Generative AI for Business Leaders
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How Google does Machine Learning
Our Courses
Collaborative Data Science for Healthcare
Data and learning should be at the front and center of healthcare delivery. In this course, we bring together computer scientists, health providers and social scientists collaborating to improve population health by analyzing and mining data routinely collected in the process of patient care.
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English
Introduction to Computational Thinking and Data Science
6.00.2x is an introduction to using computation to understand real-world phenomena.
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English
Principles, Statistical and Computational Tools for Reproducible Data Science
Learn skills and tools that support data science and reproducible research, to ensure you can trust your own research results, reproduce them yourself, and communicate them to others.
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25
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English
Data Science: Capstone
Show what you've learned from the Professional Certificate Program in Data Science.
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44
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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.
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25
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English
Data Science: Linear Regression
Learn how to use R to implement linear regression, one of the most common statistical modeling approaches in data science.
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35
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English
Digital Humanities in Practice: From Research Questions to Results
Combine literary research with data science to find answers in unexpected ways. Learn basic coding tools to help save time and draw insights from thousands of digital documents at once.
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Self Paced
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14
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English
Predictive Analytics: Basic Modeling Techniques
What is Predictive Analytics? These methods lie behind the most transformative technologies of the last decade, that go under the more general name Artificial Intelligence or AI. In this course, the focus is on the skills that will allow you to fit a model to data, and measure how well it performs. We will be doing enough data science so that you get hands-on familiarity with understanding a dataset, fitting a model to it, and generating predictions.
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26
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English
Introduction to Machine Learning: Supervised Learning
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
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Self Paced
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40 hours
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English
IBM Introduction to Machine Learning
Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). This four-course Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science.
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Self Paced
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English
Network Analysis for Marketing Analytics
Network analysis is a long-standing methodology used to understand the relationships between words and actors in the broader networks in which they exist. This course covers network analysis as it pertains to marketing data, specifically text datasets and social networks. Learners walk through a conceptual overview of network analysis and dive into real-world datasets through instructor-led tutorials in Python.
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Self Paced
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10 hours
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English
Data Science and Agile Systems for Product Management
Deliver faster, higher quality, and fault-tolerant products regardless of industry using the latest in Agile, DevOps, and Data Science.
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Self Paced
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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.
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Self Paced
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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.
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Self Paced
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English
Python for Data Science Project
This mini-course is intended for you to demonstrate foundational Python skills for working with data.
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Self Paced
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English
Analytics for Decision Making
Discover the foundational concepts that support modern data science and learn to analyze various data types and quality to make smart business decisions.
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2
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English
Ethics in AI and Data Science
Learn how to build and incorporate ethical principles and frameworks in your AI and Data Science technology and business initiatives to add transparency, build trust, drive adoption, and lead with trust and responsibility.
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18
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English
MLOps2 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform
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 (GCP): Data Pipeline Automation & Optimization using Google Cloud Platform.
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38
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English
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
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26
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English
MLOps2 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services
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 (AWS): Data Pipeline Automation & Optimization using Amazon Web Services.
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English
MLOps1 (GCP): Deploying AI & ML Models in Production using Google Cloud Platform
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 (GCP) - Deploying AI & ML Models in Production using Google Cloud Platform
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Course by
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Self Paced
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26
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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
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Course by
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Self Paced
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28
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English
MLOps1 (AWS): Deploying AI & ML Models in Production using Amazon Web Services
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 (AWS) - Deploying AI & ML Models in Production using Amazon Web Services.
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35
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English
Data Science for Construction, Architecture and Engineering
This course introduces data science skills targeting applications in the design, construction, and operations of buildings. You will learn practical coding within this context with an emphasis on basic Python programming and the Pandas library.
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5
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English