Great Learning is an ed-tech company that offers programs in career critical competencies such as Analytics, Data Science, Big Data, Machine Learning, Artificial Intelligence, Cloud Computing, DevOps, Full Stack Development and more.

Our programs are taken by thousands of professionals globally who build competencies in these emerging areas to secure and grow their careers. At Great Learning, our focus is on creating industry-relevant programs and crafting learning experiences that help candidates learn, apply and demonstrate capabilities in areas that are driving the future.

List of 15 Free Courses from Great Learning

1. Introduction to Information Security

In this course you will be introduced to basic concepts of information security. You will also get to understand some of the areas and domains where information security is being used and also be exposed to new advancements in the field and areas of cutting edge research such as quantum computing, what it means to conventional information security. The course will also cover application security and software security and examines what can be done in the pre-deployment phase to secure software systems. This free course is offered as part of Great Learning’s learning collaboration with the Stanford Centre for Professional Development.

2. Mastering Big Data Analytics

This course covers two important frameworks Hadoop and Spark, which provide some of the most important tools to carry out enormous big data tasks. The first module of the course will start with the introduction to Big data and soon will advance into big data ecosystem tools and technologies like HDFS, YARN, MapReduce, Hive, etc.

In the second module, the course will take you through an introduction to spark and then dive into Scala and Spark concepts like RDD, transformations, actions, persistence and deploying Spark applications. The course also covers Spark Streaming and Kafka, various data formats like JSON, XML, Avro, Parquet and Protocol Buffers.

3. Microsoft Azure Essentials

Microsoft has made big strides in this domain , and it is no surprise as to why they are one of the leaders when it comes to Cloud Computing. This course is designed to help you get familiar with Microsoft’s Cloud platform , Microsoft Azure.

4. Cloud Computing with AWS

Cloud Computing on AWS is designed to help you learn the absolute basic concepts that you must understand to work with any cloud platform (and not just AWS). This course covers Compute, Storage, Network, Identity & Access Management and Cloud Organization. The course uses AWS, the most popular cloud platform in use today, to explain how to practically implement these concepts.

5. Introduction to Neural Networks and Deep Learning

Deep learning allows machines to solve relatively complex problems even when using data that is diverse, less structured or interdependent. Deep learning is a form of machine learning that is inspired and modeled on how the human brain works. In this course you will be introduced to the basics of deep learning and learn how it compares to other techniques. During the course you will also understand the applications of deep learning in various fields and learn more about different frameworks used for neural networks.

6. Computer Vision Essentials

Computer vision (CV) is a set of techniques that are used to help machines to “see” and comprehend what is contained in digital imagery such as photos and videos. It has varied applications such as in medical imaging, motion capture, surveillance, automating retail checkouts, and in optical character recognition. This course starts with the basic steps of digitizing images, sampling them and compressing them (quantization). It then covers various methods to work with images including classification, identification, detection, etc.

7. Marketing and Retail Analytics

This beginner course explains some basic terminologies used in marketing and discusses an application of analytics in retail. Marketing analytics is the process of measuring, managing and analyzing marketing performance to maximize effectiveness and to optimize return on investment. The course also explains RFM, a marketing technique that is used to determine quantitatively which customers are the best ones by examining factors such as recency of purchase, frequency of purchase, and how much a customer spends.

8. Predictive Modeling and Analytics

In this highly competitive global environment, any professional and progressive organization that aims to grow significantly cannot depend solely on qualitative methods of prediction. Instead, robust quantitative methods that model large amounts of data can be used for more accurate prediction. Predictive Modeling encompasses a variety of techniques that can be used by organizations to predict continuous (for example, sales or demand data) as well as categorical (is someone a buyer or non-buyer?) dependent variables based on a host of input variables (independent variables).

9. Financial Risk Analytics

The financial services industry is increasingly utilizing data and information to help drive decision-making and to help assess and manage risk. This course provides an introduction to Financial Risk Analytics and will help you understand how to assess credit risk, how to model credit risk and also look at methods of optimizing risk.

10. Statistical Methods for Decision Making

All of us in our day-to-day life use numbers in our calculations. Organizations today are inundated with numerical data and information. In business, it is essential for managers to carry out data analysis and be able to interpret their results for effective decision-making. For this, they need to prepare quantitative arguments to justify their decisions. The Statistical Methods for Decision Making (SMDM) course teaches you how to use statistics to help take a real-world problem and apply various techniques to make effective business decisions.

11. Cloud Foundations

In this course, you will be introduced to the basic concepts of cloud computing and become familiar with the history and evolution of cloud computing. Further in the course, you will also get introduced to virtualization and its correlation with cloud computing. You will also get a good understanding of virtualization basics and how VMs compare to containers.

12. Data Visualization using Tableau

A lot of people lack the ability to understand data in its purest form and that is where data visualization comes in. Visualization plays an important role in the representation of both small and large scale datasets. In this course, you will learn to create easy to read and understand graphs, charts and other visual representations of data.

13. Introduction to R

R is a comprehensive statistical and graphical programming language which is fast gaining popularity among data analysts. It is free and runs on a variety of platforms including Windows, Unix, and macOS. It provides an unparalleled platform for programming new statistical methods in an easy and straightforward manner.

14. Machine Learning Foundations

Machine learning uses two types of techniques: one is supervised learning which trains a model on known input and output data so that it can predict future outputs. The second called unsupervised learning finds hidden patterns or intrinsic structures in input data. In this course, you will learn some of the most popular supervised learning algorithms such as KNN and Naive Bayes.

15. Python for Machine Learning

Python is an easy to learn, powerful programming language. You can use Python when your data analysis tasks need to be integrated with web apps or if statistics code needs to be incorporated into a production database.

Being a full-fledged programming language, Python is a great tool to implement algorithms for production use. There are several Python packages for basic data analysis and machine learning. In this course, you will learn about two popular packages in Python: NumPy and Pandas. These are the essential foundational packages that are required for basic data manipulation.