Online Stanford University Course

Top 10 Best + Free Online Stanford University Courses

Stanford Online provides individuals with a single point of access to Stanford’s expanded education and global learning opportunities.

Via free online classes, graduate and technical qualifications, advanced degrees, and global and expanded education programs, we promote extended and meaningful interaction between Stanford faculty and learners around the world.

The Office of Vice Provost for Technology and Learning at Stanford University manages Stanford Online. Here is a list of the top 10 Stanford free courses:-

List of Free Stanford University Courses

1. Computer Science 101 (edx)

CS101 is a self-paced course that teaches students with no previous knowledge of computer science the fundamental concepts of the subject. While computers tend to be complex, they actually follow a few basic patterns. CS101 deconstructs and visualizes certain patterns, which is useful for today’s computer users.

Students in CS101 experiment with and play with short bits of “computer code” to demonstrate the strength and limitations of computers. Since all works inside the browser, there is no need to download or install any additional apps. CS101 also discusses the fundamentals of today’s computers, such as what a machine is, what hardware is, what software is, and what the internet is all about.

What you’ll learn – 

  • The nature of computers and code, what they can and cannot do
  • How computer hardware works: chips, CPU, memory, disk
  • Necessary jargon: bits, bytes, megabytes, gigabytes
  • How software works: what is a program, what is “running”
  • How digital images work
  • Computer code: loops and logic
  • Big ideas: abstraction, logic, bugs
  • How structured data works
  • How the internet works: IP address, routing, ethernet, wi-fi
  • Computer security: viruses, trojans, and passwords, oh my!
  • Analog vs. digital
  • Digital media, images, sounds, video, compression

Skills –

  • Algorithm
  • Coding
  • Hardware
  • Software

Prerequisites –

Aside from the ability to use a web browser, no prior computer knowledge is presumed.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 6 weeks, 4-6 hours per week

Instructor – Nick Parlante

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

2. Machine Learning (Coursera)

Machine learning is the science of having machines behave without being specifically programmed. Machine learning has enabled self-driving vehicles, functional speech recognition, successful web search, and a dramatically improved understanding of the human genome in the last decade. Machine learning is so popular these days that you actually use it hundreds of times a day without even realizing it. Many researchers claim it is the most efficient method of approaching human-level AI. This class will teach you about the most powerful machine learning methods and give you hands-on experience implementing and using them. More importantly, you’ll learn not just the theoretical foundations of learning, but also the practical know-how needed to rapidly and efficiently apply these methods to new problems. Finally, you’ll discover some of Silicon Valley’s most innovative machine learning and AI practices.

What you’ll learn – 

  • Machine learning, Datamining, and Statistical pattern recognition
  • Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). 
  •  Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). 
  • Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI). 

Skills –

  • Logistic Regression
  • Artificial Neural Network
  • Machine Learning (ML) Algorithms
  • Machine Learning

Prerequisites –

Aside from the ability to use a web browser, no prior computer knowledge is presumed.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 60 hours

Instructor – Andrew Ng

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

3. Intro to Statistics (Udacity)

The aim of statistics is to derive meaning from data. We will cover techniques for visualizing data relationships as well as systematic techniques for understanding the relationships using mathematics in this class.

What you’ll learn – 

  • Seeing relationships in data.
  • Making predictions based on data.
  • Simpson’s paradox.
  • Manipulating Normal Distribution
  • Binomial Distribution.

Skills-

  • Probability
  • Calculus
  • Visualization

Prerequisites –

No prior knowledge of statistics is needed for this course. It would be beneficial to have a basic understanding of algebra, such as how to compute the mean, median, and mode of a set of numbers.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 8 weeks

Instructor – Sebastian Thrun

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

4. Databases: Relational Databases and SQL (Edx)

This is one of five self-paced Databases courses that began in the fall of 2011 as one of Stanford’s three inaugural major open online courses. Many of the original “Databases” courses are now available on edx.org.

SQL, the long-accepted standard query language for relational database systems, is covered in this course, as well as an introduction to relational databases.

Databases: Advanced Topics in SQL and Databases: OLAP and Recursion, which can be taken in any order, can be taken after this course. Advanced Topics is a broad and practical course that covers indexes, transactions, restrictions, causes, views, and authorization, while OLAP and Recursion are recommended for learners with a special interest in these topics.

What you’ll learn – 

  • Introduction to the relational model and concepts in relational databases and relational database management systems
  • Creating indexes for increased query performance
  • Using transactions for concurrency control and failure recovery
  • Database constraints: key, referential integrity, and “check” constraints
  • The data-modeling component of the Unified Modeling Language (UML), how UML diagrams are translated to relations

Skills –

  • SQL
  • Database Management
  • Calculus

Prerequisites –

There are no particular prerequisites for this course; however, all of the Databases series courses require some computer science history.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 2 weeks, 8-9 hours per week

Instructor – Jennifer Widom

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

5. Introduction to Mathematical Thinking (Coursera)

Learn to think like a mathematician, a complex cognitive mechanism that has developed over thousands of years.

Mathematical reasoning is not the same as doing math, at least not in the way it is taught in our educational system. In school math, learning procedures to solve highly stereotyped problems is a common subject. Professional mathematicians have a particular way of thinking to solve real-world problems, whether they come from the real world, science, or mathematics itself. Success in school math necessitates the ability to think outside the box. The ability to think outside the box, on the other hand, is a core characteristic of mathematical thought, and it is a valuable skill in today’s world. This course aids in the growth of critical thinking skills.

What you’ll learn – 

  • Formalized parts of language for use in mathematics.
  • Analysis of language for use in mathematics.
  • Branch of mathematics known as Number Theory
  •  Real Analysis

Skills –

  • Number Theory
  • Real Analysis
  • Mathematical Logic
  • Language

Prerequisites –

There are no particular prerequisites for this course; however, all of the Databases series courses require some computer science history.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration– 39 hours

Instructor – Dr. Keith Devlin

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

6. Stanford Introduction to Food and Health (Coursera)

The course focuses on changing dietary habits and reducing the amount of time spent preparing and eating food. As part of the course, you’ll study the transition to processed foods and the associated lifestyle diseases. You’ll learn how to tell the difference between healthy and unhealthy grocery items and how to put together a nutritious meal. There’s still more. You will have access to videos that will help you prepare a healthy meal at home. You will be convinced of the value of consuming home-cooked meals, devoting time to meal planning, avoiding processed foods, and maintaining a healthy lifestyle by the end of the course.

What You Will Learn –

  • You will be able to investigate contemporary eating trends and consequent lifestyle diseases
  • You will learn about food, nutrition, and tools for identifying  healthy and unhealthy foods
  •  Tons of instructional cooking videos
  • Completely online, beginner-level course
  • Practical tips to assemble a healthy meal
  •  Flexible deadlines that allow self-paced learning

Skills –

  • Health Informatics
  • Nutrition
  • Food Safety
  • Food Science

Pre-requirements –

No need of prerequisites.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 7 hours

Instructor – Maya Adam

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

7. Child Nutrition and Cooking (Coursera)

Childhood eating habits stick with a child into adulthood and have a long-term effect on his or her health. In recent years, processed foods have made their way into even children’s diets. The course covers emerging child nutrition trends as well as the health risks associated with obesity in children. You’ll learn about the components of a nutritious meal, foods that cause allergies, and the environmental impact of food. At the end of the course, you will have learned how to prepare and pack a healthy lunch for a kid. This course would support parents and students interested in pursuing a career as a teacher or healthcare professional.

What You Will Learn –

  • Understand food, nutrition, food labels, and elements of taste.
  •  Tips for buying vegetables, preparing a healthy meal, and packing a healthy lunch for a child.
  •  Simple recipes.
  • Quizzes and exercises to check your learning.
  •  Completely online, beginner-level course.
  •  Flexible deadlines that allow self-paced learning.

Skills –

  • Nutrition
  • Organic Food
  • Food Safety
  • Food Science

Pre-requirements –

No need of prerequisites.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – approx 11 hours

Instructor – Maya Adam, MD

8. Thermodynamics and Phase Equilibria (Edx)

The first and second laws of thermodynamics, entropy, an equilibrium for isolated systems, materials properties, and phase equilibria are all covered in this course.

What you’ll learn – 

  • What thermodynamic functions govern heterogeneous equilibria and how to calculate these functions from measurable materials properties.
  • Unary phase equilibria and first-order phase transitions, metastability.
  • Thermodynamics of solutions and their application to binary phase equilibria and binary phase diagrams.
  • Thermodynamics of chemical reactions

Skills –

  • Calculus
  • Thermodynamics
  • Equilibria

Prerequisites –

No need of prerequisites.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 16 weeks

Instructor – Alberto Salleo

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

9. Principles of Economics (Edx)

Microeconomic analysis, which involves the customer and firm conduct, is the subject of the first part of the course. We examine product and service markets, as well as the policies that affect them. In the second half of the course, students will learn about macroeconomic principles such as national production, jobs, inflation, and interest rates. The study looks at models that forecast long-run growth and short-term volatility in national economies. Following that, the importance of government taxation, monetary policy, and fiscal policy is addressed.

What you’ll learn – 

  • Observing and Explaining the Economy
  • The Supply and Demand Model
  • Using the Supply and Demand Model
  • Deriving Demand
  • Deriving Supply
  • The Competitive Equilibrium Model
  • Market Equilibrium and Efficiency

Skills –

  • Calculus
  • Economy
  • Demand and Supply

Prerequisites –

No need of prerequisites.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 10 weeks

Instructor – John Taylor, Yiming He

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

10. Mining Massive Datasets (Edx)

The course is based on the book Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman, who also happen to be the course instructors.

MapReduce systems and algorithms, Locality-sensitive hashing, Algorithms for data streams, PageRank and Web-link analysis, Frequent itemset analysis, Clustering, Computational ads, Recommendation systems, Social-network graphs, Dimensionality reduction, and Machine-learning algorithms are only a few of the big topics addressed.

What you’ll learn – 

  • MapReduce systems and algorithms
  • Locality-sensitive hashing
  • Algorithms for data streams
  • Clustering
  • Computational advertising
  • Recommendation systems
  • Social-network graphs
  • PageRank and Web-link analysis
  • Frequent itemset analysis
  • Dimensionality reduction
  • Machine-learning algorithms

Skills –

  • Algorithm
  • Clustering
  • Machine learning

Prerequisites –

The course is designed for Computer Science graduate students and advanced undergraduates. You should have taken Data Structures, Algorithms, Database Systems, Linear algebra, Multivariable Calculus, and Statistics at the very least.

Who can take this course –

Anybody who wants to learn more and make better use of their time is welcome to enroll.

Duration – 7 weeks

Instructor – Jeff Ullman,Jure Leskovec,Anand Rajaraman

*Note: This course has begun on April 26, 2021, but enrollment is currently open.

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