Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R

Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R

ABOUT THE COURSE:

This course has been designed by two AI & Machine Learning experts so that we can share our knowledge and help you learn complex theory, algorithms, and coding libraries in a simple way.

We will walk you step-by-step into the World of Machine Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course can be completed by doing either the AWS tutorials, Python tutorials, or R tutorials, or the three of them - AWS, Python & R. Pick the ones you need for your career.

This course is fun and exciting, and at the same time, we dive deep into Machine Learning. It is structured the following way:

• Part 1 - Data Preprocessing: Importing the dataset with pandas, Matrix of Features and Target Vector, Training & Test Sets, Imputing Missing Data, Encoding Categorical Variables, Feature Scaling

• Part 2 - Regression: Simple Linear Regression, Multiple Linear Regression, Polynomial Regression, SVR, Decision Tree Regression, Random Forest Regression

• Part 3 - Classification: Logistic Regression, K-NN, SVM, Kernel SVM, Naive Bayes, Decision Tree Classification, Random Forest Classification

• Part 4 - Clustering: K-Means, Hierarchical Clustering

• Part 5 - Association Rule Learning: Apriori, Eclat

• Part 6 - Reinforcement Learning: Upper Confidence Bound, Thompson Sampling

• Part 7 - Natural Language Processing: Bag-of-words model and algorithms for NLP

• Part 8 - Deep Learning: Artificial Neural Networks, Convolutional Neural Networks

• Part 9 - Dimensionality Reduction: PCA, LDA, Kernel PCA

• Part 10 - Model Selection & Boosting: k-fold Cross Validation, Parameter Tuning, Grid Search, XGBoost

• Part 11 - ML Data Preprocessing with AWS: Data types (Apache Parquet, JSON, CSV), Data Preparation with S3, ETL with AWS Glue, Data Wrangling with AWS Glue DataBrew & SageMaker Data Wrangler, Feature Engineering with SageMaker

• Part 12 - ML Model Development with AWS: XGBoost, LightGBM, CatBoost, Ensemble Models, Hyperparameter Tuning Techniques, Building Ensemble Models for Regression & Classification with Amazon SageMaker AI, Natural Language Processing with Amazon Comprehend, Computer Vision with Amazon Rekognition, Text to Speech with Amazon Polly, Speech To Text with Amazon Transcribe, Text Extraction with Amazon Textract, Machine Translation with Amazon Translate

• Part 13 - ML Model Deployment with AWS: Methods for Deploying Models in Production, Deployment in Amazon SageMaker AI, Serverless vs. Real-Time vs. Asynchronous Inference, Deployment Endpoints in Amazon SageMaker, SageMaker vs. ECS vs. EKS vs. Lambda Deployment Targets, CloudFormation & Cloud Development Kit (CDK), Elastic Container Registry (ECR), Elastic Container Service (ECS) & Fargate, Building Containers with Amazon ECR, ECS & EKS

• Part 14 - ML Workflow Automation (CI/CD Pipelines) with AWS: AWS CodePipeline, AWS CodeBuild, AWS CodeCommit, AWS CodeDeploy, Creating an ML pipeline with Amazon SageMaker Pipelines

• Part 15 - ML Solution Monitoring and Maintenance with AWS: Features of Responsible AI, Legal Risks of Generative AI, Tools for Responsible ML, Model/Data Quality and Bias Drift with SageMaker Clarify, Monitoring Models in Production with SageMaker Model Monitor, SageMaker Model Cards, SageMaker Inference Recommender, SageMaker Savings Plans

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