Machine Learning Specialization offered by Stanford University
- Private University
8180 acre campus- Estd. 1885
Machine Learning Specialization at Stanford University Overview
Machine Learning Specialization
at Stanford University
Prepare individuals for careers in data science, machine learning engineering, or related fields
Duration | 1 month |
Total fee | ₹49 |
Mode of learning | Online |
Official Website | Go to Website  |
Course Level | UG Certificate |
Machine Learning Specialization at Stanford University Highlights
Machine Learning Specialization
at Stanford University
- Earn a certificate from Stanford University
- Learn from industry experts
Machine Learning Specialization at Stanford University Course details
Machine Learning Specialization
at Stanford University
Skills you will learn
Who should do this course?
- For individuals who want to enhance their knowledge & skills in the field
What are the course deliverables?
- This includes understanding algorithms like linear regression, logistic regression, decision trees, support vector machines, and ensemble methods
- You'll learn the typical steps involved in building and deploying a machine learning model, including data preparation, model training, evaluation, and optimization
- Learning to code various algorithms from scratch or using libraries enhances your understanding and problem-solving skills
- You'll learn to approach problems analytically, identify suitable machine learning techniques, and interpret results effectively
More about this course
- The Machine Learning Specialization is a foundational online program created in collaboration between Stanford Online and DeepLearning.AI
- This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications
- This 3-course Specialization is an updated and expanded version of Andrew Ng’s pioneering Machine Learning course
- 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 for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more
Machine Learning Specialization at Stanford University Curriculum
Machine Learning Specialization
at Stanford University
Multiple Linear Regression
Logistic Regression
Neural Networks
Decision Trees
Clustering
Dimensionality Reduction
Recommender Systems
Evaluating and Tuning Models
Taking a Datacentric Performance Improvement
Machine Learning Specialization at Stanford University Entry Requirements
Machine Learning Specialization
at Stanford University
Eligibility criteria
- Education: Graduate
Conditional Offer
- Not mentioned
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Machine Learning Specialization at Stanford University Contact Information
Machine Learning Specialization
at Stanford University