Are you ready to dive into the exciting world of machine learning? Stanford University, a global leader in technology and innovation, is offering a free machine learning course. This exceptional opportunity allows you to learn from world-renowned experts and gain practical skills in building intelligent systems.
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About the Stanford University
Stanford University (officially Leland Stanford Junior University) is a private research university in Stanford, California, United States. It was founded in 1885 by railroad magnate Leland Stanford, the eighth governor of and then-incumbent senator from California, and his wife, Jane, in memory of their only child, Leland Jr.
The university admitted its first students in 1891, opening as a coeducational and non-denominational institution. It 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, university provost Frederick Terman inspired an entrepreneurial culture to build a self-sufficient local industry (later Silicon Valley).In 1951, the Stanford Research Park was established in Palo Alto and is the world’s first university research park. By 2021, the university had 2,288 tenure-line faculty, senior fellows, center fellows, and medical faculty on staff.
The university is organized around seven schools of study on an 8,180-acre (3,310-hectare) campus, one of the largest in the nation. It houses the Hoover Institution, a public policy think tank, and is classified among “R1: Doctoral Universities – Very high research activity”. Students compete in 36 varsity sports, and the university is one of eight private institutions in the Atlantic Coast Conference (ACC). Stanford has won 131 NCAA team championships and was awarded the NACDA Directors’ Cup for 25 consecutive years, beginning in 1994. Students and alumni have won 302 Olympic medals (including 153 gold).
About the Free Machine Learning Course
In the first course of the Machine Learning Specialization, you will:
Build machine learning models in Python using popular machine learning libraries NumPy and sci-kit-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.
Eligibility Criteria
Stanford University Free Machine Learning Course For Everyone.
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What you’ll learn
Build machine learning models in Python using popular machine learning libraries NumPy & sci-kit-learn
Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression
Skills you’ll gain
Linear Regression
Regularization to Avoid Overfitting
Logistic Regression for Classification
Gradient Descent
Supervised Learning
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How To Apply?
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