Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you'll learn about some of Silicon Valley's best practices in innovation as it pertains to machine learning and AI.
von
Maschinelles Lernen
Stanford UniversityÜber diesen Kurs
Karriereergebnisse der Lernenden
11%
17%
Kompetenzen, die Sie erwerben
- Logistic Regression
- Artificial Neural Network
- Machine Learning (ML) Algorithms
- Machine Learning
Karriereergebnisse der Lernenden
11%
17%
von
Stanford University
The Leland Stanford Junior University, commonly referred to as Stanford University or Stanford, is an American private research university located in Stanford, California on an 8,180-acre (3,310 ha) campus near Palo Alto, California, United States.
Lehrplan - Was Sie in diesem Kurs lernen werden
Introduction
Welcome to Machine Learning! In this module, we introduce the core idea of teaching a computer to learn concepts using data—without being explicitly programmed. The Course Wiki is under construction. Please visit the resources tab for the most complete and up-to-date information.
Linear Regression with One Variable
Linear regression predicts a real-valued output based on an input value. We discuss the application of linear regression to housing price prediction, present the notion of a cost function, and introduce the gradient descent method for learning.
Linear Algebra Review
This optional module provides a refresher on linear algebra concepts. Basic understanding of linear algebra is necessary for the rest of the course, especially as we begin to cover models with multiple variables.
Linear Regression with Multiple Variables
Octave/Matlab Tutorial
This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. To complete the programming assignments, you will need to use Octave or MATLAB. This module introduces Octave/Matlab and shows you how to submit an assignment.
Logistic Regression
Logistic regression is a method for classifying data into discrete outcomes. For example, we might use logistic regression to classify an email as spam or not spam. In this module, we introduce the notion of classification, the cost function for logistic regression, and the application of logistic regression to multi-class classification.
Regularization
Machine learning models need to generalize well to new examples that the model has not seen in practice. In this module, we introduce regularization, which helps prevent models from overfitting the training data.
Neural Networks: Representation
Neural networks is a model inspired by how the brain works. It is widely used today in many applications: when your phone interprets and understand your voice commands, it is likely that a neural network is helping to understand your speech; when you cash a check, the machines that automatically read the digits also use neural networks.
Bewertungen
- 5 stars92,37 %
- 4 stars6,91 %
- 3 stars0,47 %
- 2 stars0,09 %
- 1 star0,14 %
Top-Bewertungen von MASCHINELLES LERNEN
Broke my hype and fantasies about ML and created a sophisticated interest and a new perspective on the real challenges. Very good teaching and excellent online learning platform (shoutout to coursera)
This is course just awesome. You get everything you wanted from this course. It covers on all topics in detail, helps in getting confidence in learning all the techiques and ideas in machine learning.
good course; just 2 suggestions: improve the skew data part (week 6) and furnish the formula to evaluate the number of iteration in the window from image dimension, window dimension and step (week 11)
Perfect foundational overview of the topic with challenging exercises, at least for someone who left university over 20 years ago and has since then not done much with his skills in Linear Algebra ;-)
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