Post it in the course discussion forum in the week that you are in (if you haven't already) and seek help there - sometimes others have run into the same issue and have found solutions, or can help review your code. 本栏目（Machine Learning）包括单参数的回归、多参数回归、Octave Tutorial（Ps：Octave一个开源的可以取代Matlab软件，彼此可以兼容）、Logistic Regression、Regularization、神经网络、机器学习系统设计、SVM（support Vector支持向量机）、聚类、降维、异常检测、大规模机器学习等章节。. We've all heard the buzz around machine learning and the way it pervades. A short video on the 3 key steps to effective listening and how effective listening is related to self awareness. courseraのmachine learning講座をやりはじめたのですが、week2のプログラミング課題の任意課題（ex1_multi）を実行すると下記のようにwarningが発生してしまいます。. As Håkon Hapnes Strand mentioned, Matlab/Octave is not commonly used for machine learning as python. Programming Exercise 1: Linear Regression Machine Learning Introduction In this exercise, you will implement linear regression and get to see it work on data. 4_i686-pc-ming32. Programming Exercise 2: Logistic Regression Machine Learning October 20, 2011 Introduction In this exercise, you will implement logistic. Machine learning is the science of getting computers to act without being explicitly programmed. Don't know where to start? The answer is one button away. Where reshape() is used to form the Theta matrices, replace ‘num_labels’ with ‘1’. You'll receive the same credential as students who attend class on campus. Fan of Portland Trail Blazers, Former SNH48 Member Kiku and Blackpink Rosé. m in machine-learning-coursera-assignment-codes | source code search engine Toggle navigation. matlab, R, python, julia, etc). 相关搜索： machine learning. Here is the note from the mentioned pdf. Qiita is a technical knowledge sharing and collaboration platform for programmers. I have recently completed the Machine Learning course from Coursera by Andrew NG. [I'm assuming you, or anyone reading this answer would like to capitalise on their machine learning expertise to work on real world data problems. APDaga DumpBox :The Thirst for learning We share free technical tutorial on the hot technical topics like Internet of Things (IoT), Machine Learning (ML),. They are also a foundational tool in formulating many machine learning problems. I have started doing Andrew Ng's popular machine learning course on Coursera. coursera Machine Learning Week2 学习笔记. I am taking Andrew Ng's Coursera class on machine learning. 吴恩达（Andrew Ng）在 Coursera 上开设的机器学习入门课《Machine Learning》，授课地址是： Coursera Andrew Ng Machine Learning. This helps them to practice for. See also: Pattern recognition Machine learning is a scientiﬁc discipline that explores. Coursera stat week 5 project. Advice for applying Machine Learning -- Andrew Ng; Andrew Ng's Coursera Machine Leaning(ML) Notes Week 2; Andrew Ng's Coursera Machine Leaning Coding Hw 1; 机器学习——Andrew Ng machine-learning-ex1 python实现; PyTorch学习——Andrew Ng machine-learning-ex1 Linear Regression实现 《machine learning》Andrew Ng chapter 13 support. Sign in Sign up. Machine learning from Coursera: A very good course in general, especially the assignment, really provide you a platform to test out whatever you have learnt from the video. Machine learning is the science of getting computers to act without being explicitly programmed. My personal folder is called "Coursera-ML". Andrew Ng’s Machine Learning Class on Coursera. Lecture notes and assignments for coursera machine learning class. implement linear regression and get to see it work on data. Welcome to communicate with me among above topics. m % gradientDescent. I very much liked that each video was on just one topic and that they were short and to the point so I could do one between tasks at work or whenever I. 0 Instructions: 1) Extract the contents of this zip file to the "machine-learning-ex?/ex?/" folder for each programming exercise. Now that the ex1 homework period is over, can we have a fully vectorized multiple variables gradient descent function? I don't have much confidence in the one i submitted because i bet it's hideously unoptimized, and would like to see one that'd work in the real world with lots of features and data. Supervised learning 监督学习这里存在两种问题 regression 和 classification。前者是预测相关的问题，后者是分类相关的问题。 首先介绍的是监督学习，notes1中用房屋价格预测来介绍Linear Regression线性回归。这里因为没有数据集，所以我选择用ex1中的ex1data1. 0 comments. So I implement every exercise of the Coursera ML class using numpy, scipy and tensorflow. You'll receive the same credential as students who attend class on campus. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. Don't know where to start? The answer is one button away. ↓Coursera MLの前回 www. Loss functions are common in machine learning, information theory, statistics, and mathematical optimization, and help guide decision making under uncertainty. This course is the first in a sequence of three. By continuing to use Pastebin, you agree to our use of cookies as described in the Cookies Policy. 正直難しくて辛いですね。 qiita. 発展課題：Gradient Descentのlearning rateの考察. Linear Regression Andrew Ng Coursera Machine Learning Ex1. This app works best with JavaScript enabled. 2: Principal Component Analysis. Coursera Machine Learningの課題ex1を，特にライブラリなどを使わずにバカ正直にやってみた版．バカ正直にやり過ぎてほぼOctaveのときと変わらない．Coursera Honor Codeに触れそう．．．大丈夫かな．．．. All gists Back to GitHub. 03, num\_ iters = 400$. Coursera《Introduction to TensorFlow》第三周测验 《Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning》第三周(Enhancing Vision with Convolutional Neural Networks)的测验答案. Classification. I had some very basic previous knowledge of bash but this course cleared any doubts I had and also thought me in a way that was easy to comprehend. It is a note about the process that I'm trying to learn Machine Learning on coursera. This is a review for Andrew Ng’s Coursera Machine Learning course which gives a tour of machine learning. - yhyap/machine-learning-coursera. matlab, R, python, julia, etc). I am taking Andrew Ng's Coursera class on machine learning. coursera Machine Learning Week3-1 学习笔记. I thought I would share one other plot I put together in evaluating the gradient descent algorithm used for logistic regression from my previous post. This course is the first in a sequence of three. The data sets are from the Coursera machine learning course offered by Andrew Ng. Data, software,and communication can be used for bad: to entrench unfair power structures, to undermine human rights, and to protect vested interests. Note that for most machine learning problems, is very high dimensional, so we don't be able to plot. Machine Learning Exercises. Build career skills in data science, computer science, business, and more. 0 Instructions: 1) Extract the contents of this zip file to the "machine-learning-ex?/ex?/" folder for each programming exercise. Here is the note from the mentioned pdf. OK, I Understand. Implementation Note: If your learning rate is too large, J(theta) can di- verge and blow up', resulting in values which are too large for computer calculations. About Us : We are a DHT resource search engine based on the Torrents protocol, all the resources come from the DHT web crawler for 24 hours. Machine Learning | Coursera. If you’re interested in taking a free online course, consider Coursera. This course is different from machine learning courses by say, Andrew Ng in that this course won't focus on coding the algorithm and rather would emphasize on diving right into the implementation of those algorithms using libraries that the R programming language already equips us with. Machine Learning Week 3 Quiz 2 (Regularization) Stanford Coursera. 사용하는 노트북의 OS는 windows 10이고 Octave도 자체도 계속. Machine Learning by Stanford University. [I'm assuming you, or anyone reading this answer would like to capitalise on their machine learning expertise to work on real world data problems. Andrew Ng’s Coursera Machine Leaning Coding Hw 1. 其实今年做毕设的时候我刷过其中一部分课程，当时在做deep learning，其中涉及到不少概念都与machine learning相关，于是就走马观花跳着看了一部分视频，但总感觉只是懂个皮毛，所以这次决定从头到尾完整地刷一遍，把一些概念再熟悉一遍，把习题和代码作业都解决掉。. Start learning Python today! DataCamp's Intro to Python course teaches you how to use Python programming for data science with interactive video tutorials. I have tried to provide multiple solutions for same problem like Using for loop & Vectorized Implementation (Optimiz. I'm super excited to be taking this course after reading about all the cool press it's been getting in the nerd news. You still need to randomly initialize the Theta values, just as with any NN. We’re launching the Advanced Machine Learning with TensorFlow on Google Cloud Platform specialization on Coursera to address the growing demand for practical, in-depth machine learning courses that emphasize real-world datasets and intuitive understanding. This is a review for Andrew Ng's Coursera Machine Learning course which gives a tour of machine learning. NFI uses motivational Learning tools & software & Self learning Video tutorial for flexible & easy learning. coursera Machine Learning Week2 学习笔记. 作业文件 machine-learning-ex3 1. coursera Machine Learning Week3-2 学习笔记. Posts about Machine Learning written by Anirudh. Data, software,and communication can be used for bad: to entrench unfair power structures, to undermine human rights, and to protect vested interests. Programming Exercise 1: Linear Regression Machine Learning Introduction In this exercise, you will implement linear regression and get to see it work on data. I have tried to provide multiple solutions for same problem like Using for loop & Vectorized Implementation (Optimiz. 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. 微博: 百里云_bly. [Machine Learning] Coursera (Andrew Ng) 筆記 - Linear Regression ex1. I'm going to skip some of the initial problems which the course uses to get students used to the Octave environment and matrix operations. I have started doing Andrew Ng's popular machine learning course on Coursera. The course is offered with Matlab/Octave. Machine Learning Week 1 Quiz 2 (Linear Regression with One Variable) Stanford Coursera_错题汇总 【Machine Learning, Coursera】机器学习Week3 Logistic Regression 【Machine Learning】4 多变量线性回归(Linear Regression with Multiple Variables) PyTorch学习——Andrew Ng machine-learning-ex1 Linear Regression实现. Coursera 의 Machine learning을 수강하면서 2주차 숙제인 Programming Assignment를 Octave나 Matlab으로 제출할려고 하는데 계속 오류가 발생한다. For the journal, see Machine Learning (journal). 打开练习文件夹（即ex1、ex2这些文件夹，我目前截图使用的是ex6）下\lib\目录中的submitWithConfiguration. Thank you for your interest in this question. We're working on linear regression and right now I'm dealing with coding the cost function. machine-learning-ex1(此为作业文件) 将这两个文件解压拖入matla Andrew Ng机器学习编程作业:Logistic Regression. hapjin Technology is a powerful force in our society. Contribute to tjaskula/Coursera development by creating an account on GitHub. implement linear regression and get to see it work on data. Linear Regression Andrew Ng Coursera Machine Learning Ex1. This is the second of a series of posts where I attempt to implement the exercises in Stanford's machine learning course in Python. Unfortunately, the machine learning class is taught Octave and I'm hoping to implement the algorithms in R. gcc-3497次阅读 coursera Machine Learning Week 1学习笔记. You may use either MATLAB or Octave (>= 3. Sign in Sign up. After implementing gradient descent in the first exercise (goal is to predict the price of a 1650 sq-ft, 3 br house), the J_history shows me a list of the same value (2. 编程作业有两个文件 1. I have started doing Andrew Ng's popular machine learning course on Coursera. 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. On January 12, 2016, Stanford University professors Trevor Hastie and Rob Tibshirani will offer the 3rd iteration of Statistical Learning, a MOOC which first began in January 2014, and has become quite a popular course among data scientists. For this reason, and for fun, I have rewritten the assignments and their instructions in python (Jupyter notebook). Hi, I think you are doing this assignment in Octave and that's why you are facing this issue. All the data is generated automatically by the program. They are also a foundational tool in formulating many machine learning problems. Cost computation: Use the linear cost function for J (from ex1 and ex5) for the unregularized portion. Hi, I think you are doing this assignment in Octave and that's why you are facing this issue. For the journal, see Machine Learning (journal). I'm super excited to be taking this course after reading about all the cool press it's been getting in the nerd news. m in machine-learning-coursera-assignment-codes | source code search engine Toggle navigation. Coursera Machine Learningの課題ex1を，特にライブラリなどを使わずにバカ正直にやってみた版．バカ正直にやり過ぎてほぼOctaveのときと変わらない．Coursera Honor Codeに触れそう．．．大丈夫かな．．．. I'm going to skip some of the initial problems which the course uses to get students used to the Octave environment and matrix operations. Introduction机器学习综述 (Week 1) Machine Learning - II. But since in this example we have only one feature, being able to plot this gives a nice sanity-check on our result. I claim that there is a rare resource which is SIMPLE and COMPLETE in machine learning. m % plotData. For this reason, and for fun, I have rewritten the assignments and their instructions in python (Jupyter notebook). machine-learning-ex1这是Coursera上Week2的ml-ex1的编程作业代码。经过测验，全部通过。具体文件可以进入我的github包括以下八个文件：%warmUpExercis 博文 来自： loserChen的博客. coursera Machine Learning ex1. function [X_norm, mu, sigma] = featureNormalize (X) %FEATURENORMALIZE Normalizes the features in X % FEATURENORMALIZE(X) returns a normalized version of X where % the mean value o. Machine Learning Exercises. Coursera ML Assignment 1 Part II. There are two types of learning. %% Machine Learning Online Class % Exercise 1: Linear regression with multiple variables % % Instructions % -----% % This file contains code that helps you get started on the. I’m super excited to be taking this course after reading about all the cool press it’s been getting in the nerd news. Machine Learning | Coursera. See the complete profile on LinkedIn and discover Imtiaz's connections and jobs at similar companies. Email:pxu4 [at. View Notes - ex2 from CS 229 at Stanford University. I thought, now that I am starting to get away from Matlab and use Python more, I should re-do the exercises in Python. I am Ritchie Ng, a machine learning engineer specializing in deep learning and computer vision. Coursera Machine Learningの課題ex1を，特にライブラリなどを使わずにバカ正直にやってみた版．バカ正直にやり過ぎてほぼOctaveのときと変わらない．Coursera Honor Codeに触れそう．．．大丈夫かな．．．. Coursera ML MOOC. I'm going to skip some of the initial problems which the course uses to get students used to the Octave environment and matrix operations. m in machine-learning-coursera-assignment-codes | source code search engine Toggle navigation. m的18行应该为if(isoct)，否则程序都无法跑. computeCostMulti. GitHub: billy-inn. 4_i686-pc-ming32. 作业文件 machine-learning-ex3 1. NFI uses motivational Learning tools & software & Self learning Video tutorial for flexible & easy learning. 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. Introduction机器学习综述 (Week 1) Machine Learning - II. 打开练习文件夹（即ex1、ex2这些文件夹，我目前截图使用的是ex6）下\lib\目录中的submitWithConfiguration. To learn from data, we use probability theory, which has been the mainstay of statistics and engineering for centuries. hapjin Technology is a powerful force in our society. Machine learning is the science of getting computers to act without being explicitly programmed. I have previously done the Coursera Machine Learning exercises in Matlab. ++Gradient descent, also known as steepest descent, is an iterative optimization algorithm for finding a local minimum of differentiable functions. Learn some stunts which cannot be done by Programming 101. Coursera / Machine Learning / Week 2. You can record and post programming tips, know-how and notes here. 随笔分类 - b:Machine Learning 如何用Python计算Softmax？ 摘要：Softmax函数，或称归一化指数函数，它能将一个含任意实数的K维向量z“压缩”到另一个K维实向量$\sigma{(z)}中，使得每一个元素的范围都在(0,1)之间，并且所有元素的和为1。. gradientDescent. On January 12, 2016, Stanford University professors Trevor Hastie and Rob Tibshirani will offer the 3rd iteration of Statistical Learning, a MOOC which first began in January 2014, and has become quite a popular course among data scientists. Machine Learning by Stanford University. Edureka’s Tableau 10 Certification training is aligned with the Tableau Qualified Associate Level Examination. Coursera has added another Machine Learning Specialization. I heard AI a year ago, but never really look into it as an elderly who is hardly to accept new things. machine-learning-ex1 这是Coursera上 Week2 的ml-ex1的编程作业代码。经过测验，全部通过。 具体文件可以进入我的github 包括以下八个文件：. View Notes - ex2 from CS 229 at Stanford University. I’m super excited to be taking this course after reading about all the cool press it’s been getting in the nerd news. 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 Online Class Coursera COMPUTER S 101 - Fall 2016 ex1_multi. Machine Learning specialists, and those interested in learning more about the field. machine-learning-ex1(此为作业文件) 将这两个文件解压拖入. 사용하는 노트북의 OS는 windows 10이고 Octave도 자체도 계속. Unfortunately, the machine learning class is taught Octave and I’m hoping to implement the algorithms in R. GitHub Gist: instantly share code, notes, and snippets. They have provided this set of codes for submission to run which i just used to run beforehand but on this new version i am unable to. As tours go… the course doesn’t go into depth for each topic, but the thing I like is where Professor Ng gives the intuition for the concepts. Coursera Machine Learning in Julia; Description; Scripts for Coursera Stanford Machine Learning assignments in Julia. You may use either MATLAB or Octave (>= 3. machine-learning-ex1这是Coursera上Week2的ml-ex1的编程作业代码。经过测验，全部通过。具体文件可以进入我的github包括以下八个文件：%warmUpExercis 博文 来自： loserChen的博客. A list of datasets for machine learning. Coursera 日本語字幕付き. coursera上斯坦福的machine learning课程作业的下载网址突然不能访问，显示DNS错误，怎么办. Linear Regression Andrew Ng Coursera Machine Learning Ex1. yu kai's blog. I was going through Andrew's ML course on Coursera (Week 2). Where reshape() is used to form the Theta matrices, replace 'num_labels' with '1'. It is a note about the process that I’m trying to learn Machine Learning on coursera. This is a review for Andrew Ng’s Coursera Machine Learning course which gives a tour of machine learning. We start the course by considering a retrieval task of fetching a document similar to one someone is currently reading. Thank you for your interest in this question. Many folks registered in the class expressed a similar interest, so I decided to share my attempts at the programming exercises here. 环境：windows7一、使用matlab1、一开始使用matlab，但在提交时发现ex1/lib/makeValidFieldName. Machine Learning Coursera second week assignment solution. For the regularized portion, use the same method as ex4. txt) or read online for free. I am Ritchie Ng, a machine learning engineer specializing in deep learning and computer vision. Skip to content. machine-learning-ex1这是Coursera上Week2的ml-ex1的编程作业代码。经过测验，全部通过。具体文件可以进入我的github包括以下八个文件：%warmUpExercis 博文 来自： loserChen的博客. It is a note about the process that I'm trying to learn Machine Learning on coursera. Note that for most machine learning problems, is very high dimensional, so we don't be able to plot. Coursera ML MOOC. courseraのMachine LearningをPythonで復習。 参考にさせていただいているサイト [email protected]さんex1（線形回帰）ex2. m % gradientDescen. 方針 オンライン 学習 プラットフォームCourseraで一番人気の講座、Stanford 大学のMachine Learning。講師 続きを表示 方針 オンライン 学習 プラットフォームCourseraで一番人気の講座、Stanford 大学のMachine. All gists Back to GitHub. 随笔分类 - b:Machine Learning 如何用Python计算Softmax？ 摘要：Softmax函数，或称归一化指数函数，它能将一个含任意实数的K维向量z“压缩”到另一个K维实向量\sigma{(z)}\$中，使得每一个元素的范围都在(0,1)之间，并且所有元素的和为1。. 天津大学新校区宿舍名称吴恩达machine learning主成分分析得分图含义; 天津大学教育学院研究生在哪上ng吴恩达python主成分分析检测; 天津大学建筑学研究生2017吴恩达的课程看不懂如何用spss做主成分分析. We also used caret-the famous R machine learning package- to verify our results. Posts about Machine Learning written by Anirudh. coursera Machine Learning Week3-2 学习笔记. Programming Exercise 1: Linear Regression Machine Learning Introduction In this exercise, you will implement linear regression and get to see it work on data. I was facing some problem in submitting. Introduction机器学习综述 (Week 1) Machine Learning - II. GitHub Gist: instantly share code, notes, and snippets. Many folks registered in the class expressed a similar interest, so I decided to share my attempts at the programming exercises here. The same source code archive can also be used to build the Windows and Mac versions, and is the starting point for ports to all other platforms. com ↑で数学を避けてきた～～の記事ですごくオススメされているので始めたのですが、 確かに日本語字幕は付いているし、わかりやすいとは思います。. Because it has attracted low-quality or spam answers that had to be removed, posting an answer now requires 10 reputation on this site (the association bonus does not count). The manufacturing industry is making a digital transformation, allowing companies to customize production through advances in machine learning, sustainable design, generative design, and collaboration, with integrated design and manufacturing processes. Programming Exercise 1: Linear Regression Machine Learning Introduction In this exercise, you will implement linear regression and get to see it work on data. I'm going to skip some of the initial problems which the course uses to get students used to the Octave environment and matrix operations. Machine-Learning. Coursera Machine Learningの課題ex1を，特にライブラリなどを使わずにバカ正直にやってみた版．バカ正直にやり過ぎてほぼOctaveのときと変わらない．Coursera Honor Codeに触れそう．．．大丈夫かな．．．. Post it in the course discussion forum in the week that you are in (if you haven't already) and seek help there – sometimes others have run into the same issue and have found solutions, or can help review your code. Machine Learning; Programming fascinated by how easy and fast it is to spin up a cluster on GCP and couldn't help myself from trying it outside the Coursera. Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. All gists Back to GitHub. machine-learning-ex8. Machine Learning Lover, Deep Learning Alchemist and NLPer. machine learning 课程视频. This ZIP file contains the instructions in a PDF and the starter code. "machine-learning-ex1" is the folder that I downloaded for the week two assignment. 吴恩达在Coursera上开设的Machine Learning课程，经过数年的改进和传播，目前已有许多中文学习资料。吴恩达本人对这门课也很有感情，他曾表示自己保留斯坦福教职，很大程度上是因为想教这门课。. Programming Excerise 1: Linear Regression. m - Octave/MATLAB script for the later parts of the exercise. Machine learning is the science of getting computers to act without being explicitly programmed. CourseraのMachine Learning動画 Coursera Machine Learningの課題をPythonで: ex1（線形回帰）. 这门课程对想要了解和初步掌握机器学习的人来说是不二的选择. About this course: Machine learning is the science of getting computers to act without being explicitly programmed. 発展課題：Gradient Descentのlearning rateの考察. Linear Regression Andrew Ng Coursera Machine Learning Ex1. Be sure you preserve the folder attributes and the other files in that subfolder. I decided to prepare and discuss about machine learning algorithms in a different series which is valuable and can be unique throughout the internet. txt and ex1_multi. 打开练习文件夹（即ex1、ex2这些文件夹，我目前截图使用的是ex6）下\lib\目录中的submitWithConfiguration. Machine learning cannot replace existing physical models, but improve certain aspects of them. Andrew's class may be the common sense among ML practitioners. In Fall 2010, we transitioned to a 16:9 aspect ratio, distributing videos in MP4 format (with an H. implement linear regression and get to see it work on data. Coursera degrees cost much less than comparable on-campus programs. Instructions Download the programming assignment here. Sign in Sign up. Of course, that may not be applicable for you and there may be good reasons for that (for instance,. I found this function in the files for my homework assignment from machine learning class. My Machine Learning Notes Week 2 Mc Ai. Coursera S Machine Learning Notes. Implementation Note: If your learning rate is too large, J(theta) can di- verge and blow up', resulting in values which are too large for computer calculations. Coursera吴恩达机器学习week2的ex1编程作业代码 machine-learning-ex1 这是Coursera上 Week2 的ml-ex1的编程作业代码。经过测验，全部通过。 具体文件可以进入我的github 包括以下八个文件： % warmUpExercise. Jul 29, 2014 • Daniel Seita. Build career skills in data science, computer science, business, and more. See also: Pattern recognition Machine learning is a scientiﬁc discipline that explores. Machine Learning Foundations(机器学习基石)笔记 第一节; android下调试声卡驱动之Machine部分; Machine Learning - I. Coursera Machine Learning Week 6 Assignment Answers. Machine Learning. ++Gradient descent, also known as steepest descent, is an iterative optimization algorithm for finding a local minimum of differentiable functions. Cost computation: Use the linear cost function for J (from ex1 and ex5) for the unregularized portion. coursera Machine Learning Week2 学习笔记. m in machine-learning-coursera-assignment-codes | source code search engine Toggle navigation. I would recommend you to do it in octave or in matlab. Many folks registered in the class expressed a similar interest, so I decided to share my attempts at the programming exercises here. After implementing gradient descent in the first exercise (goal is to predict the price of a 1650 sq-ft, 3 br house), the J_history shows me a list of the same value (2. multi I figured it out later on, just didnt change it yet in the first file. Hey! I would recommend a couple of steps: 1. 吴恩达（Andrew Ng）在 Coursera 上开设的机器学习入门课《Machine Learning》，授课地址是： Coursera Andrew Ng Machine Learning. It is a great course, highly recommended for those who wants to work in the AI / Data Science field or get a better understanding of these fast developing and highly sought after skills. 环境：windows7一、使用matlab1、一开始使用matlab，但在提交时发现ex1/lib/makeValidFieldName. Contribute to ahawker/machine-learning-coursera development by creating an account on GitHub. These are my learning exercices from Coursera. Before running the code make sure that you are in the same directory. Machine Learning | Coursera. Machine Learning (Spring 2014). This patch works around the defective printf() function in Octave 4. courseraのmachine learning講座をやりはじめたのですが、week2のプログラミング課題の任意課題（ex1_multi）を実行すると下記のようにwarningが発生してしまいます。. Programming Excerise 1: Linear Regression. Here is the note from the mentioned pdf. “machine-learning-ex1” is the folder that I downloaded for the week two assignment. I have tried to provide multiple solutions for same problem like Using for loop & Vectorized Implementation (Optimiz. Large Margin Classification; Kernels; SVMs. Because it has attracted low-quality or spam answers that had to be removed, posting an answer now requires 10 reputation on this site (the association bonus does not count). Andrew's class may be the common sense among ML practitioners. m的18行应该为if(isoct)，否则程序都无法跑. Coursera Machine Learningの課題をPythonで: ex7-1 (K-meansクラスタリングで画像圧縮) Coursera Machine Learningの課題をPythonで: ex1（線形. About this course: Machine learning is the science of getting computers to act without being explicitly programmed. Sign in to leave a comment. This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. Linear Regression with One Variable单变量线性回归 (Week 1) coursera Machine Learning ex1; coursera Machine Learning ex2. I would recommend you to do it in octave or in matlab. Programming Exercise 2: Logistic Regression Machine Learning October 20, 2011 Introduction In this exercise, you will implement logistic. After implementing gradient descent in the first exercise (goal is to predict the price of a 1650 sq-ft, 3 br house), the J_history shows me a list of the same value (2. We start the course by considering a retrieval task of fetching a document similar to one someone is currently reading. Classification. ・Coursera / Machine Learningの教材を2度楽しむ - Qiita ・Coursera Machine Learningの課題をPythonで: ex1（線形回帰） - Qiita （以下雑記） あと，web系の勉強の今後の方針．サーバーサイド言語の勉強を始めたい．Ruby流行ってるしいいのでは？. Now that the ex1 homework period is over, can we have a fully vectorized multiple variables gradient descent function? I don't have much confidence in the one i submitted because i bet it's hideously unoptimized, and would like to see one that'd work in the real world with lots of features and data. Finally, we'd like to make some predictions using the learned hypothesis. Here is the code for submitWithConfiguration function but I do not have the code for 'parts' function as the code is for a course on coursera and it is used to submit the code on the website. Machine_learning Machine learning ,Andrew Ng. Coursera Machine Learning Week 6 Assignment Answers. 在[email protected]座谈会上，Daphne Koller在采访中说道，截至到2012年11月，Coursera上有来自196国家的超过190万人。他们至少注册过一门课堂，尽管有数百万人注册过课堂，但完成率仅是7-9%。(维基百科) 5. This post is part of a series covering the exercises from Andrew Ng's machine learning class on Coursera. I have tried to provide multiple solutions for same problem like Using for loop & Vectorized Implementation (optimiz.