class labeling produces segmentation masks, using encoder-decoder networks with Resnet backbones, and how this differs from bounding boxes and object detection.

Explore the linear learner, a supervised learning algorithm that fits a line for regression and supports classification. Learn pre-processing, SGD optimization, hyperparameters, and evaluation metrics used for training and inference.

Master factorization machines, a supervised extension of linear models for highly sparse data, enabling click prediction and recommendations, with guidance on sparse data formats and cpu deployment.

Explore DeepAR, a one-dimensional time series forecasting algorithm in Amazon SageMaker that handles seasonality and multiple time series, offering point and probabilistic forecasts for inventory and financial planning.