Nowadays, it’s no surprise that online learning is booming throughout the world. If you like to find a course to help you boost your career, take a look at our list of the top 10 free online technical courses provided by Harvard University.
1. CS50’s Introduction to Game Development
In a quest to understand how video games themselves are implemented, you’ll explore the design of such childhood games as: Super Mario Bros., Pong, Flappy Bird, Breakout, Match 3, Legend of Zelda, Angry Birds, Pokémon, 3D Helicopter Game, Dreadhalls, and Portal.
Via lectures and hands-on projects, the course explores principles of 2D and 3D graphics, animation, sound, and collision detection using frameworks like Unity and LÖVE 2D, as well as languages like Lua and C#. By class’s end, you’ll have programmed several of your own games and gained a thorough understanding of the basics of game design and development.
Topics include database design, scalability, security, and user experience. Through hands-on projects, you’ll learn to write and use APIs, create interactive UIs, and leverage cloud services like GitHub and Heroku. By course’s end, you’ll emerge with knowledge and experience in principles, languages, and tools that empower you to design and deploy applications on the Internet.
3. Introduction to Computer Science
Students who earn a satisfactory score on 9 problem sets (i.e., programming assignments) and a final project are eligible for a certificate. This is a self-paced course–you may take CS50x on your own schedule.
4. CS50’s Introduction to Artificial Intelligence with Python
AI is transforming how we live, work, and play. By enabling new technologies like self-driving cars and recommendation systems or improving old ones like medical diagnostics and search engines, the demand for expertise in AI and machine learning is growing rapidly. This course will enable you to take the first step toward solving important real-world problems and future-proofing your career.
CS50’s Introduction to Artificial Intelligence with Python explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By course’s end, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.
Enrol now to gain expertise in one of the fastest-growing domains of computer science from the creators of one of the most popular computer science courses ever, CS50. You’ll learn the theoretical frameworks that enable these new technologies while gaining practical experience in how to apply these powerful techniques in your work.
5. The Future of ML is Tiny and Bright
In this exciting Professional Certificate program offered by Harvard University and Google TensorFlow, you will learn about the emerging field of Tiny Machine Learning (TinyML), its real-world applications, and the future possibilities of this transformative technology.
TinyML is a cutting-edge field that brings the transformative power of machine learning (ML) to the performance- and power-constrained domain of embedded systems. Successful deployment in this field requires intimate knowledge of applications, algorithms, hardware, and software.
The program will emphasize hands-on experience with training and deploying machine learning into tiny embedded devices. This series of courses features projects based on a TinyML Program Kit that includes an Arduino board with onboard sensors and an ARM Cortex-M4 microcontroller. To ensure you hit the road running, the kit also comes equipped with a camera. The TinyML Program Kit has everything you need to unlock your imagination and build applications around image recognition, audio processing, and gesture detection. Before you know it, you’ll be implementing an entire tiny machine learning application.
This first-of-its-kind program combines computer science with engineering to feature real-world application case studies that examine the challenges facing TinyML deployments.
This program is a collaboration between expert faculty at Harvard’s John A. Paulson School of Engineering and Applied Sciences (SEAS) and innovative members of Google’s TensorFlow team.
6. Applications of TinyML
Do you know what happens when you say “OK Google” to a Google device? Is your Google Home always listening?
Following on the Foundations of Tiny ML course, Applications of TinyML will give you the opportunity to see tiny machine learning applications in practice. This course features real-world case studies, guided by industry leaders, that examine deployment challenges on tiny or deeply embedded devices.
Dive into the code for using sensor data for tasks such as gesture detection and voice recognition. Focusing on the neural network of the applications, specifically on training and inference, you will review the code behind “OK Google,” “Alexa,” and smartphone features on Android and Apple . Learn about real-word industry applications of TinyML as well as Keyword Spotting, Visual Wake Words, Anomaly Detection, Dataset Engineering, and Responsible Artificial Intelligence.
Tiny Machine Learning (TinyML) is one of the fastest-growing areas of deep learning and is rapidly becoming more accessible. The second course in the TinyML Professional Certificate program, Applications of TinyML shows you the code behind some of the world’s most widely-used TinyML devices.
7. Data Science: Wrangling
In this course, part of our Professional Certificate Program in Data Science, we cover several standard steps of the data wrangling process like importing data into R, tidying data, string processing, HTML parsing, working with dates and times, and text mining. Rarely are all these wrangling steps necessary in a single analysis, but a data scientist will likely face them all at some point.
Very rarely is data easily accessible in a data science project. It’s more likely for the data to be in a file, a database, or extracted from documents such as web pages, tweets, or PDFs. In these cases, the first step is to import the data into R and tidy the data, using the tidyverse package. The steps that convert data from its raw form to the tidy form is called data wrangling.
This process is a critical step for any data scientist. Knowing how to wrangle and clean data will enable you to make critical insights that would otherwise be hidden.
8. Data Science: Machine Learning
Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors.
In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system.
You will learn about training data, and how to use a set of data to discover potentially predictive relationships. As you build the movie recommendation system, you will learn how to train algorithms using training data so you can predict the outcome for future datasets. You will also learn about overtraining and techniques to avoid it such as cross-validation. All of these skills are fundamental to machine learning.
9. Data Science: Visualization
As part of our Professional Certificate Program in Data Science, this course covers the basics of data visualization and exploratory data analysis. We will use three motivating examples and ggplot2, a data visualization package for the statistical programming language R. We will start with simple datasets and then graduate to case studies about world health, economics, and infectious disease trends in the United States.
We’ll also be looking at how mistakes, biases, systematic errors, and other unexpected problems often lead to data that should be handled with care. The fact that it can be difficult or impossible to notice a mistake within a dataset makes data visualization particularly important.
The growing availability of informative datasets and software tools has led to increased reliance on data visualizations across many areas. Data visualization provides a powerful way to communicate data-driven findings, motivate analyses, and detect flaws. This course will give you the skills you need to leverage data to reveal valuable insights and advance your career.
10. Data Science: Productivity Tools
A typical data analysis project may involve several parts, each including several data files and different scripts with code. Keeping all this organized can be challenging.
Part of our Professional Certificate Program in Data Science, this course explains how to use Unix/Linux as a tool for managing files and directories on your computer and how to keep the file system organized. You will be introduced to the version control systems git, a powerful tool for keeping track of changes in your scripts and reports. We also introduce you to GitHub and demonstrate how you can use this service to keep your work in a repository that facilitates collaborations.
Finally, you will learn to write reports in R markdown which permits you to incorporate text and code into a document. We’ll put it all together using the powerful integrated desktop environment RStudio.