AWS Certified Machine Learning Associate MLA-C01 - Updated

explore identity and access management in AWS by defining users, attaching policies, and assigning permissions to enable or restrict actions, including service and execution roles.

Explore AWS data engineering tools, including data storage with S3, data integration with Glue, data warehousing with Redshift, and data visualization with QuickSight, to enable scalable, cost-efficient data processing.

Discover how to use AWS S3 buckets and objects, and compare storage classes like S3 Standard, S3 Intelligent-Tiering, S3 One Zone-IA, S3 Glacier, S3 Glacier Deep Archive, and S3 Outpost for scalable, durable storage.

Learn to install and verify the AWS CLI v2, generate IAM access keys, configure credentials with aws configure, and set up named profiles for multi-account use.

Learn to create an S3 bucket with the AWS CLI, upload a file, and apply a lifecycle rule to move objects to Standard IA after 30 days for cost optimization.

Explore how AWS S3 intelligent-tiering automatically moves infrequently accessed objects to archive storage based on usage, without performance impact. Contrast this with lifecycle management, which moves data after 90 days.

perform a quick cleanup by deleting the file copied to the s3 bucket using the aws s3 mb command, then delete the s3 bucket to finalize the activity.

Enables data replication in S3 to achieve a recovery point by mirroring objects from source bucket to destination bucket within 15 minutes, with versioning and a configured IAM role.

Explore Amazon Kinesis for data analysis by ingesting, processing, and analyzing streams with Kinesis Video Streams, Kinesis Data Streams, Kinesis Data Firehose, and Kinesis Data Analytics, and compare to SQS.

Create an S3 bucket and a Kinesis Data Stream, configure the Kinesis Data Firehose delivery stream to push streaming data to S3, and verify the setup with demo data.

Perform an ETL operation in AWS Glue using S3 as data source, with a crawler creating a table in the Glue data catalog for later aggregation in an ETL job.

Discover Amazon Athena, a serverless, interactive query service that analyzes data stored in S3 with standard SQL, enabling ad hoc queries in parallel and pay-as-you-go pricing.

Learn how AWS step functions provide serverless orchestration with a visual workflow to manage standard and express workflows, including exactly-once and at-least-once executions and branching and error handling.

Learn to build and test a step function workflow by creating a Python lambda, deploying code, wiring the lambda to a state machine, and executing a sample run.

Learn to build a serverless workflow with aws step functions that orchestrates lambda and services, manages state and retries, and uses dynamodb and sns for end-to-end automation.

Explore data pre-processing with AWS Glue DataBrew and AWS Step Functions to train an ML model on an air quality dataset that predicts NYC CO2 levels.

Identify and distinguish qualitative and quantitative data types, including nominal and ordinal, discrete and continuous. Explain how to classify features and dependent versus independent variables for data analysis and EDA.

Master descriptive statistics to summarize data using mean, median, mode, and measures of variability. Learn frequency distribution, central tendency, and univariate to multivariate analysis with practical examples.

Clean data by removing duplicates, save the cleaned data to csv, and reorder columns for clarity. Perform outlier analysis with box plots and five-number summaries to drop anomalies.

Learn to handle missing values by dropping or imputing data, using median for numeric columns and mode for categorical ones, and visualize missingness with pandas and the missing number library.

Impute missing values and fix categorical typos by applying custom functions to product type, rating, and payment mode, then prepare the data for transformation.

Explore data transformation for exploratory data analysis by converting date columns to date time, cleaning price data to numeric INR, and handling currencies, missing values, and outliers with IQR.

Encode categorical data using label encoding for ordinal data and one-hot encoding for nominal data with sklearn and pandas get dummies. Use rating and shipping mode as examples.

Explore Amazon QuickSight, a cloud-based, fully managed visualization tool that connects data from AWS and other sources to create fast, interactive dashboards, with ML insights and anomaly detection.

Explore the three main types of machine learning: supervised, unsupervised, and reinforcement learning, and how labeled data, features, and patterns guide model selection, with a preview of linear regression.

Apply linear regression to predict sales from TV, radio, and newspaper spend, train on a split dataset, and evaluate with mean absolute error, mean squared error, RMSE, and R2.

Explore how the decision tree algorithm splits data using questions, forming root, branches, and leaves in supervised learning for classification and regression, guided by impurity-based loss like Gini or entropy.

Explore how decision tree loss functions guide splits, comparing entropy and information gain with gini impurity, and reviewing algorithms such as ID3, C4.5, CART, CHAID, and Mars.

Implement a decision tree classifier on the social network ads dataset, from data loading and scaling to training, predicting, and evaluating with accuracy and F1, and visualize the resulting tree.

Understand overfitting and underfitting and how data noise and model complexity affect them. Learn how k-fold cross validation prevents overfitting and improves generalization.

Get practical insight into bagging classifiers, built from bootstrap sampling and aggregation, training many models in parallel and aggregating votes to improve accuracy, including random forests and feature importance.

Explore unsupervised learning with clustering, focusing on k-means, exclusive clustering, internal cohesion, external separation, and metrics like inertia and silhouette score.

Explore hierarchical clustering, building a dendrogram from bottom up via agglomerative and divisive methods. Compare linkage metrics—single, average, complete, mean, centroid—using Euclidean distance to reveal cluster structure.

Learn dbscan, a density-based clustering that finds arbitrary-shaped clusters, is robust to outliers, requires no predefined cluster count, uses epsilon and min points, with a practical sklearn example.

Explore time series analysis, including trend, seasonality, cyclic patterns, and irregularity, and apply stationary testing with the augmented Dickey-Fuller test or the Kwiatkowski-Phillips-Schmidt-Shin test, plus differencing and ARMA forecasting.

Explore how Amazon Personalize builds and deploys machine learning powered recommendations and intelligent user segmentation at scale, delivering personalized movie and product experiences for customers.

Explore activation functions in deep learning, from sigmoid and tanh to ReLU variants like leaky ReLU, ELU, SELU, swish, in TensorFlow, and use softmax for multiclass classification.

explore the common deep learning network architecture for binary and multi-class classification and regression, detailing input features, hidden layers, output neurons, activations, and loss functions.

Discover how to tune deep learning hyperparameters, including learning rate, batch size, and epochs, along with model choices like hidden layers, number of units, activation functions, and early stopping.

Explore recurrent neural networks and architectures—from 1 to 1 to many to many, including encoder-decoder, and learn about vanishing and exploding gradients along with remedies like LSTM.

Explore how long short term memory networks solve long term dependencies in sequences using forget, input, and output gates, and introduce attention-based encoder-decoder architectures for many to many tasks.

DevOps alone cannot optimize ML projects; ML demands iterative experimentation, data governance, specialized infrastructure, and MLOps practices to effectively deploy learning models.

Explore the AWS technical stack for ml and MLOps, from data preparation and feature engineering to training, deployment, governance, and model registry, powered by SageMaker and pay-as-you-go services.

Explore how CloudWatch monitors resources and applications, creates alarms, visualizes metrics on dashboards, and automates actions through logs, log groups, and EventBridge rules.

Explore AWS Polly, a text-to-speech service using deep learning to generate lifelike speech, with neural engine and standard options, and experiment with voices like Sally.

Explore AWS DeepLens, a deep learning enabled video camera for computer vision with pre-built models and tutorials; register device and deploy models via the AWS management console for Lambda inference.

Explore how Amazon DevOps Guru uses ML-powered insights to automatically detect operational issues, improve application availability, and reduce downtime through actionable recommendations.

Explore Amazon Rekognition, a deep learning visual analysis service that searches, verifies, and organizes millions of images and videos with object detection, facial analysis, and celebrity recognition.

Explore Amazon Translate, a service for real-time and batch translation with API integration, and tailor translations using custom terminology and parallel data with translation memory.

extracts text and data from handwriting and scanned documents using machine learning, going beyond OCR to analyze forms, tables, and ID documents with the AnalyzeID API.