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Logistic RegressionArtificial Neural NetworkMachine Learning (ML) AlgorithmsMachine Learning

100 % online

Beginnen Sie sofort und lernen Sie in Ihrem eigenen Tempo.

Flexible Fristen

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Ca. 55 Stunden zum Abschließen

Englisch

Untertitel: Chinesisch (vereinfacht), Englisch, Hebräisch, Spanisch, Hindi, Japanisch...

Lehrplan - Was Sie in diesem Kurs lernen werden

Woche
1
2 Stunden zum Abschließen

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 Videos (Gesamt 42 min), 9 Lektüren, 1 Quiz
5 Videos
Welcome6m
What is Machine Learning?7m
Supervised Learning12m
Unsupervised Learning14m
9 Lektüren
Machine Learning Honor Code8m
What is Machine Learning?5m
How to Use Discussion Forums4m
Supervised Learning4m
Unsupervised Learning3m
Who are Mentors?3m
Get to Know Your Classmates8m
Frequently Asked Questions11m
Lecture Slides20m
1 praktische Übung
Introduction10m
2 Stunden zum Abschließen

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 Videos (Gesamt 70 min), 8 Lektüren, 1 Quiz
7 Videos
Cost Function8m
Cost Function - Intuition I11m
Cost Function - Intuition II8m
Gradient Descent11m
Gradient Descent Intuition11m
Gradient Descent For Linear Regression10m
8 Lektüren
Model Representation3m
Cost Function3m
Cost Function - Intuition I4m
Cost Function - Intuition II3m
Gradient Descent3m
Gradient Descent Intuition3m
Gradient Descent For Linear Regression6m
Lecture Slides20m
1 praktische Übung
Linear Regression with One Variable10m
2 Stunden zum Abschließen

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 Videos (Gesamt 61 min), 7 Lektüren, 1 Quiz
6 Videos
Addition and Scalar Multiplication6m
Matrix Vector Multiplication13m
Matrix Matrix Multiplication11m
Matrix Multiplication Properties9m
Inverse and Transpose11m
7 Lektüren
Matrices and Vectors2m
Addition and Scalar Multiplication3m
Matrix Vector Multiplication2m
Matrix Matrix Multiplication2m
Matrix Multiplication Properties2m
Inverse and Transpose3m
Lecture Slides10m
1 praktische Übung
Linear Algebra10m
Woche
2
3 Stunden zum Abschließen

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 Videos (Gesamt 65 min), 16 Lektüren, 1 Quiz
8 Videos
Gradient Descent for Multiple Variables5m
Gradient Descent in Practice I - Feature Scaling8m
Gradient Descent in Practice II - Learning Rate8m
Features and Polynomial Regression7m
Normal Equation16m
Normal Equation Noninvertibility5m
Working on and Submitting Programming Assignments3m
16 Lektüren
Setting Up Your Programming Assignment Environment8m
Access MATLAB Online and Upload the Exercise Files3m
Installing Octave on Windows3m
Installing Octave on Mac OS X (10.10 Yosemite and 10.9 Mavericks and Later)10m
Installing Octave on Mac OS X (10.8 Mountain Lion and Earlier)3m
Installing Octave on GNU/Linux7m
More Octave/MATLAB resources10m
Multiple Features3m
Gradient Descent For Multiple Variables2m
Gradient Descent in Practice I - Feature Scaling3m
Gradient Descent in Practice II - Learning Rate4m
Features and Polynomial Regression3m
Normal Equation3m
Normal Equation Noninvertibility2m
Programming tips from Mentors10m
Lecture Slides20m
1 praktische Übung
Linear Regression with Multiple Variables10m
5 Stunden zum Abschließen

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 Videos (Gesamt 80 min), 1 Lektüre, 2 Quiz
6 Videos
Moving Data Around16m
Computing on Data13m
Plotting Data9m
Control Statements: for, while, if statement12m
Vectorization13m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Octave/Matlab Tutorial10m
Woche
3
2 Stunden zum Abschließen

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 Videos (Gesamt 71 min), 8 Lektüren, 1 Quiz
7 Videos
Hypothesis Representation7m
Decision Boundary14m
Cost Function10m
Simplified Cost Function and Gradient Descent10m
Advanced Optimization14m
Multiclass Classification: One-vs-all6m
8 Lektüren
Classification2m
Hypothesis Representation3m
Decision Boundary3m
Cost Function3m
Simplified Cost Function and Gradient Descent3m
Advanced Optimization3m
Multiclass Classification: One-vs-all3m
Lecture Slides10m
1 praktische Übung
Logistic Regression10m
4 Stunden zum Abschließen

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 Videos (Gesamt 39 min), 5 Lektüren, 2 Quiz
4 Videos
Cost Function10m
Regularized Linear Regression10m
Regularized Logistic Regression8m
5 Lektüren
The Problem of Overfitting3m
Cost Function3m
Regularized Linear Regression3m
Regularized Logistic Regression3m
Lecture Slides10m
1 praktische Übung
Regularization10m
Woche
4
5 Stunden zum Abschließen

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 Videos (Gesamt 63 min), 6 Lektüren, 2 Quiz
7 Videos
Neurons and the Brain7m
Model Representation I12m
Model Representation II11m
Examples and Intuitions I7m
Examples and Intuitions II10m
Multiclass Classification3m
6 Lektüren
Model Representation I6m
Model Representation II6m
Examples and Intuitions I2m
Examples and Intuitions II3m
Multiclass Classification3m
Lecture Slides10m
1 praktische Übung
Neural Networks: Representation10m
Woche
5
5 Stunden zum Abschließen

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 Videos (Gesamt 78 min), 8 Lektüren, 2 Quiz
8 Videos
Backpropagation Algorithm11m
Backpropagation Intuition12m
Implementation Note: Unrolling Parameters7m
Gradient Checking11m
Random Initialization6m
Putting It Together13m
Autonomous Driving6m
8 Lektüren
Cost Function4m
Backpropagation Algorithm10m
Backpropagation Intuition4m
Implementation Note: Unrolling Parameters3m
Gradient Checking3m
Random Initialization3m
Putting It Together4m
Lecture Slides10m
1 praktische Übung
Neural Networks: Learning10m
Woche
6
5 Stunden zum Abschließen

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 Videos (Gesamt 63 min), 7 Lektüren, 2 Quiz
7 Videos
Evaluating a Hypothesis7m
Model Selection and Train/Validation/Test Sets12m
Diagnosing Bias vs. Variance7m
Regularization and Bias/Variance11m
Learning Curves11m
Deciding What to Do Next Revisited6m
7 Lektüren
Evaluating a Hypothesis4m
Model Selection and Train/Validation/Test Sets3m
Diagnosing Bias vs. Variance3m
Regularization and Bias/Variance3m
Learning Curves3m
Deciding What to do Next Revisited3m
Lecture Slides10m
1 praktische Übung
Advice for Applying Machine Learning10m
1 Stunde zum Abschließen

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 Videos (Gesamt 60 min), 3 Lektüren, 1 Quiz
5 Videos
Error Analysis13m
Error Metrics for Skewed Classes11m
Trading Off Precision and Recall14m
Data For Machine Learning11m
3 Lektüren
Prioritizing What to Work On3m
Error Analysis3m
Lecture Slides10m
1 praktische Übung
Machine Learning System Design10m
Woche
7
5 Stunden zum Abschließen

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 Videos (Gesamt 98 min), 1 Lektüre, 2 Quiz
6 Videos
Large Margin Intuition10m
Mathematics Behind Large Margin Classification19m
Kernels I15m
Kernels II15m
Using An SVM21m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Support Vector Machines10m
Woche
8
1 Stunde zum Abschließen

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 Videos (Gesamt 39 min), 1 Lektüre, 1 Quiz
5 Videos
K-Means Algorithm12m
Optimization Objective7m
Random Initialization7m
Choosing the Number of Clusters8m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Unsupervised Learning10m
4 Stunden zum Abschließen

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 Videos (Gesamt 67 min), 1 Lektüre, 2 Quiz
7 Videos
Motivation II: Visualization5m
Principal Component Analysis Problem Formulation9m
Principal Component Analysis Algorithm15m
Reconstruction from Compressed Representation3m
Choosing the Number of Principal Components10m
Advice for Applying PCA12m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Principal Component Analysis10m
Woche
9
2 Stunden zum Abschließen

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 Videos (Gesamt 91 min), 1 Lektüre, 1 Quiz
8 Videos
Gaussian Distribution10m
Algorithm12m
Developing and Evaluating an Anomaly Detection System13m
Anomaly Detection vs. Supervised Learning7m
Choosing What Features to Use12m
Multivariate Gaussian Distribution13m
Anomaly Detection using the Multivariate Gaussian Distribution14m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Anomaly Detection10m
4 Stunden zum Abschließen

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 Videos (Gesamt 58 min), 1 Lektüre, 2 Quiz
6 Videos
Content Based Recommendations14m
Collaborative Filtering10m
Collaborative Filtering Algorithm8m
Vectorization: Low Rank Matrix Factorization8m
Implementational Detail: Mean Normalization8m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Recommender Systems10m
Woche
10
1 Stunde zum Abschließen

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 Videos (Gesamt 64 min), 1 Lektüre, 1 Quiz
6 Videos
Stochastic Gradient Descent13m
Mini-Batch Gradient Descent6m
Stochastic Gradient Descent Convergence11m
Online Learning12m
Map Reduce and Data Parallelism14m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Large Scale Machine Learning10m
Woche
11
1 Stunde zum Abschließen

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 Videos (Gesamt 57 min), 1 Lektüre, 1 Quiz
5 Videos
Sliding Windows14m
Getting Lots of Data and Artificial Data16m
Ceiling Analysis: What Part of the Pipeline to Work on Next13m
Summary and Thank You4m
1 Lektüre
Lecture Slides10m
1 praktische Übung
Application: Photo OCR10m
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von SBSep 27th 2018

One of the best course at Coursera, the content are very well versed, assignments and quiz are quite challenging and good, Andrew is one of the best guide we could have in our side.\n\nThanks Coursera

von 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

Dozent

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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

Über 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....

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