Master data engineering on aws by designing data repositories for machine learning, building ingestion pipelines with kinesis and snowball, and transforming data for s3 data lakes and warehouses.

Learn to ingest streaming data using an Amazon Kinesis stream by creating the stream, validating it, putting and retrieving records, and understanding shard basics and cleanup.

Learn to ingest streaming data into an Amazon S3 bucket using Kinesis Data Firehose, creating a delivery stream from a Kinesis data stream, with optional Lambda transformations.

Learn to perform an ETL workflow in AWS by using Glue with S3 as data source, creating a crawler and Glue job to aggregate data and save results to S3.

Explore how AWS Batch enables batch management to run thousands of jobs on AWS. Enable dynamic provisioning with Fargate or EC2, priority-based scheduling, GPU support, and integrated monitoring.

Explore states in AWS step functions by building a simple workflow with lambda functions to add A and B, check the sum, and wait between cycles.

Explore exploratory data analysis techniques, handle missing values with imputation, address imbalanced data and outliers, and apply PCA and LDA to guide predictive model selection.

Balance imbalanced datasets in classification by using undersampling and oversampling, including smote and near miss, demonstrated on a credit card fraud example.

Implement a decision tree classifier on the social network ads dataset, scale features, split data, train the model, evaluate with accuracy and F1, visualize the tree, and note overfit.

Learn ensemble learning through the wisdom of crowd, using hard and soft voting to fuse multiple classifiers, including random forest, and improve accuracy beyond any single model.

Analyze a time series dataset of seaplane passengers using ARIMA, convert non-stationary data to stationary via differencing, estimate p, d, q with auto.arima, fit the model, and forecast future values.

Explore activation functions in deep learning, from sigmoid and tanh to relu variants like leaky relu, selu, and softmax. See how nonlinear transforms shape neural network outputs.

discover how vanishing and exploding gradients affect backpropagation in deep neural networks, and explore solutions like Xavier initialization, non-saturating activations, batch normalization, and gradient clipping.

Implement a convolutional neural network on the cat dog dataset to build an image classifier. Use image data generator, train/test split, and transfer learning to improve accuracy.

Explore how feed forward neural networks process text as independent feature vectors, the challenges this poses for learning context, and why recurrent neural networks are needed to remember previous inputs.

deploy a trained cat or dog classifier serverlessly with AWS Lambda, converting to TensorFlow Lite, pre-processing image data, and packaging into a Docker image for ECR and API Gateway.

Learn to containerize a Python ML project with Docker, deploy on AWS Lambda via ECR, and expose it via API gateway using TFLite runtime and Keras image helper.

Explore how CloudWatch monitors applications and infrastructure, uses logs and log groups, sets alarms and automations, visualizes metrics with dashboards, and routes events via EventBridge rules across AWS services.

Explore AWS Lex for building voice and text chat bots, configuring intents with training data and airline templates, and using Transcribe, SageMaker, and Polly.

Explore Amazon Comprehend and Amazon Comprehend Medical for natural language processing and text analytics, including named entity recognition, topics, sentiment, syntax, and real-time analysis with custom models.

Explore Amazon Forecast to perform time series analysis and build accurate forecasting models using machine learning, with historical data and automated ML, the same technology powering Amazon.com.

Leverage AWS panorama to deploy computer vision at the edge, using a machine learning appliance and SDK to add vision to on premise cameras with low latency and data privacy.

Explore named entity recognition and word sense disambiguation in natural language processing, with Python demonstrations of the Lesk algorithm and tokenization. Learn how methods enable information extraction and text mining.

Explore the bag of words technique for text representation in natural language processing, comparing it with one-hot encoding and outlining its advantages and limitations.

Master tf-idf by combining term frequency and inverse document frequency to quantify word importance across a document corpus, with practical examples and stopwords handling.

Learn to implement tf-idf, compare it with count vectorizer, and compute term frequency and idf to generate tf-idf scores for information extraction and text classification using python and sklearn.

Explore the skip-gram word embedding architecture and its contrast with cbow, showing how a simple one-hidden-layer neural network predicts context words from a target word using a window size.

Explore the practical differences between cbow and skip-gram, including training samples, window size effects, training time, and where each model excels for rare words and syntactic relationships.

Explore key terminologies in inferential statistics, including parameter space, sample space, sampling distribution, standard error, and estimation, with point and interval estimation and their relationship to population and samples.

Explore the three main forms of statistical inference—point estimation, interval estimation, and hypothesis testing—along with estimators, estimates, and bias.

Load the Boston housing dataset, sample 200, compute the sample mean, then calculate the z critical, margin of error, and 95% confidence interval.

Master z tests for hypothesis testing by comparing the sample mean to an assumed mean with known variance, using the z statistic in a passport processing example.

Learn to set up MFA on the AWS root account using an authenticator app to securely log in and manage root security settings.

Explore enterprise MLOps essentials, bridging data science and DevOps, and automate the ML lifecycle with Docker, Kubernetes, MLflow, and SageMaker on AWS.