Stanford University is offering free IT courses for both experienced and fresher techies. These courses cover a wide range of topics, including machine learning, databases, computer science, and cybersecurity. The courses are available online and can be accessed by anyone, with no admission requirements. Stanford Continuing Studies offers computer science and technology classes for adults taught by industry experts. The courses are designed to be flexible, with evening classes, weekend workshops, and online courses. Upon completion of the courses, participants can obtain a certificate to showcase their skills.
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About the
Stanford University (officially Leland Stanford Junior University) is a private research university in Stanford, California. The campus occupies 8,180 acres (3,310 hectares), among the largest in the United States, and enrolls over 17,000 students. Its influence, wealth, and rankings have made it one of the most prestigious universities in the world.
Stanford University was founded in 1885 by Leland Stanford—a railroad magnate who served as the eighth governor of and then-incumbent senator from California—and his wife, Jane, in memory of their only child, Leland Stanford Jr., who had died of typhoid fever aged 15 the previous year.
The university admitted its first students on October 1, 1891, as a coeducational and non-denominational institution. Stanford University struggled financially after Leland died in 1893 and again after much of the campus was damaged by the 1906 San Francisco earthquake. Following World War II, Frederick Terman, the university’s provost, inspired and supported faculty and graduates’ entrepreneurialism to build a self-sufficient local industry, which would later be known as Silicon Valley.
Here Are Stanford University’s free IT courses
1. Supervised Machine Learning: Regression and Classification
In the first course of the Machine Learning Specialization, you will:
• Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. • Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications.
This Specialization is taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012.
It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, the new Machine Learning Specialization is the best place to start.
What you’ll learn?
Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn
Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.
Skills you’ll gain
Linear Regression
Category: Regularization to Avoid OverfittingRegularization to Avoid Overfitting
Category: Logistic Regression for ClassificationLogistic Regression for Classification
Category: Gradient DescentGradient Descent
Category: Supervised LearningSupervised Learning
Course Link Click Here
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2. Introduction to Statistics
Stanford’s “Introduction to Statistics” teaches you statistical thinking concepts that are essential for learning from data and communicating insights. By the end of the course, you will be able to perform exploratory data analysis, understand key principles of sampling, and select appropriate tests of significance for multiple contexts. You will gain the foundational skills that prepare you to pursue more advanced topics in statistical thinking and machine learning.
Topics include Descriptive Statistics, Sampling and Randomized Controlled Experiments, Probability, Sampling Distributions and the Central Limit Theorem, Regression, Common Tests of Significance, Resampling, and Multiple Comparisons.
what you will learn?
Introduction and Descriptive Statistics for Exploring Data
Probability
Regression
Resampling
Course Link Click Here
3. Cryptography I
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.
Skills you’ll gain
Cryptography
Category: Cryptographic AttacksCryptographic Attacks
Category: Symmetric-Key AlgorithmSymmetric-Key Algorithm
Category: Public-Key CryptographyPublic-Key Cryptography
Course Link Click Here
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4. Introduction to Clinical Data
This course introduces you to a framework for successful and ethical medical data mining. We will explore the variety of clinical data collected during the delivery of healthcare. You will learn to construct analysis-ready datasets and apply computational procedures to answer clinical questions. We will also explore issues of fairness and bias that may arise when we leverage healthcare data to make decisions about patient care.
In support of improving patient care, Stanford Medicine is jointly accredited by the Accreditation Council for Continuing Medical Education (ACCME), the Accreditation Council for Pharmacy Education (ACPE), and the American Nurses Credentialing Center (ANCC), to provide continuing education for the healthcare team. Visit the FAQs below for important information regarding 1) the Date of the original release and expiration date; 2) Accreditation and Credit Designation statements; and 3) the Disclosure of financial relationships for every person in control of activity content.
What you’ll learn?
How to apply a framework for medical data mining
Ethical use of data in healthcare decisions
How to make use of data that may be inaccurate in systematic ways
What makes a good research question and how to construct a data mining workflow answer it
Course Link Click Here
How to Apply?
Step1. Click ON the Enroll For Free
Step2. Click on Audit and and enroll Succesfully
FAQ
Q: Is this true? Is Stanford offering free IT courses?
A: Yes! Stanford University offers a variety of free online courses through its online learning platform, Stanford Online. These courses cover a range of IT topics, catering to both experienced professionals and tech newbies.
Q: What types of free IT courses are offered?
A: The course catalog is diverse, ranging from programming fundamentals like Python and Java to advanced topics like machine learning, artificial intelligence, and cybersecurity. You can also find courses on software development, data science, and network engineering.
