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Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

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シラバス - 本コースの学習内容

1
2時間で修了

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....
5件のビデオ (合計42分), 9 readings, 1 quiz
5件のビデオ
Welcome6 分
What is Machine Learning?7 分
Supervised Learning12 分
Unsupervised Learning14 分
9件の学習用教材
Machine Learning Honor Code8 分
What is Machine Learning?5 分
How to Use Discussion Forums4 分
Supervised Learning4 分
Unsupervised Learning3 分
Who are Mentors?3 分
Get to Know Your Classmates8 分
Frequently Asked Questions11 分
Lecture Slides20 分
1の練習問題
Introduction10 分
2時間で修了

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....
7件のビデオ (合計70分), 8 readings, 1 quiz
7件のビデオ
Cost Function8 分
Cost Function - Intuition I11 分
Cost Function - Intuition II8 分
Gradient Descent11 分
Gradient Descent Intuition11 分
Gradient Descent For Linear Regression10 分
8件の学習用教材
Model Representation3 分
Cost Function3 分
Cost Function - Intuition I4 分
Cost Function - Intuition II3 分
Gradient Descent3 分
Gradient Descent Intuition3 分
Gradient Descent For Linear Regression6 分
Lecture Slides20 分
1の練習問題
Linear Regression with One Variable10 分
2時間で修了

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....
6件のビデオ (合計61分), 7 readings, 1 quiz
6件のビデオ
Addition and Scalar Multiplication6 分
Matrix Vector Multiplication13 分
Matrix Matrix Multiplication11 分
Matrix Multiplication Properties9 分
Inverse and Transpose11 分
7件の学習用教材
Matrices and Vectors2 分
Addition and Scalar Multiplication3 分
Matrix Vector Multiplication2 分
Matrix Matrix Multiplication2 分
Matrix Multiplication Properties2 分
Inverse and Transpose3 分
Lecture Slides10 分
1の練習問題
Linear Algebra10 分
2
3時間で修了

Linear Regression with Multiple Variables

What if your input has more than one value? In this module, we show how linear regression can be extended to accommodate multiple input features. We also discuss best practices for implementing linear regression....
8件のビデオ (合計65分), 16 readings, 1 quiz
8件のビデオ
Gradient Descent for Multiple Variables5 分
Gradient Descent in Practice I - Feature Scaling8 分
Gradient Descent in Practice II - Learning Rate8 分
Features and Polynomial Regression7 分
Normal Equation16 分
Normal Equation Noninvertibility5 分
Working on and Submitting Programming Assignments3 分
16件の学習用教材
Setting Up Your Programming Assignment Environment8 分
Access MATLAB Online and Upload the Exercise Files3 分
Installing Octave on Windows3 分
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10 分
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3 分
Installing Octave on GNU/Linux7 分
More Octave/MATLAB resources10 分
Multiple Features3 分
Gradient Descent For Multiple Variables2 分
Gradient Descent in Practice I - Feature Scaling3 分
Gradient Descent in Practice II - Learning Rate4 分
Features and Polynomial Regression3 分
Normal Equation3 分
Normal Equation Noninvertibility2 分
Programming tips from Mentors10 分
Lecture Slides20 分
1の練習問題
Linear Regression with Multiple Variables10 分
5時間で修了

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....
6件のビデオ (合計80分), 1 reading, 2 quizzes
6件のビデオ
Moving Data Around16 分
Computing on Data13 分
Plotting Data9 分
Control Statements: for, while, if statement12 分
Vectorization13 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Octave/Matlab Tutorial10 分
3
2時間で修了

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. ...
7件のビデオ (合計71分), 8 readings, 1 quiz
7件のビデオ
Hypothesis Representation7 分
Decision Boundary14 分
Cost Function10 分
Simplified Cost Function and Gradient Descent10 分
Advanced Optimization14 分
Multiclass Classification: One-vs-all6 分
8件の学習用教材
Classification2 分
Hypothesis Representation3 分
Decision Boundary3 分
Cost Function3 分
Simplified Cost Function and Gradient Descent3 分
Advanced Optimization3 分
Multiclass Classification: One-vs-all3 分
Lecture Slides10 分
1の練習問題
Logistic Regression10 分
4時間で修了

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. ...
4件のビデオ (合計39分), 5 readings, 2 quizzes
4件のビデオ
Cost Function10 分
Regularized Linear Regression10 分
Regularized Logistic Regression8 分
5件の学習用教材
The Problem of Overfitting3 分
Cost Function3 分
Regularized Linear Regression3 分
Regularized Logistic Regression3 分
Lecture Slides10 分
1の練習問題
Regularization10 分
4
5時間で修了

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. ...
7件のビデオ (合計63分), 6 readings, 2 quizzes
7件のビデオ
Neurons and the Brain7 分
Model Representation I12 分
Model Representation II11 分
Examples and Intuitions I7 分
Examples and Intuitions II10 分
Multiclass Classification3 分
6件の学習用教材
Model Representation I6 分
Model Representation II6 分
Examples and Intuitions I2 分
Examples and Intuitions II3 分
Multiclass Classification3 分
Lecture Slides10 分
1の練習問題
Neural Networks: Representation10 分
5
5時間で修了

Neural Networks: Learning

In this module, we introduce the backpropagation algorithm that is used to help learn parameters for a neural network. At the end of this module, you will be implementing your own neural network for digit recognition. ...
8件のビデオ (合計78分), 8 readings, 2 quizzes
8件のビデオ
Backpropagation Algorithm11 分
Backpropagation Intuition12 分
Implementation Note: Unrolling Parameters7 分
Gradient Checking11 分
Random Initialization6 分
Putting It Together13 分
Autonomous Driving6 分
8件の学習用教材
Cost Function4 分
Backpropagation Algorithm10 分
Backpropagation Intuition4 分
Implementation Note: Unrolling Parameters3 分
Gradient Checking3 分
Random Initialization3 分
Putting It Together4 分
Lecture Slides10 分
1の練習問題
Neural Networks: Learning10 分
6
5時間で修了

Advice for Applying Machine Learning

Applying machine learning in practice is not always straightforward. In this module, we share best practices for applying machine learning in practice, and discuss the best ways to evaluate performance of the learned models. ...
7件のビデオ (合計63分), 7 readings, 2 quizzes
7件のビデオ
Evaluating a Hypothesis7 分
Model Selection and Train/Validation/Test Sets12 分
Diagnosing Bias vs. Variance7 分
Regularization and Bias/Variance11 分
Learning Curves11 分
Deciding What to Do Next Revisited6 分
7件の学習用教材
Evaluating a Hypothesis4 分
Model Selection and Train/Validation/Test Sets3 分
Diagnosing Bias vs. Variance3 分
Regularization and Bias/Variance3 分
Learning Curves3 分
Deciding What to do Next Revisited3 分
Lecture Slides10 分
1の練習問題
Advice for Applying Machine Learning10 分
1時間で修了

Machine Learning System Design

To optimize a machine learning algorithm, you’ll need to first understand where the biggest improvements can be made. In this module, we discuss how to understand the performance of a machine learning system with multiple parts, and also how to deal with skewed data. ...
5件のビデオ (合計60分), 3 readings, 1 quiz
5件のビデオ
Error Analysis13 分
Error Metrics for Skewed Classes11 分
Trading Off Precision and Recall14 分
Data For Machine Learning11 分
3件の学習用教材
Prioritizing What to Work On3 分
Error Analysis3 分
Lecture Slides10 分
1の練習問題
Machine Learning System Design10 分
7
5時間で修了

Support Vector Machines

Support vector machines, or SVMs, is a machine learning algorithm for classification. We introduce the idea and intuitions behind SVMs and discuss how to use it in practice. ...
6件のビデオ (合計98分), 1 reading, 2 quizzes
6件のビデオ
Large Margin Intuition10 分
Mathematics Behind Large Margin Classification19 分
Kernels I15 分
Kernels II15 分
Using An SVM21 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Support Vector Machines10 分
8
1時間で修了

Unsupervised Learning

We use unsupervised learning to build models that help us understand our data better. We discuss the k-Means algorithm for clustering that enable us to learn groupings of unlabeled data points....
5件のビデオ (合計39分), 1 reading, 1 quiz
5件のビデオ
K-Means Algorithm12 分
Optimization Objective7 分
Random Initialization7 分
Choosing the Number of Clusters8 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Unsupervised Learning10 分
4時間で修了

Dimensionality Reduction

In this module, we introduce Principal Components Analysis, and show how it can be used for data compression to speed up learning algorithms as well as for visualizations of complex datasets. ...
7件のビデオ (合計67分), 1 reading, 2 quizzes
7件のビデオ
Motivation II: Visualization5 分
Principal Component Analysis Problem Formulation9 分
Principal Component Analysis Algorithm15 分
Reconstruction from Compressed Representation3 分
Choosing the Number of Principal Components10 分
Advice for Applying PCA12 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Principal Component Analysis10 分
9
2時間で修了

Anomaly Detection

Given a large number of data points, we may sometimes want to figure out which ones vary significantly from the average. For example, in manufacturing, we may want to detect defects or anomalies. We show how a dataset can be modeled using a Gaussian distribution, and how the model can be used for anomaly detection. ...
8件のビデオ (合計91分), 1 reading, 1 quiz
8件のビデオ
Gaussian Distribution10 分
Algorithm12 分
Developing and Evaluating an Anomaly Detection System13 分
Anomaly Detection vs. Supervised Learning7 分
Choosing What Features to Use12 分
Multivariate Gaussian Distribution13 分
Anomaly Detection using the Multivariate Gaussian Distribution14 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Anomaly Detection10 分
4時間で修了

Recommender Systems

When you buy a product online, most websites automatically recommend other products that you may like. Recommender systems look at patterns of activities between different users and different products to produce these recommendations. In this module, we introduce recommender algorithms such as the collaborative filtering algorithm and low-rank matrix factorization....
6件のビデオ (合計58分), 1 reading, 2 quizzes
6件のビデオ
Content Based Recommendations14 分
Collaborative Filtering10 分
Collaborative Filtering Algorithm8 分
Vectorization: Low Rank Matrix Factorization8 分
Implementational Detail: Mean Normalization8 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Recommender Systems10 分
10
1時間で修了

Large Scale Machine Learning

Machine learning works best when there is an abundance of data to leverage for training. In this module, we discuss how to apply the machine learning algorithms with large datasets....
6件のビデオ (合計64分), 1 reading, 1 quiz
6件のビデオ
Stochastic Gradient Descent13 分
Mini-Batch Gradient Descent6 分
Stochastic Gradient Descent Convergence11 分
Online Learning12 分
Map Reduce and Data Parallelism14 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Large Scale Machine Learning10 分
11
1時間で修了

Application Example: Photo OCR

Identifying and recognizing objects, words, and digits in an image is a challenging task. We discuss how a pipeline can be built to tackle this problem and how to analyze and improve the performance of such a system. ...
5件のビデオ (合計57分), 1 reading, 1 quiz
5件のビデオ
Sliding Windows14 分
Getting Lots of Data and Artificial Data16 分
Ceiling Analysis: What Part of the Pipeline to Work on Next13 分
Summary and Thank You4 分
1件の学習用教材
Lecture Slides10 分
1の練習問題
Application: Photo OCR10 分
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by SSMay 17th 2019

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.

by RDMar 31st 2018

Perhaps the greatest instructor and the greatest course, I enjoyed it so much I had continued to do it in between my exams and looking forward fto start or deeplearning,ai specialization in a few days

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Andrew Ng

CEO/Founder Landing AI; Co-founder, Coursera; Adjunct Professor, Stanford University; formerly Chief Scientist,Baidu and founding lead of Google Brain

スタンフォード大学(Stanford University)について

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....

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