Our Courses

MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how to automate your pipeline’s functions and continuously optimize its performance, which is why we developed this course, MLOps2 (Azure): Data Pipeline Automation & Optimization using Microsoft Azure Machine Learning

• Course by
• 26
• English

AI Foundations for Everyone

Artificial Intelligence (AI) is no longer science fiction. It is rapidly permeating all industries and having a profound impact on virtually every aspect of our existence. Whether you are an executive, a leader, an industry professional, a researcher, or a student - understanding AI, its impact and transformative potential for your organization and our society is of paramount importance. This specialization is designed for those with little or no background in AI, whether you have technology background or not, and does not require any programming skills.

• Course by
• Self Paced
• English

Business Application of Machine Learning and Artificial Intelligence in Healthcare

The future of healthcare is becoming dependent on our ability to integrate Machine Learning and Artificial Intelligence into our organizations. But it is not enough to recognize the opportunities of AI; we as leaders in the healthcare industry have to first determine the best use for these applications ensuring that we focus our investment on solving problems that impact the bottom line. Throughout these four modules we will examine the use of decision support, journey mapping, predictive analytics, and embedding Machine Learning and Artificial Intelligence into the healthcare industry.

• Course by
• Self Paced
• 13 hours
• English

What is Machine Learning?

This series of short videos and accompanying in-classroom lessons will introduce you and your students to how artificial intelligence works and why it matters. You can choose individual lessons based on topic, explore the whole series of topics in one class or teach all 7 lessons to give a deeper dive. Join our movement and register at https://studio.code.org/users/sign_in and learn more at https://code.org/ai/how-ai-works and at https://studio.code.org/courses

• Course by
• 3 min
• English

Exploratory Data Analysis in AWS

Exploratory Data Analysis in AWS is the second course in the AWS Certified Machine Learning Specialty specialization. The main focus of this course is to analyze Data Streams and Data Analytics services in AWS along with exploring Data Analysis in AWS. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.

• Course by
• 5 hours
• English

Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs

In this Google Cloud Labs Specialization, you'll receive hands-on experience building and practicing skills in BigQuery and Cloud Data Fusion.

• Course by
• Self Paced
• English

Explainable deep learning models for healthcare - CDSS 3

This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification.

• Course by
• Self Paced
• 30 hours
• English

Applied Data Science Ethics

AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.

• Course by
• Self Paced
• English

Principles of Data Science Ethics

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics.

This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects and an audit process to follow in reviewing them. Case studies with Python code are provided.

• Course by
• Self Paced
• English

PyTorch Basics for Machine Learning

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

• Course by
• Self Paced
• English

Computer Vision and Image Processing Fundamentals

Learn about computer vision, one of the most exciting fields in machine learning. artificial intelligence and computer science.

• Course by
• Self Paced
• 10
• English

Mathematical Methods for Data Analysis

Learn mathematical methods for data analysis including mathematical formulations and computational methods. Some well-known machine learning algorithms such as k-means are introduced in the examples.

• Course by
• Self Paced
• 21
• English

Digital Marketing Analytics: Tools and Techniques

Learn how to leverage leading tools and approaches to digital marketing data analysis. Dive into SEO and Paid search strategies, web analytics, online testing, machine learning and AI/Big Data applications to strengthen your digital marketing efforts and leverage your resources most effectively.

• Course by
• 10
• English

Data Science: Machine Learning

Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.

• Course by
• 25
• English

MLOps1 (Azure): Deploying AI & ML Models in Production using Microsoft Azure Machine Learning

Most data science projects fail. There are various reasons why, but one of the primary reasons is the challenge of deployment. One piece to the deployment puzzle is understanding how data engineers can effectively work with data scientists to monitor and iterate on model performance, which is why we developed this course: MLOps1 (Azure) - Deploying AI & ML Models in Production using Microsoft Azure Machine Learning

• Course by
• Self Paced
• 28
• English

Introduction to Machine Learning and AI

Discover machine learning and how it works, and train your own AI using free online tools.

• Course by
• 63
• English

Oracle Autonomous Database Administration Workshop

Learn the fundamentals of Autonomous databases and take your DBA skills knowledge to the next level and learn to deploy and administer Autonomous databases. Explore planning, implementing, and deploying an Oracle Autonomous Database, the first and only autonomous database service in the cloud.In this module you will learn how to create applications on Autonomous Database using SQL, APEX, and Oracle Machine Learning.

• Course by
• 12
• English

PyTorch and Deep Learning for Decision Makers

Learn how PyTorch, a deep learning framework, can be used to automate and optimize processes through the development and deployment of state-of-the-art AI applications. The course will also help you understand the importance of data quality, how to choose the right model, and the challenges in deploying and maintaining both deep learning and machine learning applications.

• Course by
• 15
• English

Data Science and Machine Learning Capstone Project

Create a project that you can use to showcase your Data Science skills to prospective employers. Apply various data science and machine learning techniques to analyze and visualize a data set involving a real life business scenario and build a predictive model.

• Course by
• 28
• English

Analyzing Data with Python

In this course, you will learn how to analyze data in Python using multi-dimensional arrays in numpy, manipulate DataFrames in pandas, use SciPy library of mathematical routines, and perform machine learning using scikit-learn!

• Course by
• 15
• English

AI for Everyone: Master the Basics

Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning and neural networks.

• Course by
• 33
• English

Introduction to Machine Learning on AWS

This course is intended for software developers and engineers taking their first steps with the AWS services that do much of heavy lifting of Machine Learning for you.

• Course by
• 15
• English

Machine Learning at the Edge on Arm: A Practical Introduction

****This course will provide you with the hands-on experience you’ll need to create innovative machine learning applications using ubiquitous Arm-based microcontrollers.

• Course by
• 8
• English

Robotic process and intelligent automation for finance

In this course we explain how automation can play a key role in delivering the requirement to have robust processes and clean data. By using automation tools and machine learning, finance leaders can identify, impl

• Course by
• Self Paced
• 15
• English

Machine learning with Python for finance professionals

A machine learning course focused on delivering practical Python skills for finance professionals looking to maximise their use of these time-saving tools within their organisation.

• Course by
• Self Paced
• English