I have restructured the course to start with SageMaker Lectures First. Machine Learning Service Lectures are still available in the later parts of the course. Newly updated sections start with 2019 prefix.
All source code for SageMaker Course is now available on Github
The new house keeping lectures cover all the steps for setting up code from GitHub.
*** SageMaker Lectures – DeepAR – Time Series Forecasting, XGBoost – Gradient Boosted Tree algorithm in-depth with hands-on. XGBoost has won several competitions and is a very popular Regression and Classification Algorithm, Factorization Machine based Recommender Systems and PCA for dimensionality reduction ***
There are several courses on Machine Learning and AI. What is special about this course?
Here are the top reasons:
- Cloud based machine learning keeps you focused on the current best practices.
- In this course, you will Learn most useful algorithms. Don’t waste your time sifting through mountains of techniques that are in the wild
- Cloud based service is very easy to integrate with your application and has support for wide variety of programming languages.
- Whether you have small data or big data, elastic nature of the AWS cloud allows you to handle them all.
- There is also No upfront cost or commitment – Pay only for what you need and use
In this course, you will learn AI and Machine Learning in three different ways:
AWS Machine Learning
AWS Machine Learning Service is designed for complete beginners.
You will learn three popular easy to understand linear algorithms from the ground-up
You will gain hands-on knowledge on complete lifecycle – from model development, measuring quality, tuning, and integration with your application
The next service is AWS SageMaker.
If you are comfortable coding in Python, SageMaker service is for you.
You will learn how to deploy your own Jupyter Notebook instance on the AWS Cloud.
You will gain hands-on model development experience on very powerful and popular machine learning algorithms like
- XGBoost – a gradient boosted tree algorithm that has won several competitions,
- Recurrent Neural Networks for Time Series forecasting,
- Factorization Machines for high dimensional sparse datasets like Click Stream data
- Neural Network based Image Classifiers,
- Dimensionality reduction with Principal Component Analysis
- and much more
In Application Services section of this course,
You will learn about a set of pre-trained services that you can directly integrate with your application.
You will gain hands-on experience in ready-to-use Vision service for image and video analysis, Conversation chatbots and Language Services for text translation, Speech recognition, and text to speech and more
I am looking forward to seeing you in the course.
Who this course is for:
- This course is designed for anyone who is interested in machine learning and data science
- If you are new to machine learning, this is a perfect course to upskill yourself and fastest way to learn machine learning
- If you are an experienced practitioner, you will gain insight into AWS Machine Learning capability and learn how you can convert your ideas into highly scalable solution in a matter of days
- AWS Certification – If you are preparing for certification, you will learn best practices and gain hands-on experience on securely deploying products using AWS Cloud
- All materials and software instructions are covered in housekeeping lecture
- Familiarity with a programming language
- AWS Account – if you want to try the hands-on activities. AWS charges a small amount for model creation and predictions
- Some basic knowledge of Pandas, Numpy, Matplotlib would be helpful but not absolutely needed
Last updated 5/2019
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