Wikipedia: “Coursera is an American massive open online course (MOOC) provider founded in 2012 by Stanford University’s computer science professors Andrew Ng and Daphne Koller that offers massive open online courses (MOOC), specializations, degrees, professional and master track courses.

Coursera works with universities and other organizations to offer online courses, certifications, and degrees in a variety of subjects, such as engineering, data science, machine learning, mathematics, business, financing, computer science, digital marketing, humanities, medicine, biology, social sciences, 3000 plus a variety of courses giving students a very broad range of information & experience in different fields.”

In this article, we are going to talk about the best FREE courses at Courses, from areas like Artificial Intelligence and Computer Science.

We are going to list courses that are totally FREE including the certificate, or courses that are supported by Financial Aid by Coursera that can assist you with the payments if you can’t afford the prices.

You may not see some of the most popular “free” courses, since most of them are free for the first 7 days, and then you need to pay to continue. The courses you are going to see, as the title suggests are COMPLETELY FREE.

Most of these courses are created by professors at some of the best universities in the world or by some of the best companies in the world, so it is a great chance for you to get the knowledge you need to work in an industry that pays over $100,000 per year, sometimes as an entry salary, based on your skills level.

If you are a frequent visitor to our website, you’ve probably already read our other articles about FREE courses from some of the top universities or companies. If you haven’t here are some of them:

 

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The BEST FREE Artificial Intelligence and Computer Science courses from Coursera

  1. Machine Learning (Stanford University)

“Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome.

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

More importantly, you’ll learn about not only the theoretical underpinnings of learning but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI.

This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Game Theory (Stanford University)

“Popularized by movies such as “A Beautiful Mind,” game theory is the mathematical modeling of strategic interaction among rational (and irrational) agents.

Beyond what we call `games’ in common language, such as chess, poker, soccer, etc., it includes the modeling of conflict among nations, political campaigns, competition among firms, and trading behavior in markets such as the NYSE.

How could you begin to model keyword auctions, and peer to peer file-sharing networks, without accounting for the incentives of the people using them?

The course will provide the basics: representing games and strategies, the extensive form (which computer scientists call game trees), Bayesian games (modeling things like auctions), repeated and stochastic games, and more. We’ll include a variety of examples including classic games and a few applications.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.


 

  1. Data Science Math Skills (Duke University)

“Data science courses contain math-no avoiding that! This course is designed to teach learners the basic math you will need in order to be successful in almost any data science math course and was created for learners who have basic math skills but may not have taken algebra or pre-calculus.

Data Science Math Skills introduces the core math that data science is built upon, with no extra complexity, introducing unfamiliar ideas and math symbols one-at-a-time.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Introduction to Calculus (The University of Sydney)

“The focus and themes of the Introduction to Calculus course address the most important foundations for applications of mathematics in science, engineering, and commerce.

The course emphasizes the key ideas and historical motivation for calculus, while at the same time striking a balance between theory and application, leading to a mastery of key threshold concepts in foundational mathematics.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Introduction to Mathematical Thinking (Stanford University)

“Learn how to think the way mathematicians do – a powerful cognitive process developed over thousands of years. Mathematical thinking is not the same as doing mathematics – at least not as mathematics is typically presented in our school system.

School math typically focuses on learning procedures to solve highly stereotyped problems. Professional mathematicians think a certain way to solve real problems, problems that can arise from the everyday world, or from science, or from within mathematics itself.

The key to success in school math is to learn to think inside-the-box. In contrast, a key feature of mathematical thinking is thinking outside-the-box – a valuable ability in today’s world. This course helps to develop that crucial way of thinking.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Python and Statistics for Financial Analysis (The Hong Kong University of Science and Technology)

“Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry.

The course combines both python coding and statistical concepts and applies to analyzing financial data, such as stock data.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Process Mining: Data science in Action (Eindhoven University of Technology)

“Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy-to-use software, the course provides data science knowledge that can be applied directly to analyze and improve processes in a variety of domains.

Data science is the profession of the future because organizations that are unable to use (big) data in a smart way will not survive. It is not sufficient to focus on data storage and data analysis.

The data scientist also needs to relate data to process analysis. Process mining bridges the gap between traditional model-based process analysis (e.g., simulation and other business process management techniques) and data-centric analysis techniques such as machine learning and data mining.

Process mining seeks the confrontation between event data (i.e., observed behavior) and process models (hand-made or discovered automatically). This technology has become available only recently, but it can be applied to any type of operational processes (organizations and systems).

Example applications include: analyzing treatment processes in hospitals, improving customer service processes in a multinational, understanding the browsing behavior of customers using booking sites, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine.

All of these applications have in common that dynamic behavior needs to be related to process models. Hence, we refer to this as “data science in action”.

The course explains the key analysis techniques in process mining. Participants will learn various process discovery algorithms. These can be used to automatically learn process models from raw event data.

Various other process analysis techniques that use event data will be presented. Moreover, the course will provide easy-to-use software, real-life data sets, and practical skills to directly apply the theory in a variety of application domains.

This course starts with an overview of approaches and technologies that use event data to support decision making and business process (re)design.

Then the course focuses on process mining as a bridge between data mining and business process modeling. The course is at an introductory level with various practical assignments.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Learn to Program: The Fundamentals (University of Toronto)

“Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Excel Fundamentals for Data Analysis (Macquarie University)

“As data becomes the modern currency, so the ability to analyze the data quickly and accurately has become of paramount importance. Excel with its extraordinarily broad range of features and capabilities is one of the most widely used programs for doing this.

In the first course of our Excel Skills for Data Analysis and Visualization Specialization, you will learn the fundamentals of Excel for data analysis.

When you have completed the course, you will be able to use a range of Excel tools and functions to clean and prepare data for analysis; automate data analysis with the help of Named Ranges and Tables; and use logic and lookup functions to transform, link and categorize data.

This course will enable you to build a strong foundation in the fundamentals, helping you to be more efficient in your day-to-day and developing the necessary skills to work with the more advanced techniques used in later courses. To make the content easy to relate to and to personalize the learning experience, we are going to follow Zara’s journey through the course.

Who is Zara? Well, she is no-one and everyone. You will find that Zara’s trials and tribulations sound familiar, and together with Zara, you will develop your Excel skills along the way – and, importantly, have some fun doing it.

The Excel Skills for Data Analytics and Visualization courses are the sequel to one of the most successful specializations on Coursera, Excel Skills for Business, which has attracted hundreds of thousands of learners and top ratings.

Transform your skills, your confidence, and your opportunities by adding this new set of skills to your repertoire.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

 

 

 

  1. Bayesian Statistics: From Concept to Data Analysis (University of California, Santa Cruz)

“This course introduces the Bayesian approach to statistics, starting with the concept of probability and moving to the analysis of data. We will learn about the philosophy of the Bayesian approach as well as how to implement it for common types of data.

We will compare the Bayesian approach to the more commonly-taught Frequentist approach, and see some of the benefits of the Bayesian approach. In particular, the Bayesian approach allows for better accounting of uncertainty, results that have more intuitive and interpretable meaning, and more explicit statements of assumptions.

This course combines lecture videos, computer demonstrations, readings, exercises, and discussion boards to create an active learning experience. For computing, you have the choice of using Microsoft Excel or the open-source, freely available statistical package R, with equivalent content for both options. The lectures provide some of the basic mathematical development as well as explanations of philosophy and interpretation.

Completion of this course will give you an understanding of the concepts of the Bayesian approach, understanding the key differences between Bayesian and Frequentist approaches, and the ability to do basic data analyses.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Foundations of Data Science: K-Means Clustering in Python (University of London)

“Organizations all around the world are using data to predict behaviors and extract valuable real-world insights to inform decisions. Managing and analyzing big data has become an essential part of modern finance, retail, marketing, social science, development and research, medicine and government.

This MOOC, designed by an academic team from Goldsmiths, University of London, will quickly introduce you to the core concepts of Data Science to prepare you for intermediate and advanced Data Science courses. It focuses on the basic mathematics, statistics and programming skills that are necessary for typical data analysis tasks.

You will consider these fundamental concepts on an example data clustering task, and you will use this example to learn basic programming skills that are necessary for mastering Data Science techniques. During the course, you will be asked to do a series of mathematical and programming exercises and a small data clustering project for a given dataset.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Python Programming: A concise introduction (Wesleyan University)

“The goal of the course is to introduce students to Python Version 3.x programming using hands-on instruction. It will show how to install Python and use the Spyder IDE (Integrated Development Environment) for writing and debugging programs.

The approach will be to present an example followed by a small exercise where the learner tries something similar to solidify a concept.

At the end of each module, there will be an exercise where the student is required to write simple programs and submit them for grading. It is intended for students with little or no programming background, although students with such a background should be able to move forward at their preferred pace.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Data Analytics for Lean Six Sigma (Amsterdam University)

“In this course, you will learn data analytics techniques that are typically useful within Lean Six Sigma improvement projects. At the end of this course, you are able to analyze and interpret data gathered within such a project.

You will be able to use Minitab to analyze the data. I will also briefly explain what Lean Six Sigma is. I will emphasize on use of data analytics tools and the interpretation of the outcome. I will use many different examples from actual Lean Six Sigma projects to illustrate all tools.

I will not discuss any mathematical background. The setting we chose for our data example is a Lean Six Sigma improvement project. However, data analytics tools are very widely applicable.

So, you will find that you will learn techniques that you can use in a broader setting apart from improvement projects”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Data Science in Stratified Healthcare and Precision Medicine (The University of Edinburgh)

“An increasing volume of data is becoming available in biomedicine and healthcare, from genomic data to electronic patient records and data collected by wearable devices. Recent advances in data science are transforming the life sciences, leading to precision medicine and stratified healthcare.

In this course, you will learn about some of the different types of data and computational methods involved in stratified healthcare and precision medicine.

You will have hands-on experience of working with such data.  And you will learn from leaders in the field about successful case studies.

Topics include: (i) Sequence Processing, (ii) Image Analysis, (iii) Network Modelling, (iv) Probabilistic Modelling, (v) Machine Learning, (vi) Natural Language Processing, (vii) Process Modelling, and (viii) Graph Data.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Introduction to User Experience Design (Georgia Tech)

“The focus of this course is to introduce the learner to User Experience (UX) Design. User Experience design is a design that is user-centered. The goal is to design artifacts that allow the users to meet their needs in the most effective efficient and satisfying manner.

The course introduces the novice to a cycle of discovery and evaluation and a set of techniques that meet the user’s needs. This course is geared toward the novice. It is for learners that have heard about “user experience” or “user interface” design but don’t really know much about these disciplines.

The course mantra is that “Design is a systematic and data-driven process.” Design is systematic because it is based on a set of techniques and also on a cycle of discovery. In this course, the learner is introduced to the four-step user interface design cycle.

Along the way learners are exposed to a set of techniques to gather information about a) what the user needs b) how to design and model interfaces based on these and then how to evaluate the design to ascertain that the user’s goals are met.

These techniques are tools that are used in a standardized manner and give us the data we use in our design. This means that anyone (regardless of their current training) that is willing to learn these techniques and follow the proposed cycle can be a UX designer!”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

 

  1. Cryptography 1 (Stanford University)

“Cryptography is an indispensable tool for protecting information in computer systems. In this course, you will learn the inner workings of cryptographic systems and how to correctly use them in real-world applications. The course begins with a detailed discussion of how two parties who have a shared secret key can communicate securely when a powerful adversary eavesdrops and tampers with traffic.

We will examine many deployed protocols and analyze mistakes in existing systems. The second half of the course discusses public-key techniques that let two parties generate a shared secret key.

Throughout the course, participants will be exposed to many exciting open problems in the field and work on fun (optional) programming projects. In a second course (Crypto II) we will cover more advanced cryptographic tasks such as zero-knowledge, privacy mechanisms, and other forms of encryption.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Introduction to Systems Engineering (UNSW Sydney)

“The course takes you to step by step through the system life cycle, from design to development, production, and management. You will learn how the different components of a system interrelate, and how each contributes to a project’s goals and success.

The discipline’s terminology, which can so often confuse the newcomer, is presented in an easily digestible form. Weekly video lectures introduce and synthesize key concepts, which are then reinforced with quizzes and practical exercises to help you measure your learning. This course welcomes anyone who wants to find out how complex systems can be developed and implemented successfully.

It is relevant to anyone in project management, engineering, QA, logistic support, operations, management, maintenance, and other work areas. No specific background is required, and we welcome learners with all levels of interest and experience.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Information Systems Auditing, Controls and Assurance (The Hong Kong University of Science and Technology)

“With the latest IS technologies emerging, such as Big Data, FinTech, Virtual Banks, there are more concerns from the public on how organizations maintain systems’ integrity, such as data privacy, information security, the compliance to the government regulations.

Management in organizations also needs to be assured that systems work the way they expected. IS auditors play a crucial role in handling these issues. In the course “Information Systems Auditing, Controls and Assurance”, you will explore the risks of information systems, and how to mitigate the risks by proper IS Controls.

You will also get familiar with the IS Audit procedures and how they are applied during the IS development throughout the Systems Development Life Cycle (SDLC). Finally, you will get to observe how we can make the system changes more manageable using formal IS Management practices, such as Change Management Controls and Emergency Changes.

The conversations between the course instructor – Prof. Percy Dias, and the IS auditing practitioner will give you a concrete idea of how IS auditors perform their duties, the qualities to become IS auditors, and future prospects of the IS auditing industry.

This course is suitable for students and graduates from Information Systems, Information Technology and Computer Science, and IT practitioners who are interested to get into the IS auditing field.

It is also a good starting point for learners who would like to pursue further studies for IS audit certifications – such as Certified Information Systems Auditor (CISA).”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Probability and Statistics: To p or not to p? (University of London)

“We live in an uncertain and complex world, yet we continually have to make decisions in the present with uncertain future outcomes. Indeed, we should be on the look-out for “black swans” – low-probability high-impact events.

To study, or not to study? To invest, or not to invest? To marry, or not to marry?

While uncertainty makes decision-making difficult, it does at least make life exciting!  If the entire future was known in advance, there would never be an element of surprise. Whether a good future or a bad future, it would be a known future.

In this course, we consider many useful tools to deal with uncertainty and help us to make informed (and hence better) decisions – essential skills for a lifetime of good decision-making.

Key topics include quantifying uncertainty with probability, descriptive statistics, point and interval estimation of means and proportions, the basics of hypothesis testing, and a selection of multivariate applications of key terms and concepts seen throughout the course.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

 

  1. Discrete Optimization (The University of Melbourne)

“Optimization technology is ubiquitous in our society. It schedules planes and their crews, coordinates the production of steel, and organizes the transportation of iron ore from the mines to the ports. Optimization clears the day-ahead and real-time markets to deliver electricity to millions of people.

It organizes kidney exchanges and cancer treatments and helps scientists understand the fundamental fabric of life, control complex chemical reactions, and design drugs that may benefit billions of individuals. This class is an introduction to discrete optimization and exposes students to some of the most fundamental concepts and algorithms in the field.

It covers constraint programming, local search, and mixed-integer programming from their foundations to their applications for complex practical problems in areas such as scheduling, vehicle routing, supply-chain optimization, and resource allocation.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Practical Time Series Analysis (Sunny Online)

“Many of us are “accidental” data analysts. We trained in the sciences, business, or engineering and then found ourselves confronted with data for which we have no formal analytic training.

This course is designed for people with some technical competencies who would like more than a “cookbook” approach, but who still need to concentrate on the routine sorts of presentation and analysis that deepen the understanding of our professional topics.

In practical Time Series Analysis, we look at data sets that represent sequential information, such as stock prices, annual rainfall, sunspot activity, the price of agricultural products, and more.

We look at several mathematical models that might be used to describe the processes which generate these types of data. We also look at graphical representations that provide insights into our data. Finally, we also learn how to make forecasts that say intelligent things about what we might expect in the future.

Please take a few minutes to explore the course site. You will find video lectures with supporting written materials as well as quizzes to help emphasize important points. The language for the course is R, a free implementation of the S language.

It is a professional environment and fairly easy to learn.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Introduction to Augmented Reality and ARCore (Daydream)

“This class will teach you the fundamentals of augmented reality (AR), and how to build an AR experience using ARCore. Through the four-week course, you’ll learn:

  • How to identify different types of AR experiences
  • Tools and platforms used in the AR landscape
  • What makes AR feel “real”
  • Popular use cases for AR
  • How to create an AR use flow
  • How AR experiences work
  • Tools like Google Poly and Unity to build AR experiences
  • Next steps to start building an AR experience using ARCore and other tools

This course will break down complex AR concepts to make them easy to understand, while also sharing expert tips and knowledge from Daydream’s ARCore team. The course is great for beginners who are just getting started with AR or ARCore.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

Also, we have a new private Facebook group where we are going to share some materials that are not going to be published online and will be available for our members only. The members will have early access to every new post we make and share your thoughts, tips, articles and questions. Become part of our private Facebook group now.
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  1. Programming Languages, Part A (University of Washington)

“This course is an introduction to the basic concepts of programming languages, with a strong emphasis on functional programming. The course uses the languages ML, Racket, and Ruby as vehicles for teaching the concepts, but the real intent is to teach enough about how any language “fits together” to make you more effective programming in any language — and in learning new ones.

This course is neither particularly theoretical nor just about programming specifics — it will give you a framework for understanding how to use language constructs effectively and how to design correct and elegant programs. By using different languages, you will learn to think more deeply than in terms of the particular syntax of one language.

The emphasis on functional programming is essential for learning how to write robust, reusable, composable, and elegant programs. Indeed, many of the most important ideas in modern languages have their roots in functional programming.

Get ready to learn a fresh and beautiful way to look at software and how to have fun building it. The course assumes some prior experience with programming, as described in more detail in the first module.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Single Variable Calculus (University of Pennsylvania)

“Calculus is one of the grandest achievements of human thought, explaining everything from planetary orbits to the optimal size of a city to the periodicity of a heartbeat. This brisk course covers the core ideas of single-variable Calculus with an emphasis on conceptual understanding and applications.

The course is ideal for students beginning in the engineering, physical, and social sciences. Distinguishing features of the course include: 1) the introduction and use of Taylor series and approximations from the beginning; 2) a novel synthesis of discrete and continuous forms of Calculus; 3) an emphasis on the conceptual over the computational; and 4) a clear, dynamic, unified approach. In this fifth part–part five of five–we cover a calculus for sequences, numerical methods, series and convergence tests, power and Taylor series, and conclude the course with a final exam.

Learners in this course can earn a certificate in the series by signing up for Coursera’s verified certificate program and passing the series’ final exam.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Data Privacy Fundamentals (Northeastern University)

“This course is designed to introduce data privacy to a wide audience and help each participant see how data privacy has evolved as a compelling concern to public and private organizations as well as individuals. In this course, you will hear from legal and technical experts and practitioners who encounter data privacy issues daily.

This course will review theories of data privacy as well as data privacy in the context of social media and artificial intelligence. It will also explore data privacy issues in journalism, surveillance, new technologies like facial recognition and biometrics. Completion of the course will enable the participant to be eligible for CPE credit.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

Become a Creator: Learn More Here

 

  1. Information Security: Context and Introduction (University of London)

“In this course, you will explore information security through some introductory material and gain an appreciation of the scope and context around the subject. This includes a brief introduction to cryptography, security management, and network and computer security that allows you to begin the journey into the study of information security and develop your appreciation of some key information security concepts.

The course concludes with a discussion around a simple model of the information security industry and explores skills, knowledge, and roles so that you can determine and analyze potential career opportunities in this developing profession and consider how you may need to develop personally to attain your career goals.

After completing the course, you will have gained an awareness of key information security principles regarding information, confidentiality, integrity, and availability. You will be able to explain some of the key aspects of information risk and security management, in addition, summarize some of the key aspects of computer and network security, including some appreciation of threats, attacks, exploits, and vulnerabilities.

You will also gain an awareness of some of the skills, knowledge, and roles/careers opportunities within the information security industry.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Code Yourself! An Introduction to Programming (The University of Edinburgh)

“Have you ever wished you knew how to program, but had no idea where to start from? This course will teach you how to program in Scratch, an easy-to-use visual programming language.

More importantly, it will introduce you to the fundamental principles of computing and it will help you think like a software engineer.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Introduction to TCP/IP (Yonsei University)

“You use the Internet through your PC (Personal Computer), laptop, tablet, smart pad, and smartphone every day in everything you do. Through your own PC/laptop, you can easily learn everything about the Internet, and that is what this course is focused on.

In this course ‘Introduction to TCP/IP,’ you will learn the operational functions of Internet technologies (which include IPv4, IPv6, TCP, UDP, addressing, routing, domain names, etc.) and your PC/laptop’s security and gateway Internet setup and basic principles.

In addition, through a simple Wireshark experiment, you will see the TCP/IP packets and security systems in action that are serving your PC/laptop, that serves you.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Biology Meets Programming: Bioinformatics for Beginners (UC San Diego)

“Are you interested in learning how to program (in Python) within a scientific setting? This course will cover algorithms for solving various biological problems along with a handful of programming challenges helping you implement these algorithms in Python.

It offers a gently-paced introduction to our Bioinformatics Specialization (https://www.coursera.org/specializations/bioinformatics), preparing learners to take the first course in the Specialization, “Finding Hidden Messages in DNA” (https://www.coursera.org/learn/dna-analysis). Each of the four weeks in the course will consist of two required components.  First, an interactive textbook provides Python programming challenges that arise from real biological problems.

If you haven’t programmed in Python before, not to worry! We provide “Just-in-Time” exercises from the Codecademy Python track (https://www.codecademy.com/learn/python). And each page in our interactive textbook has its own discussion forum, where you can interact with other learners. Second, each week will culminate in a summary quiz.

Lecture videos are also provided that accompany the material, but these videos are optional.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

 

 

 

  1. Computers, Waves, Simulations: A Practical Introduction to Numerical Methods using Python (LMU Munich)

“Interested in learning how to solve partial differential equations with numerical methods and how to turn them into python codes? This course provides you with a basic introduction of how to apply methods like the finite-difference method, the pseudospectral method, the linear and spectral element method to the 1D (or 2D) scalar wave equation.

The mathematical derivation of the computational algorithm is accompanied by python codes embedded in Jupyter notebooks. In a unique setup, you can see how the mathematical equations are transformed into a computer code and the results visualized.

The emphasis is on illustrating the fundamental mathematical ingredients of the various numerical methods (e.g., Taylor series, Fourier series, differentiation, function interpolation, numerical integration) and how they compare.

You will be provided with strategies on how to ensure your solutions are correct, for example benchmarking with analytical solutions or convergence tests. The mathematical aspects are complemented by a basic introduction to wave physics, discretization, meshes, parallel programming, computing models.

The course targets anyone who aims at developing or using numerical methods applied to partial differential equations and is seeking a practical introduction at a basic level. The methodologies discussed are widely used in natural sciences, engineering, as well as economics and other fields.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Programming Languages, Part B (University of Washington)

“This course is an introduction to the basic concepts of programming languages, with a strong emphasis on functional programming. The course uses the languages ML, Racket, and Ruby as vehicles for teaching the concepts, but the real intent is to teach enough about how any language “fits together” to make you more effective programming in any language — and in learning new ones.

This course is neither particularly theoretical nor just about programming specifics — it will give you a framework for understanding how to use language constructs effectively and how to design correct and elegant programs. By using different languages, you will learn to think more deeply than in terms of the particular syntax of one language.

The emphasis on functional programming is essential for learning how to write robust, reusable, composable, and elegant programs. Indeed, many of the most important ideas in modern languages have their roots in functional programming. Get ready to learn a fresh and beautiful way to look at software and how to have fun building it.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Programming Languages, Part C (University of Washington)

“This course is an introduction to the basic concepts of programming languages, with a strong emphasis on functional programming. The course uses the languages ML, Racket, and Ruby as vehicles for teaching the concepts, but the real intent is to teach enough about how any language “fits together” to make you more effective programming in any language — and in learning new ones.

This course is neither particularly theoretical nor just about programming specifics — it will give you a framework for understanding how to use language constructs effectively and how to design correct and elegant programs. By using different languages, you will learn to think more deeply than in terms of the particular syntax of one language.

The emphasis on functional programming is essential for learning how to write robust, reusable, composable, and elegant programs. Indeed, many of the most important ideas in modern languages have their roots in functional programming.

Get ready to learn a fresh and beautiful way to look at software and how to have fun building it.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Developing Android Apps with App Inventor (The Hong Kong University of Science and Technology)

“The course will give students hands-on experience in developing interesting Android applications. No previous experience in programming is needed, and the course is suitable for students with any level of computing experience.

MIT App Inventor will be used in the course. It is a blocks-based programming tool that allows everyone, even novices, to start programming and build fully functional apps for Android devices.

Students are encouraged to use their own Android devices for hands-on testing and exploitation.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Algorithms, Part 1 (Princeton University)

“This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.

Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free.  It does not offer a certificate upon completion.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

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  1. Algorithms, Part 2 (Princeton University)

“This course covers the essential information that every serious programmer needs to know about algorithms and data structures, with emphasis on applications and scientific performance analysis of Java implementations.

Part I covers elementary data structures, sorting, and searching algorithms. Part II focuses on graph- and string-processing algorithms. All the features of this course are available for free. It does not offer a certificate upon completion.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Computer Science: Programming with a Purpose (Princeton University)

“The basis for education in the last millennium was “reading, writing, and arithmetic;” now it is reading, writing, and computing. Learning to program is an essential part of the education of every student, not just in the sciences and engineering, but in the arts, social sciences, and humanities, as well.

Beyond direct applications, it is the first step in understanding the nature of computer science’s undeniable impact on the modern world. This course covers the first half of our book Computer Science: An Interdisciplinary Approach (the second half is covered in our Coursera course Computer Science: Algorithms, Theory, and Machines).

Our intent is to teach programming to those who need or want to learn it, in a scientific context.  We begin by introducing basic programming elements such as variables, conditionals, loops, arrays, and I/O. Next, we turn to functions, introducing key concepts such as recursion, modular programming, and code reuse. Then, we present a modern introduction to object-oriented programming.

We use the Java programming language and teach basic skills for computational problem solving that are applicable in many modern computing environments. Proficiency in Java is a goal, but we focus on fundamental concepts in programming, not Java per se. All the features of this course are available for free. It does not offer a certificate upon completion.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Audio Signal Processing for Music Applications (Universitat Pompeu Fabra of Barcelona; Stanford University)

“In this course, you will learn about audio signal processing methodologies that are specific for music and of use in real applications.

We focus on the spectral processing techniques of relevance for the description and transformation of sounds, developing the basic theoretical and practical knowledge with which to analyze, synthesize, transform and describe audio signals in the context of music applications. The course is based on open software and content.

The demonstrations and programming exercises are done using Python under Ubuntu, and the references and materials for the course come from open online repositories. We are also distributing with open licenses the software and materials developed for the course.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Data Processing Using Python (Nanjing University)

“This course, as a whole, based on Finance data and through the establishment of popular cases one after another, enables learners to more vividly feel the simplicity, elegance, and robustness of Python.

Also, it discusses the fast, convenient, and efficient data processing capacity of Python in humanities and social sciences fields like literature, sociology, and journalism and science and engineering fields like mathematics and biology, in addition to business fields. Similarly, it may also be flexibly applied into other fields.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Geographical Information Systems, Part 1 (EPFL)

“This course is organized into two parts presenting the theoretical and practical foundations of geographic information systems (GIS).

In this first part of the course, we will focus on the digitization and the storage of geodata. In particular, you will learn:

  • To characterize spatial objects and/or phenomena (territory modeling) with respect to their position in space (through coordinate systems, projections, and spatial relationships) and according to their intrinsic nature (object/vector mode vs. Image/raster mode);
  • About the different means used to acquire spatial data; including direct measurement, georeferencing images, digitization, existing data source, etc.);
  • About the different ways in which geodata can be stored – notably, files and relational databases;
  • How to use data modeling tools to describe and create a spatial database;
  • To query and analyze data using SQL, a common data manipulation language”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

  1. Real-Time Embedded Systems Concepts and Practices (University of Colorado Boulder)

“Course Description: In this course, students will design and build a microprocessor-based embedded system application using a real-time operating system or RT POSIX extensions with Embedded Linux.

The course focus is on the process as well as the fundamentals of integrating microprocessor-based embedded system elements for digital command and control of typical embedded hardware systems.”

The course is totally FREE, but it also can be assisted by the Financial Aid by Coursera.

 

 

Conclusion

So, here is our selection of TOP 40 COMPLETELY FREE Artificial Intelligence and Computer Science courses from Coursera.

Reading the list, you may didn’t met some of the most popular courses from IBM or Google or some other company or organization, and you’ve heard that those are free.

Well, those courses are not completely free, they give you 7 days free trial, and then you need to pay to continue. The courses you see here are completely free and are assisted by the Financial Aid by Coursera.

We hope this will be helpful for you and will inspire you to learn something new since it is completely free and can land you a great job and a great carrier.

Check out our previous articles related to FREE courses:

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Like with every post we do, we encourage you to continue learning, trying, and creating.

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