CIS serves a collaborative platform for students and cloud technology experts to collaborate, exchange insights, and offer mutual support. Online cloud resources are sought for students to equip with the latest cloud technologies and enable them to launch their innovative ideas.
AWS DeepRacer
AWS DeepRacer Student helps high school and college-enrolled students around the globe develop their ML skills in a fun, hands-on autonomous racing league. Any student age 16 or older, can leverage 20 hours of ML educational material and 10 hours of monthly model training compute resources for free. Put your new found skills to the test for your chance to win prizes by becoming one of the top racers in the global student league.
| Course Name | Category | Level | Duration | Platform | What you will learn | Link |
|---|---|---|---|---|---|---|
| Introduction to Artificial Intelligence (IBM) | AI | Beginner | 8 hours | Coursera | In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project. This course does not require any programming or computer science expertise and is designed to introduce the basics of AI to anyone whether you have a technical background or not. | here |
| Conversational AI on Vertex AI and Dialogflow CX | Conversational AI | Intermediate | 5 hours | Cloudskillsboost.google | In this course you will learn how to use the new generative AI features in Dialogflow CX to create virtual agents that can have more natural and engaging conversations with customers. Discover how to deploy generative fallback responses to gracefully handle errors and omissions in customer conversations, deploy generators to increase intent coverage, and structure, ingest, and manage data in a data store. And explore how to deploy and maintain generative AI agents using your data, and deploy and maintain hybrid agents in combination with existing intent-based design paradigms. | here |
| Applied Data Science Capstone | Data & Data Science | Intermediate | 13 hours | Coursera | Demonstrate proficiency in data science and machine learning techniques using a real-world data set and prepare a report for stakeholders Apply your skills to perform data collection, data wrangling, exploratory data analysis, data visualization model development, and model evaluation Write Python code to create machine learning models including support vector machines, decision tree classifiers, and k-nearest neighbors Evaluate the results of machine learning models for predictive analysis, compare their strengths and weaknesses and identify the optimal model | here |
| Data Analysis with Python | Data & Data Science | Beginner | 15 hours | Coursera | Develop Python code for cleaning and preparing data for analysis - including handling missing values, formatting, normalizing, and binning data Perform exploratory data analysis and apply analytical techniques to real-word datasets using libraries such as Pandas, Numpy and Scipy Manipulate data using dataframes, summarize data, understand data distribution, perform correlation and create data pipelines Build and evaluate regression models using machine learning scikit-learn library and use them for prediction and decision making | here |
| Data Analysis with R Programming | Data & Data Science | Beginner | 36 hours | Coursera | Describe the R programming language and its programming environment. Explain the fundamental concepts associated with programming in R including functions, variables, data types, pipes, and vectors. Describe the options for generating visualizations in R. Demonstrate an understanding of the basic formatting in R Markdown to create structure and emphasize content. | here |
| Data Science Methodology | Data & Data Science | Beginner | 6 hours | Coursera | Describe what a data science methodology is and why data scientists need a methodology. Apply the six stages in the Cross-Industry Process for Data Mining (CRISP-DM) methodology to analyze a case study. Evaluate which analytic model is appropriate among predictive, descriptive, and classification models used to analyze a case study. Determine appropriate data sources for your data science analysis methodology. | here |
| Data Visualization with Python | Data & Data Science | Intermediate | 19 hours | Coursera | Implement data visualization techniques and plots using Python libraries, such as Matplotlib, Seaborn, and Folium to tell a stimulating story Create different types of charts and plots such as line, area, histograms, bar, pie, box, scatter, and bubble Create advanced visualizations such as waffle charts, word clouds, regression plots, maps with markers, & choropleth maps Generate interactive dashboards containing scatter, line, bar, bubble, pie, and sunburst charts using the Dash framework and Plotly library | here |
| Databases and SQL for Data Science with Python | Data & Data Science | Beginner | 20 hours | Coursera | Analyze data within a database using SQL and Python. Create a relational database and work with multiple tables using DDL commands. Construct basic to intermediate level SQL queries using DML commands. Compose more powerful queries with advanced SQL techniques like views, transactions, stored procedures, and joins. | here |
| Excel Basics for Data Analysis | Data & Data Science | Beginner | 11 hours | Coursera | Display working knowledge of Excel for Data Analysis. Perform basic spreadsheet tasks including navigation, data entry, and using formulas. Employ data quality techniques to import and clean data in Excel. Analyze data in spreadsheets by using filter, sort, look-up functions, as well as pivot tables. | here |
| Fundamentals of Scalable Data Science | Data & Data Science | Beginner | 27 hours | Coursera | Apache Spark is the de-facto standard for large scale data processing. This is the first course of a series of courses towards the IBM Advanced Data Science Specialization. We strongly believe that is is crucial for success to start learning a scalable data science platform since memory and CPU constraints are to most limiting factors when it comes to building advanced machine learning models. In this course we teach you the fundamentals of Apache Spark using python and pyspark. We'll introduce Apache Spark in the first two weeks and learn how to apply it to compute basic exploratory and data pre-processing tasks in the last two weeks. Through this exercise you'll also be introduced to the most fundamental statistical measures and data visualization technologies. | here |
| Google Data Analytics Professional Certificate | Data & Data Science | Beginner | 6 months (10 hours / week) | Coursera | Gain an immersive understanding of the practices and processes used by a junior or associate data analyst in their day-to-day job Learn key analytical skills (data cleaning, analysis, & visualization) and tools (spreadsheets, SQL, R programming, Tableau) Understand how to clean and organize data for analysis, and complete analysis and calculations using spreadsheets, SQL and R programming Learn how to visualize and present data findings in dashboards, presentations and commonly used visualization platforms | here |
| IBM Data Analyst Professional Certificate | Data & Data Science | Beginner | 4 months (10 hours / week) | Coursera | Master the most up-to-date practical skills and tools that data analysts use in their daily roles Learn how to visualize data and present findings using various charts in Excel spreadsheets and BI tools like IBM Cognos Analytics & Tableau Develop working knowledge of Python language for analyzing data using Python libraries like Pandas and Numpy, and invoke APIs and Web Services Gain technical experience through hands on labs and projects and build a portfolio to showcase your work | here |
| IBM Data Science Professional Certificate | Data & Data Science | Beginner | 5 months (10 hours / week) | Coursera | Master the most up-to-date practical skills and knowledge that data scientists use in their daily roles Learn the tools, languages, and libraries used by professional data scientists, including Python and SQL Import and clean data sets, analyze and visualize data, and build machine learning models and pipelines Apply your new skills to real-world projects and build a portfolio of data projects that showcase your proficiency to employers | here |
| Introduction to Data Analytics | Data & Data Science | Beginner | 10 hours | Coursera | Explain what Data Analytics is and the key steps in the Data Analytics process Differentiate between different data roles such as Data Engineer, Data Analyst, Data Scientist, Business Analyst, and Business Intelligence Analyst Describe the different types of data structures, file formats, and sources of data Describe the data analysis process involving collecting, wrangling, mining, and visualizing data | here |
| Introduction to Data Science Specialization | Data & Data Science | Beginner | 1 months (10 hours / week) | Coursera | Describe what data science and machine learning are, their applications & use cases, and various types of tasks performed by data scientists Gain hands-on familiarity with common data science tools including JupyterLab, R Studio, GitHub and Watson Studio Develop the mindset to work like a data scientist, and follow a methodology to tackle different types of data science problems Write SQL statements and query Cloud databases using Python from Jupyter notebooks | here |
| Microsoft Azure Data Engineering Associate (DP-203) Professional Certificate | Data & Data Science | Intermediate | 3 months (10 hours / week) | Coursera | This Professional Certificate is intended for data engineers and developers who want to demonstrate their expertise in designing and implementing data solutions that use Microsoft Azure data services anyone interested in preparing for the Exam DP-203: Data Engineering on Microsoft Azure. This Professional Certificate will help you develop expertise in designing and implementing data solutions that use Microsoft Azure data services. You will learn how to integrate, transform, and consolidate data from various structured and unstructured data systems into structures that are suitable for building analytics solutions that use Microsoft Azure data services. | here |
| Microsoft Azure Data Scientist Associate (DP-100) Professional Certificate | Data & Data Science | Intermediate | 2 months (10 hours / week) | Coursera | Manage Azure resources for machine learning Run experiments and train models Deploy and operationalize ethical machine learning solutions | here |
| Microsoft Power BI Data Analyst Professional Certificate | Data & Data Science | Beginner | 5 months (10 hours / week) | Coursera | Learn to use Power BI to connect to data sources and transform them into meaningful insights. Prepare Excel data for analysis in Power BI using the most common formulas and functions in a worksheet. Learn to use the visualization and report capabilities of Power BI to create compelling reports and dashboards. Demonstrate your new skills with a capstone project and prepare for the industry-recognized Microsoft PL-300 Certification exam. | here |
| Python Project for Data Science | Data & Data Science | Intermediate | 8 hours | Coursera | Play the role of a Data Scientist / Data Analyst working on a real project. Demonstrate your Skills in Python - the language of choice for Data Science and Data Analysis. Apply Python fundamentals, Python data structures, and working with data in Python. Build a dashboard using Python and libraries like Pandas, Beautiful Soup and Plotly using Jupyter notebook. | here |
| Tools for Data Science | Data & Data Science | Beginner | 18 hours | Coursera | Describe the Data Scientist’s tool kit which includes: Libraries & Packages, Data sets, Machine learning models, and Big Data tools Utilize languages commonly used by data scientists like Python, R, and SQL Demonstrate working knowledge of tools such as Jupyter notebooks and RStudio and utilize their various features Create and manage source code for data science using Git repositories and GitHub. | here |
| What is Data Science? | Data & Data Science | Beginner | 11 hours | Coursera | Define data science and its importance in today’s data-driven world. Describe the various paths that can lead to a career in data science. Summarize advice given by seasoned data science professionals to data scientists who are just starting out. Explain why data science is considered the most in-demand job in the 21st century. | here |
| Microsoft Azure Developer Associate (AZ-204) Professional Certificate | Developer | Intermediate | 2 months (10 hours / week) | Coursera | This Professional Certificate is intended for developers participating in all phases of cloud development from requirements, definition, and design; to development, deployment, and maintenance; to performance tuning and monitoring. This program teaches developers how to create end-to-end solutions in Microsoft Azure. Students will learn how to implement Azure compute solutions, create Azure Functions, implement and manage web apps, develop solutions utilizing Azure storage, implement authentication and authorization, and secure their solutions by using KeyVault and Managed Identities. Students will also learn how to connect to and consume Azure services and third-party services, and include event- and message-based models in their solutions. The Professional Certificate also covers monitoring, troubleshooting, and optimizing Azure solutions. | here |
| Introduction to Gemini for Google Workspace | Duet AI | Introductory | 30 minutes | Cloudskillsboost.google | Gemini for Google Workspace is an add-on that provides customers with generative AI features in Google Workspace. In this learning path, you learn about the key features of Gemini and how they can be used to improve productivity and efficiency in Google Workspace. | here |
| Encoder-Decoder Architecture | Encoders | Intermediate | 8 hours | Cloudskillsboost.google | This course gives you a synopsis of the encoder-decoder architecture, which is a powerful and prevalent machine learning architecture for sequence-to-sequence tasks such as machine translation, text summarization, and question answering. You learn about the main components of the encoder-decoder architecture and how to train and serve these models. In the corresponding lab walkthrough, you’ll code in TensorFlow a simple implementation of the encoder-decoder architecture for poetry generation from the beginning. | here |
| Transformer Models and BERT Model | Encoders | Introductory | 8 hours | Cloudskillsboost.google | This course introduces you to the Transformer architecture and the Bidirectional Encoder Representations from Transformers (BERT) model. You learn about the main components of the Transformer architecture, such as the self-attention mechanism, and how it is used to build the BERT model. You also learn about the different tasks that BERT can be used for, such as text classification, question answering, and natural language inference. | here |
| Prompt Engineering for ChatGPT | Engineering | Beginner | 18 hours | Coursera | How to apply prompt engineering to effectively work with large language models, like ChatGPT How to use prompt patterns to tap into powerful capabilities within large language models How to create complex prompt-based applications for your life, business, or education | here |
| Generative AI for Everyone by Harvard | Generative AI | Introductory | 3 hours | Coursya | Learn how generative AI works, and how to use it in your life and at work. Learn directly from Andrew Ng about the technology of generative AI, how it works, and what it can (and can’t) do. Get an overview of AI tools, and learn from real-world examples of generative AI in use today. Understand the impacts of generative AI on business and society to develop effective AI strategies and approaches | here |
| Introduction to Generative AI | Generative AI | Introductory | 45 minutes | Cloudskillsboost.google | This is an introductory level microlearning course aimed at explaining what Generative AI is, how it is used, and how it differs from traditional machine learning methods. It also covers Google Tools to help you develop your own Gen AI apps. | here |
| Create Image Captioning Models | Image | Intermediate | 8 hours | Cloudskillsboost.google | This course teaches you how to create an image captioning model by using deep learning. You learn about the different components of an image captioning model, such as the encoder and decoder, and how to train and evaluate your model. By the end of this course, you will be able to create your own image captioning models and use them to generate captions for images. | here |
| Introduction to Image Generation | Image | Introductory | 8 hours | Cloudskillsboost.google | This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI. | here |
| Advanced Machine Learning on Google Cloud Specialization | Machine Learning | Advanced | 2 months (10 hours / week) | Coursera | This 5-course specialization focuses on advanced machine learning topics using Google Cloud Platform where you will get hands-on experience optimizing, deploying, and scaling production ML models of various types in hands-on labs. This specialization picks up where “Machine Learning on GCP” left off and teaches you how to build scalable, accurate, and production-ready models for structured data, image data, time-series, and natural language text. It ends with a course on building recommendation systems. Topics introduced in earlier courses are referenced in later courses, so it is recommended that you take the courses in exactly this order. This specialization incorporates hands-on labs using our Qwiklabs platform.These hands on components will let you apply the skills you learn in the video lectures. Projects will incorporate topics such as Google Cloud Platform products, which are used and configured within Qwiklabs. You can expect to gain practical hands-on experience with the concepts explained throughout the modules. | here |
| Data Science: Statistics and Machine Learning Specialization | Machine Learning | Intermediate | 3 months (10 hours / week) | Coursera | Build models, make inferences, and deliver interactive data products. This specialization continues and develops on the material from the Data Science: Foundations using R specialization. It covers statistical inference, regression models, machine learning, and the development of data products. In the Capstone Project, you’ll apply the skills learned by building a data product using real-world data. At completion, learners will have a portfolio demonstrating their mastery of the material. The five courses in this specialization are the very same courses that make up the second half of the Data Science Specialization. This specialization is presented for learners who have already mastered the fundamentals and want to skip right to the more advanced courses. | here |
| Exploratory Data Analysis for Machine Learning | Machine Learning | Intermediate | 14 hours | Coursera | This first course in the IBM Machine Learning Professional Certificate introduces you to Machine Learning and the content of the professional certificate. In this course you will realize the importance of good, quality data. You will learn common techniques to retrieve your data, clean it, apply feature engineering, and have it ready for preliminary analysis and hypothesis testing. By the end of this course you should be able to: Retrieve data from multiple data sources: SQL, NoSQL databases, APIs, Cloud Describe and use common feature selection and feature engineering techniques Handle categorical and ordinal features, as well as missing values Use a variety of techniques for detecting and dealing with outliers Articulate why feature scaling is important and use a variety of scaling techniques | here |
| Google Cloud Solutions II: Data and Machine Learning | Machine Learning | Advanced | 4 hours | Cloudskillsboost.google | In this advanced-level quest, you will learn how to harness serious Google Cloud computing power to run big data and machine learning jobs. The hands-on labs will give you use cases, and you will be tasked with implementing big data and machine learning practices utilized by Google’s very own Solutions Architecture team. From running Big Query analytics on tens of thousands of basketball games, to training TensorFlow image classifiers, you will quickly see why Google Cloud is the go-to platform for running big data and machine learning jobs. | here |
| Machine Learning with Python | Machine Learning | Intermediate | 12 hours | Coursera | This course is for you whether you want to advance your Data Science career or get started in Machine Learning and Deep Learning. This course will begin with a gentle introduction to Machine Learning and what it is, with topics like supervised vs unsupervised learning, linear & non-linear regression, simple regression and more. You will then dive into classification techniques using different classification algorithms, namely K-Nearest Neighbors (KNN), decision trees, and Logistic Regression. You’ll also learn about the importance and different types of clustering such as k-means, hierarchical clustering, and DBSCAN. With all the many concepts you will learn, a big emphasis will be placed on hands-on learning. You will work with Python libraries like SciPy and scikit-learn and apply your knowledge through labs. In the final project you will demonstrate your skills by building, evaluating and comparing several Machine Learning models using different algorithms. By the end of this course, you will have job ready skills to add to your resume and a certificate in machine learning to prove your competency. | here |
| Machine Learning with Python | Machine Learning | - | - | Freecodecamp | Machine learning has many practical applications that you can use in your projects or on the job. In the Machine Learning with Python Certification, you'll use the TensorFlow framework to build several neural networks and explore more advanced techniques like natural language processing and reinforcement learning. You'll also dive into neural networks, and learn the principles behind how deep, recurrent, and convolutional neural networks work. | here |
| ML Pipelines on Google Cloud | Machine Learning | Advanced | 13.25 hours | Cloudskillsboost.google | In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata. Then we will change focus to discuss how we can automate and reuse ML pipelines across multiple ML frameworks such as tensorflow, pytorch, scikit learn, and xgboost. You will also learn how to use another tool on Google Cloud, Cloud Composer, to orchestrate your continuous training pipelines. And finally, we will go over how to use MLflow for managing the complete machine learning life cycle. | here |
| Structuring Machine Learning Projects | Machine Learning | Beginner | 6 hours | Coursera | In the third course of the Deep Learning Specialization, you will learn how to build a successful machine learning project and get to practice decision-making as a machine learning project leader. By the end, you will be able to diagnose errors in a machine learning system; prioritize strategies for reducing errors; understand complex ML settings, such as mismatched training/test sets, and comparing to and/or surpassing human-level performance; and apply end-to-end learning, transfer learning, and multi-task learning. This is also a standalone course for learners who have basic machine learning knowledge. This course draws on Andrew Ng’s experience building and shipping many deep learning products. If you aspire to become a technical leader who can set the direction for an AI team, this course provides the “industry experience” that you might otherwise get only after years of ML work experience. The Deep Learning Specialization is our foundational program that will help you understand the capabilities, challenges, and consequences of deep learning and prepare you to participate in the development of leading-edge AI technology. It provides a pathway for you to gain the knowledge and skills to apply machine learning to your work, level up your technical career, and take the definitive step in the world of AI. | here |
| TensorFlow on Google Cloud | Machine Learning | Intermediate | 13 hours | Coursera | This course covers designing and building a TensorFlow input data pipeline, building ML models with TensorFlow and Keras, improving the accuracy of ML models, writing ML models for scaled use, and writing specialized ML models. | here |
| Advanced Learning Algorithms | Models, Mechanisms, and Algos | Beginner | 34 hours | Coursera | Build and train a neural network with TensorFlow to perform multi-class classification Apply best practices for machine learning development so that your models generalize to data and tasks in the real world Build and use decision trees and tree ensemble methods, including random forests and boosted trees | here |
| Attention Mechanism | Models, Mechanisms, and Algos | Intermediate | 8 hours | Cloudskillsboost.google | This course will introduce you to the attention mechanism, a powerful technique that allows neural networks to focus on specific parts of an input sequence. You will learn how attention works, and how it can be used to improve the performance of a variety of machine learning tasks, including machine translation, text summarization, and question answering. | here |
| Introduction to Large Language Models | Models, Mechanisms, and Algos | Introductory | 30 minutes | Cloudskillsboost.google | This is an introductory level micro-learning course that explores what large language models (LLM) are, the use cases where they can be utilized, and how you can use prompt tuning to enhance LLM performance. It also covers Google tools to help you develop your own Gen AI apps. | here |
| Agile Project Management | Project Management | Beginner | 28 hours | Coursera | Explain the Agile project management approach and philosophy, including values and principles. Discuss the pillars of Scrum and how they support Scrum values. Describe the five important Scrum events and how to set up each event for a Scrum team. Explain how to coach an Agile team and help them overcome challenges. | here |
| Foundations of Project Management | Project Management | Beginner | 18 hours | Coursera | Describe project management skills, roles, and responsibilities across a variety of industries Explain the project management life cycle and compare different program management methodologies Define organizational structure and organizational culture and explain how it impacts project management. | here |
| Google Project Management: Professional Certificate | Project Management | Beginner | 6 months (10 hours / week) | Coursera | Gain an immersive understanding of the practices and skills needed to succeed in an entry-level project management role Learn how to create effective project documentation and artifacts throughout the various phases of a project Learn the foundations of Agile project management, with a focus on implementing Scrum events, building Scrum artifacts, and understan |