Wikipedia: “The Ivy League is an American collegiate athletic conference comprising eight private universities in the Northeastern United States.

The term Ivy League is typically used beyond the sports context to refer to the eight schools as a group of elite colleges with connotations of academic excellence, selectivity in admissions, and social elitism.

Its members in alphabetical order are Brown University, Columbia University, Cornell University, Dartmouth College, Harvard University, the University of Pennsylvania, Princeton University, and Yale University.”

In the past we’ve made a couple of articles related to free courses that you’ve liked:

In this spirit, we’ve decided to make a larger list of courses related to AI, CS, and Programming from the Ivy League. The Ivy League has the best courses in the world, and we feel that free courses from this caliber can help you a lot.

Most of the courses are FREE to attend to, some of them may have some sort of certificate that may require some sort of payment, but you will be NOT required to pay, since the certificate does not represent your level of knowledge, but your work does.

So, our advice is, if you can’t afford to pay for the certificates, then don’t, the most important thing is to learn something from these courses, then later you can use it in your projects.

From our experience so far, the certificates cost around $400-$500, and you will get a couple of them, which makes them cheaper than your local web design academy, but as we’ve said, those are not important, focus on learning.

 

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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Top 50 FREE courses from the Ivy League:

Keep in mind that the order of the courses is completely random. So, let’s begin:

  1. Algorithms, 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.”

 

  1. Algorithms, 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”

 

  1. Artificial Intelligence (Columbia University)

“What do self-driving cars, face recognition, web search, industrial robots, missile guidance, and tumor detection have in common? They are all complex real-world problems being solved with applications of intelligence (AI). This course will provide a broad understanding of the basic techniques for building intelligent computer systems and an understanding of how AI is applied to problems.

You will learn about the history of AI, intelligent agents, state-space problem representations, uninformed and heuristic search, game playing, logical agents, and constraint satisfaction problems. Hands-on experience will be gained by building a basic search agent. Adversarial search will be explored through the creation of a game and an introduction to machine learning includes work on linear regression.”

 

  1. Machine Learning (Columbia University)

“Machine Learning is the basis for the most exciting careers in data analysis today. You’ll learn the models and methods and apply them to real-world situations ranging from identifying trending news topics to building recommendation engines, ranking sports teams, and plotting the path of movie zombies. Major perspectives covered include: probabilistic versus non-probabilistic modeling, supervised versus unsupervised learning.

Topics include: classification and regression, clustering methods, sequential models, matrix factorization, topic modeling, and model selection. Methods include: linear and logistic regression, support vector machines, tree classifiers, boosting, maximum likelihood and MAP inference, EM algorithm, hidden Markov models, Kalman filters, k-means, Gaussian mixture models, among others.

In the first half of the course, we will cover supervised learning techniques for regression and classification. In this framework, we possess an output or response that we wish to predict based on a set of inputs. We will discuss several fundamental methods for performing this task and algorithms for their optimization.

Our approach will be more practically motivated, meaning we will fully develop a mathematical understanding of the respective algorithms, but we will only briefly touch on abstract learning theory. In the second half of the course, we shift to unsupervised learning techniques. In these problems the end goal less clear-cut than predicting an output based on a corresponding input.

We will cover three fundamental problems of unsupervised learning: data clustering, matrix factorization, and sequential models for order-dependent data. Some applications of these models include object recommendation and topic modeling.”


 

  1. Reinforcement Learning (Brown University and Georgia Tech)

“You should take this course if you have an interest in machine learning and the desire to engage with it from a theoretical perspective. Through a combination of classic papers and more recent work, you will explore automated decision-making from a computer-science perspective.

You will examine efficient algorithms, where they exist, for single-agent and multi-agent planning as well as approaches to learning near-optimal decisions from experience. At the end of the course, you will replicate a result from a published paper in reinforcement learning.”

 

  1. Robotics: Perception (University of Pennsylvania)

“How can robots perceive the world and their own movements so that they accomplish navigation and manipulation tasks?  In this module, we will study how images and videos acquired by cameras mounted on robots are transformed into representations like features and optical flow.

Such 2D representations allow us then to extract 3D information about where the camera is and in which direction the robot moves.  You will come to understand how grasping objects is facilitated by the computation of 3D posing of objects and navigation can be accomplished by visual odometry and landmark-based localization.”

 

  1. C Programming: Pointers and Memory Management (Dartmouth University)

“In this course, we will examine a key concept, foundational to any programming language: the usage of memory. This course builds upon the basic concept of pointers, discussed in C Programming: Modular Programming and Memory Management, and introduces the more advanced usage of pointers and pointer arithmetic. Arrays of pointers and multidimensional arrays are addressed, and you will learn how to allocate memory for your own data during program execution.

This is called dynamic memory allocation at runtime using pointers. Within moments you will be coding hands-on in a new browser tool developed for this course, receiving instant feedback on your code. No need to install anything! In this course, you will gain experience with programming concepts that are foundational to any programming language.

At the end of this short course, you will reach the fourth milestone in the C Programming with Linux Professional Certificate program, unlocking the door to a career in computer engineering. This course has received financial support from the Patrick & Lina Drahi Foundation.”

 

  1. Algorithm Design and Analysis (University of Pennsylvania)

“How do you optimally encode a text file? How do you find the shortest paths on a map? How do you design a communication network? How do you route data in a network? What are the limits of efficient computation? This course, part of the Computer Science Essentials for Software Development Professional Certificate program, is an introduction to the design and analysis of algorithms and answers along the way these and many other interesting computational questions.

You will learn about algorithms that operate on common data structures, for instance sorting and searching; advanced design and analysis techniques such as dynamic programming and greedy algorithms; advanced graph algorithms such as minimum spanning trees and shortest paths; NP-completeness theory; and approximation algorithms. After completing this course, you will be able to design efficient and correct algorithms using sophisticated data structures for complex computational tasks”

 

  1. C Programming: Getting Started (Dartmouth University)

“In this course, you will learn the principles of C programming and start coding hands-on in a browser tool that will provide instant feedback on your code. The C programming language is one of the most stable and popular programming languages in the world. It helps to power your smartphone, your car’s navigation system, robots, drones, trains, and almost all electronic devices.

C is used in any circumstances where speed and flexibility are important, such as in embedded systems or high-performance computing. In this course, you will get started with C and learn how to write your first programs, how to make simple computations and print the results to the screen, how to store values in variables and how to repeat instructions using loops.

Beginners, even those without any programming experience, will be able to immediately start coding in C with the help of powerful yet simple coding tools right within the web browser. No need to install anything! We are excited to introduce you to the world of coding and launch you along your path to becoming a skilled C programmer!

This is the first course in the C Programming with Linux Professional Certificate program. This series of seven short courses will establish your programming skills and unlock doors to careers in computer engineering. This course has received financial support from the Patrick & Lina Drahi Foundation.”

 

  1. C Programming: Advanced Data Types (Dartmouth University)

“In this course, part of the C Programming with Linux Professional Certificate program, you will define your own data types in C, and use the newly created types to more efficiently store and process your data. Many programming languages provide a number of built-in data types to store things such as integers, decimals, and characters in variables, but what if you wanted to store more complex data? Defining your own data types in C allows you to more efficiently store and process data such as a customer’s name, age, and other relevant data, all in one single variable!

This course will provide a hands-on coding experience in a new browser tool developed for this course that will allow you to receive instant feedback on your code. No need to install anything! You will also gain experience with programming concepts that are foundational to any programming language.

At the end of this short course, you will reach the fifth milestone of the C Programming with Linux Professional Certificate program, unlocking the door to a career in computer engineering. This course has received financial support from the Patrick & Lina Drahi Foundation.”

 

  1. Data Science: Probability (Harvard University)

“In this course, part of our Professional Certificate Program in Data Science, you will learn valuable concepts in probability theory. The motivation for this course is the circumstances surrounding the financial crisis of 2007-2008. Part of what caused this financial crisis was that the risk of some securities sold by financial institutions was underestimated.

To begin to understand this very complicated event, we need to understand the basics of probability. We will introduce important concepts such as random variables, independence, Monte Carlo simulations, expected values, standard errors, and the Central Limit Theorem.

These statistical concepts are fundamental to conducting statistical tests on data and understanding whether the data you are analyzing is likely occurring due to an experimental method or to chance. Probability theory is the mathematical foundation of statistical inference which is indispensable for analyzing data affected by chance, and thus essential for data scientists.”

 

  1. Data Science: Linear Regression (Harvard University)

“Linear regression is commonly used to quantify the relationship between two or more variables. It is also used to adjust for confounding. This course, part of our Professional Certificate Program in Data Science, covers how to implement linear regression and adjust for confounding in practice using R.

In data science applications, it is very common to be interested in the relationship between two or more variables. The motivating case study we examine in this course relates to the data-driven approach used to construct baseball teams described in Moneyball.

We will try to determine which measured outcomes best predict baseball runs by using linear regression. We will also examine confounding, where extraneous variables affect the relationship between two or more other variables, leading to spurious associations. Linear regression is a powerful technique for removing confounders, but it is not a magical process. It is essential to understand when it is appropriate to use, and this course will teach you when to apply this technique.”

 

  1. High-Dimensional Data Analysis (Harvard University)

“If you’re interested in data analysis and interpretation, then this is the data science course for you. We start by learning the mathematical definition of distance and use this to motivate the use of the singular value decomposition (SVD) for dimension reduction of high-dimensional data sets, and multi-dimensional scaling and its connection to principal component analysis.

We will learn about the batch effect, the most challenging data analytical problem in genomics today, and describe how the techniques can be used to detect and adjust for batch effects. Specifically, we will describe the principal component analysis and factor analysis and demonstrate how these concepts are applied to data visualization and data analysis of high-throughput experimental data. Finally, we give a brief introduction to machine learning and apply it to high-throughput, large-scale data.

We describe the general idea behind clustering analysis and descript K-means and hierarchical clustering and demonstrate how these are used in genomics and describe prediction algorithms such as k-nearest neighbors along with the concepts of training sets, test sets, error rates, and cross-validation.

Given the diversity in the educational background of our students, we have divided the series into seven parts. You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures.

Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.”

 

 

  1. Data Science: Visualization (Harvard University)

“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.”

 

  1. Software Development Fundamentals (University of Pennsylvania)

“Software developers are in high demand in the current job market, and computer programming is a prerequisite skill for success in this field. Start your journey toward becoming a professional software developer by learning Java, one of the industry’s most commonly used programming languages.

This course, part of the CS Essentials for Software Development Professional Certificate program, will quickly cover Java syntax and keywords and then explore features of object-oriented programming including encapsulation, inheritance, and polymorphism.

You will learn how to apply these concepts to programmatic problem solving by investigating class modeling techniques and relationships such as aggregation, realization, and generalization. In addition to programming, you will learn about software testing techniques that help us find problems in our code, and you will use modern development environments and tools for tasks like debugging and unit testing.

We will introduce Eclipse, the Eclipse debugger, and Junit (a unit testing framework). After completing this course, you will be able to design, develop, and test large applications in Java and understand and apply core principles of professional software development.”

 

  1. CS50’s Mobile App Development with React Native (Harvard University)

“This course picks up where CS50 leaves off, transitioning from web development to mobile app development with React Native. The course introduces you to modern JavaScript (including ES6 and ES7) as well as to JSX, a JavaScript extension. Through hands-on projects, you’ll gain experience with React and its paradigms, app architecture, and user interfaces. The course culminates in a final project for which you’ll implement an app entirely of your own design.”

 

  1. Programming for the Web with JavaScript (University of Pennsylvania)

“JavaScript is the programming language of the World Wide Web. As a professional web software developer, you will not only need to know how to program in this simple yet powerful language, but you will need to understand the fundamentals of how data is exchanged on the World Wide Web (WWW) and what tools and frameworks are available to you for creating robust, interactive web applications.

This course, part of the CS Essentials for Software Development Professional Certificate program, provides an introduction to modern web development using JavaScript. In addition to exploring the basics of web page creation using HTML and CSS, you will learn advanced web page layout and responsive design tools such as Bootstrap.

You will also learn how browsers represent web page data using the Document Object Model (DOM) and how to develop dynamic, interactive web pages using JavaScript in the browser. Beyond fundamental JavaScript syntax and advanced language features such as callbacks, events, and asynchronous programming, you will work with jQuery, which provides functionality for simplified DOM manipulation and event handling.

This course will also introduce you to modern web frameworks and component-based libraries such as React.js for efficiently developing modular web page components and D3.js for creating data-driven documents. We will also teach you how to represent and exchange data using JavaScript Object Notation (JSON), and how to access RESTful APIs on the web.

Server-side JavaScript is becoming more prevalent in the industry, with web frameworks such as Node.js and Express making it simple to create and deploy complex, data-driven web applications. This course will prepare you to use such frameworks and show you how to integrate them with NoSQL databases such as MongoDB.”

 

  1. CS50’s Web Programming with Python and JavaScript (Harvard University)

“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.”

 

  1. Statistical Inference and Modeling for High-throughput Experiments (Harvard University)

“In this course, you’ll learn various statistics topics including multiple testing problem, error rates, error rate controlling procedures, false discovery rates, q-values, and exploratory data analysis. We then introduce statistical modeling and how it is applied to high-throughput data. In particular, we will discuss parametric distributions, including binomial, exponential, and gamma, and describe maximum likelihood estimation. We provide several examples of how these concepts are applied in next-generation sequencing and microarray data.

Finally, we will discuss hierarchical models and empirical Bayes along with some examples of how these are used in practice. We provide R programming examples in a way that will help make the connection between concepts and implementation. Given the diversity in the educational background of our students, we have divided the series into seven parts.

You can take the entire series or individual courses that interest you. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures.

Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.”

 

  1. Calculus: Single Variable Part 1 – Functions (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 first part–part one of five–you will extend your understanding of the Taylor series, review limits, learn the *why* behind l’Hopital’s rule, and, most importantly, learn a new language for describing the growth and decay of functions: the BIG O.”

 

 

 

 

  1. Calculus: Single Variable Part 2 – Differentiation (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 second part–part two of five–we cover derivatives, differentiation rules, linearization, higher derivatives, optimization, differentials, and differentiation operators.”

 

  1. Calculus: Single Variable Part 3 – Integration (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 third part–part three of five–we cover integrating differential equations, techniques of integration, the fundamental theorem of integral calculus, and difficult integrals.”

 

  1. Calculus: Single Variable Part 4 – Applications (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 fourth part–part four of five–we cover computing areas and volumes, other geometric applications, physical applications, and averages and mass.  We also introduce the probability.”

 

  1. Introduction to Linear Models and Matrix Algebra (Harvard University)

“Matrix Algebra underlies many of the current tools for experimental design and the analysis of high-dimensional data. In this introductory online course in data analysis, we will use matrix algebra to represent the linear models that commonly used to model differences between experimental units.

We perform statistical inference on these differences. Throughout the course, we will use the R programming language to perform matrix operations. Given the diversity in the educational background of our students, we have divided the series into seven parts. You can take the entire series or individual courses that interest you.

If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. Note that the statistics and programming aspects of the class ramp up in difficulty relatively quickly across the first three courses.

You will need to know some basic stats for this course. By the third course will be teaching advanced statistical concepts such as hierarchical models and by the fourth advanced software engineering skills, such as parallel computing and reproducible research concepts.”

 

  1. A Hands-on Introduction to Engineering Simulations (Cornell University)

“In this hands-on course, you’ll learn how to perform engineering simulations using a powerful tool from ANSYS, Inc. This is a problem-based course where you’ll learn by doing. The focus will be on understanding what’s under the black-box so as to move beyond garbage-in, garbage-out. You’ll practice using a common solution approach to problems involving different physics: structural mechanics, fluid dynamics, and heat transfer.

We’ll solve textbook examples to understand the fundamental principles of finite-element analysis and computational fluid dynamics. Then we’ll apply these principles to simulate real-world examples in the tool including a bolted rocket assembly and a wind turbine rotor. We’ll discuss current industry practices with a SpaceX engineer.

By working through examples in a leading simulation tool that professionals use, you’ll learn to move beyond button pushing and start thinking like an expert. This course teaches fundamental concepts and tool use in an integrated fashion using the power of online learning. All learners will have access to a free download of ANSYS Student. Join us to discover why simulations have changed how engineering is done and how you can be a part of this revolution.”

 

  1. Robotics: Kinematics and Mathematical Foundations (University of Pennsylvania)

“Welcome to the first course in the Robotics MicroMasters series. This is an advanced course designed for learners who have a bachelor’s degree in engineering or a similar field. Learners will succeed in this course if they have familiarity with basic operations on matrices and vectors, as well as exposure to derivatives and partial derivatives. The fundamental challenge this course address is how one can create robots that operate well in the real world.”

 

  1. Robotics: Mobility (University of Pennsylvania)

“How can robots use their motors and sensors to move around in an unstructured environment? You will understand how to design robot bodies and behaviors that recruit limbs and more general appendages to apply physical forces that confer reliable mobility in a complex and dynamic world.

We develop an approach to composing simple dynamical abstractions that partially automate the generation of complicated sensorimotor programs. Specific topics that will be covered include: mobility in animals and robots, kinematics and dynamics of legged machines, and design of dynamical behavior via energy landscapes.”

 

  1. Robotics: Computational Motion Planning (University of Pennsylvania)

“Robotic systems typically include three components: a mechanism which is capable of exerting forces and torques on the environment, a perception system for sensing the world and a decision and control system which modulates the robot’s behavior to achieve the desired ends.

In this course, we will consider the problem of how a robot decides what to do to achieve its goals. This problem is often referred to as Motion Planning and it has been formulated in various ways to model different situations.  You will learn some of the most common approaches to addressing this problem including graph-based methods, randomized planners, and artificial potential fields. Throughout the course, we will discuss the aspects of the problem that make planning challenging.”

 

  1. Robotics: Aerial Robotics (University of Pennsylvania)

“How can we create agile micro aerial vehicles that are able to operate autonomously in cluttered indoor and outdoor environments?  You will gain an introduction to the mechanics of flight and the design of quadrotor flying robots and will be able to develop dynamic models, derive controllers, and synthesize planners for operating in three-dimensional environments.  You will be exposed to the challenges of using noisy sensors for localization and maneuvering in complex, three-dimensional environments.

Finally, you will gain insights through seeing real-world examples of the possible applications and challenges for the rapidly-growing drone industry. Mathematical prerequisites: Students taking this course are expected to have some familiarity with linear algebra, single variable calculus, and differential equations. Programming prerequisites: Some experience programming with MATLAB or Octave is recommended (we will use MATLAB in this course.) MATLAB will require the use of a 64-bit computer.”

 

  1. Using Python for Research (Harvard University)

“This course bridges the gap between introductory and advanced courses in Python. While there are many excellent introductory Python courses available, most typically do not go deep enough for you to apply your Python skills to research projects.

In this course, after first reviewing the basics of Python 3, we learn about tools commonly used in research settings. This version of the course includes a new module on statistical learning. Using a combination of a guided introduction and more independent in-depth exploration, you will get to practice your new Python skills with various case studies chosen for their scientific breadth and their coverage of different Python features.”

 

  1. Quantitative Methods for Biology (Harvard University)

“Are you a biologist, health worker, or medical student who needs to learn how to program? Are you a programmer who wants a better understanding of the medical field? Are you looking for an introduction to MATLAB?

For beginners, Quantitative Methods for Biology takes a unique approach, giving you an inside glimpse of a course and its learners. You’ll study alongside students who are also learning to code.

For expert programmers, this course has a will help you learn the MATLAB you need without getting slowed down by introductory concepts that you already know. Whether you’re already comfortable with Python, JavaScript, R, or some other language, we’ll help you translate that knowledge to MATLAB. All learners will be able to access a copy of MATLAB that they can use during the run of the course, free of charge.

There will also be opportunities to put code directly into assignments so that you can test your skills and work on authentic projects. In addition, this course uses an adaptive approach to its assignments. The more skilled you are, the fewer problems you’ll need to complete in order to finish the course. If you’re having difficulty, we’ll make sure that you get the practice you need in order to succeed.”

 

  1. Big Data and Education (University of Pennsylvania)

“Online and software-based learning tools have been used increasingly in education. This movement has resulted in an explosion of data, which can now be used to improve educational effectiveness and support basic research on learning.

In this course, you will learn how and when to use key methods for educational data mining and learning analytics on this data. You will examine the methods being developed by researchers in educational data mining, learning analytics, learning-at-scale, student modeling, and artificial intelligence communities. You’ll also gain experience with standard data mining methods frequently applied to educational data. You will learn how to apply these methods and when to apply them, as well as their strengths and weaknesses for different applications.

The course will discuss how to use each method to answer education research questions, and to drive intervention and improvement in educational software and systems. Methods will be covered at a theoretical level, and in terms of learning how to apply them in Python or using software tools like RapidMiner. We will also discuss validity and generalizability; establishing how trustworthy and applicable the analysis results.”

 

 

  1. Data Science: Wrangling (Harvard University)

“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.”

 

  1. Robotics: Vision Intelligence and Machine Learning (University of Pennsylvania)

“How do robots “see”, respond to and learn from their interactions with the world around them? This is the fascinating field of visual intelligence and machine learning. Visual intelligence allows a robot to “sense” and “recognize” the surrounding environment. It also enables a robot to “learn” from the memory of past experiences by extracting patterns in visual signals.

You will understand how Machine Learning extracts statistically meaningful patterns in data that support classification, regression, and clustering. Then by studying Computer Vision and Machine Learning together you will be able to build recognition algorithms that can learn from data and adapt to new environments.

By the end of this course, part of the Robotics MicroMasters program, you will be able to program vision capabilities for a robot such as a robot localization as well as object recognition using machine learning.

Projects in this course will utilize MATLAB and OpenCV and will include real examples of video stabilization, recognition of 3D objects, coding a classifier for objects, building a perceptron, and designing a convolutional neural network (CNN) using one of the standard CNN frameworks.”

 

  1. Analytic Combinatorics (Princeton University)

“Analytic Combinatorics teaches a calculus that enables precise quantitative predictions of large combinatorial structures. This course introduces the symbolic method to derive functional relations among ordinary, exponential, and multivariate generating functions, and methods in complex analysis for deriving accurate asymptotic from the GF equations.”

 

  1. Analytics in Python (Harvard University)

“Data is the lifeblood of an organization. Competency in programming is an essential skill for successfully extracting information and knowledge from data. The goal of this course is to introduce learners to the basics of programming in Python and to give a working knowledge of how to use programs to deal with data.

In this course, we will first cover the basics of programming and then focus on using Python on the entire data management process from data acquisition to analysis of data big data and small data. This is an intensive hands-on course that will equip and reward learners with proficiency in data management skills.”

 

  1. The Computing Technology Inside Your Smartphone (Cornell University)

“We use our smartphones to communicate, to organize our lives, to find information, and to entertain ourselves. All of this is possible because a smartphone contains a powerful computer processor, which is the subject of this course. This computer science course starts by moving step-by-step through the fundamental layers of computing technology, from binary numbers to application software, and then covers advanced performance techniques and the details of actual smartphone processors.”

 

  1. CS50’s Introduction to Computer Science (Harvard University)

“This is CS50x, Harvard University’s introduction to the intellectual enterprises of computer science and the art of programming for majors and non-majors alike, with or without prior programming experience. An entry-level course taught by David J. Malan, CS50x teaches students how to think algorithmically and solve problems efficiently.

Topics include abstraction, algorithms, data structures, encapsulation, resource management, security, software engineering, and web development. Languages include C, Python, SQL, and JavaScript plus CSS and HTML. Problem sets inspired by real-world domains of biology, cryptography, finance, forensics, and gaming. The on-campus version of CS50x, CS50, is Harvard’s largest course.”

 

  1. Bitcoin and Cryptocurrency Technologies (Princeton University)

“To really understand what is special about Bitcoin, we need to understand how it works at a technical level. We’ll address the important questions about Bitcoin, such as: How does Bitcoin work? What makes Bitcoin different? How secure are your Bitcoins? How anonymous are Bitcoin users? What determines the price of Bitcoins? Can cryptocurrencies be regulated? What might the future hold?

After this course, you’ll know everything you need to be able to separate fact from fiction when reading claims about Bitcoin and other cryptocurrencies. You’ll have the conceptual foundations you need to engineer secure software that interacts with the Bitcoin network. And you’ll be able to integrate ideas from Bitcoin in your own projects.”

 

  1. Enabling Technologies for Data Science and Analytics: The Internet of Things (Columbia University)

“The Internet of Things is rapidly growing. It is predicted that more than 25 billion devices will be connected by 2020. In this data science course, you will learn about the major components of the Internet of Things and how data is acquired from sensors. You will also examine ways of analyzing event data, sentiment analysis, facial recognition software, and how data generated from devices can be used to make decisions.”

 

 

  1. CS50’s Computer Science for Business Professionals (Harvard University)

“This is CS50’s introduction to computer science for business professionals, designed for managers, product managers, founders, and decision-makers more generally. Whereas CS50 itself takes a bottom-up approach, emphasizing mastery of low-level concepts and implementation details thereof, this course takes a top-down approach, emphasizing mastery of high-level concepts and design decisions related thereto.

Through lectures on computational thinking, programming languages, internet technologies, web development, technology stacks, and cloud computing, this course empowers you to make technological decisions even if not a technologist yourself. You’ll emerge from this course with a first-hand appreciation of how it all works and all the more confident in the factors that should guide your decision-making.”

 

  1. C Programming: Using Linux Tools and Libraries (Dartmouth University)

“This seventh and final course in the C Programming with Linux Professional Certificate program will allow you to develop and use your C code within the Linux operating system. Using libraries in C is a fundamental concept when it comes to sharing code with others. In addition to compiling and linking, you will also learn how to pass arguments to an executable program.

Within moments you will be coding hands-on in a new browser tool developed for this course providing instant feedback on your code. No need to install anything! As you embark on your future career as a programmer, you will be able to continue your coding adventures with professional coding environments used by C programmers around the world.

At the end of this short course, you will have completed the C Programming with Linux Professional Certificate program, unlocking the door to a career in computer engineering. This course has received financial support from the Patrick & Lina Drahi Foundation.”

 

  1. Introduction to Probability (Harvard University)

“Probability and statistics help to bring logic to a world replete with randomness and uncertainty. This course will give you the tools needed to understand data, science, philosophy, engineering, economics, and finance. You will learn not only how to solve challenging technical problems, but also how you can apply those solutions in everyday life.

With examples ranging from medical testing to sports prediction, you will gain a strong foundation for the study of statistical inference, stochastic processes, randomized algorithms, and other subjects where probability is needed.”

 

  1. Advanced Natural Language Processing (MIT)

“This course is a graduate introduction to natural language processing – the study of human language from a computational perspective. It covers syntactic, semantic and discourse processing models, emphasizing machine learning or corpus-based methods and algorithms. It also covers applications of these methods and models in syntactic parsing, information extraction, statistical machine translation, dialogue systems, and summarization. The subject qualifies as an Artificial Intelligence and Applications concentration subject.”

 

  1. Matrix Methods in Data Analysis, Signal Processing, and Machine Learning (MIT)

 

“Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.” Here’s a link to all the video lectures.

 

  1. Mathematics of Machine Learning (MIT)

“Broadly speaking, Machine Learning refers to the automated identification of patterns in data. As such it has been a fertile ground for new statistical and algorithmic developments. The purpose of this course is to provide a mathematically rigorous introduction to these developments with emphasis on methods and their analysis.”

 

  1. Distributed Computer Systems Engineering (MIT)

“This course covers abstractions and implementation techniques for the design of distributed systems. Topics include: server design, network programming, naming, storage systems, security, and fault tolerance. The assigned readings for the course are from the current literature.”

 

  1. Database Systems (MIT)

“This course relies on primary readings from the database community to introduce graduate students to the foundations of database systems, focusing on basics such as the relational algebra and data model, schema normalization, query optimization, and transactions.

It is designed for students who have taken 6.033 (or equivalent); no prior database experience is assumed, though students who have taken an undergraduate course in databases are encouraged to attend.”

 

  1. Bioinformatics and Proteomics (MIT)

“This interdisciplinary course provides a hands-on approach to students in the topics of bioinformatics and proteomics. Lectures and labs cover sequence analysis, microarray expression analysis, Bayesian methods, control theory, scale-free networks, and biotechnology applications.

Designed for those with a computational and/or engineering background, it will include current real-world examples, actual implementations, and engineering design issues. Where applicable, engineering issues from signal processing, network theory, machine learning, robotics, and other domains will be expounded upon.”

 

  1. Machine Vision (MIT)

“Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading.

Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI, and computational vision. Applications to robotics and intelligent machine interaction are discussed.”

 

Conclusion

So, here is our top 50 list of the best FREE artificial intelligence, computer science, engineering, and programming courses from the Ivy League Universities.

All of the courses are totally FREE to attend to, and as we’ve mentioned there are certificates that you can skip if you fill so, since those are not prove to your knowledge, but your practical work is.

Check our older articles, you might find them helpful. Also, this one is a related article: How to Become Machine Learning Specialist in Under 20 Hours from This FREE LinkedIn Course

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

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