Machine Learning A-Z [2026]: ML, DL, AI with AWS, Python & R
What you'll learn
- Make powerful analysis
- Make accurate predictions
- Develop a strong intuition of many Machine Learning models
- Build robust Machine Learning models with AWS, Python & R
- Supervised Learning: Regression models and Classification models
- Unsupervised Learning: Clustering with K-Means and Hierarchical Clustering
- Association Rule Learning: Data Mining for Market Basket Analysis and Affinity Analysis
- Reinforcement Learning: Upper Confidence Bound & Thompson Sampling for CTR Optimization
- Deep Learning with Artificial Neural Networks and Perceptron for Regression and Classification
- Deep Learning with Convolutional Neural Networks for Computer Vision and Object Recognition
- Gradient Boosting Models: XGBoost, LightGBM and CatBoost for both Regression and Classification
- Ensemble Models: Build an army of powerful ML models to solve problems with maximum predictive power
- Dimensionality Reduction: Principal Component Analysis, Linear Discriminant Analysis and Quadratic Discriminant Analysis
- ML Data Preprocessing with AWS
- ML Model Development with AWS
- ML Model Deployment with AWS
- ML Workflow Automation (CI/CD Pipelines) with AWS
- ML Solution Monitoring and Maintenance with AWS
- Create strong added value to your business
- Responsible ML
Requirements
- Just some high school mathematics level.
Description
Interested in the field of Machine Learning? Then this course is for you!
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
Each section inside each part is independent. So you can either take the whole course from start to finish or you can jump right into any specific section and learn what you need for your career right now.
Moreover, the course is packed with practical exercises that are based on real-life case studies. So not only will you learn the theory, but you will also get lots of hands-on practice building your own models.
And last but not least, this course includes both Python and R code templates which you can download and use on your own projects.
Who this course is for:
- Anyone interested in Machine Learning.
- Students who have at least high school knowledge in math and who want to start learning Machine Learning.
- Any intermediate level people who know the basics of machine learning, including the classical algorithms like linear regression or logistic regression, but who want to learn more about it and explore all the different fields of Machine Learning.
- Any people who are not that comfortable with coding but who are interested in Machine Learning and want to apply it easily on datasets.
- Any students in college who want to start a career in Data Science.
- Any data analysts who want to level up in Machine Learning.
- Any people who are not satisfied with their job and who want to become a Data Scientist.
- Any people who want to create added value to their business by using powerful Machine Learning tools.
Instructors
My name is Kirill Eremenko and I am super-psyched that you are reading this!
Professionally, I come from the Data Science consulting space with experience in finance, retail, transport and other industries. I was trained by the best analytics mentors at Deloitte Australia and since starting on Udemy I have passed on my knowledge to thousands of aspiring data scientists.
From my courses you will straight away notice how I combine my real-life experience and academic background in Physics and Mathematics to deliver professional step-by-step coaching in the space of Data Science. One of the strongest sides of my teaching style is that I focus on intuitive explanations, so you can be sure that you will truly understand even the most complex topics.
To sum up, I am absolutely and utterly passionate about Data Science and I am looking forward to sharing my passion and knowledge with you!
Hadelin is one of Udemy’s top instructors and a recognized leader in AI education. He has taught AI to over 2.6 million learners worldwide and is a frequent guest speaker at prominent industry events. Hadelin has created more than 30 top-rated courses on topics such as AI, Machine Learning, Deep Learning, Blockchain, and Cloud Computing, empowering learners around the globe to upskill in cutting-edge technologies.
In addition to his partnership with Udemy, Hadelin is the co-founder of CloudWolf and SuperDataScience. He is passionate about education and is on a mission to make complex technologies simple, practical, and widely accessible to all.
As a side activity, he is also an actor who acted in seven films, and a movie producer of two films (Indian and French).
Hi there,
We are the SuperDataScience team. You will hear from us when new SuperDataScience courses are released, when we publish new podcasts, blogs, share cheat sheets, and more!
We are here to help you stay on the cutting edge of Data Science and Technology.
See you in class,
Sincerely,
SuperDataScience Team!
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