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Stanford Online

Machine learning with graphs.

Stanford School of Engineering

How do diseases and information spread? How can we predict traffic or weather? Answering these questions requires massive amounts of data. Complex data can be represented as a graph of relationships and interactions between objects. Graph data structures can be ingested by algorithms such as neural networks to perform tasks including classification, clustering, and regression.

This course explores the computational, algorithmic, and modeling challenges specific to the analysis of massive graphs. By studying underlying graph structures, you will master machine learning and data mining techniques that can improve prediction and reveal insights on a variety of networks.

• Build more accurate machine learning models by understanding the underlying relational structures of your data.
• Understand and apply traditional methods for machine learning on graphs, such as node embeddings and PageRank.
• Leverage graph-structured data and make better predictions using graph neural networks. Construct your own graph neural network using PyTorch Geometric.
• Expand your understanding of data by incorporating different node and edge types in knowledge graphs.
• Discover recurring and significant patterns of interconnections in your data with network motifs and community structure.
• Scale up your neural networks with generative models for graphs.

Core Competencies

• Deep Generative Models for Graphs
• Graph Neural Networks
• Graph Structure of the Web
• Influence Maximization
• Knowledge Graphs
• Node Embeddings and Classification
• Representation Learning

What You Need to Get Started

Prior to enrolling in your first course in the AI Professional Program, you must complete a short application (15 min) to demonstrate:

• Proficiency in Python : Coding assignments will be in Python. Some assignments will require familiarity with basic Linux command line workflows.
• College Calculus and Linear Algebra : You should be comfortable taking (multivariable) derivatives and understand matrix/vector notation and operations.
• Probability Theory : You should be familiar with basic probability distributions (Continuous, Gaussian, Bernoulli, etc.) and be able to define concepts for both continuous and discrete random variables: Expectation, independence, probability distribution functions, and cumulative distribution functions.

Groups and Teams

Special Pricing

Have a group of five or more? Enroll as a group and learn together! By participating together, your group will develop a shared knowledge, language, and mindset to tackle challenges ahead. We can advise you on the best options to meet your organization’s training and development goals.

Teaching Team

Jure Leskovec

Computer Science

Jure Leskovec is an Associate Professor of Computer Science at Stanford University. Leskovec's research focuses on the analyzing and modeling of large social and information networks as the study of phenomena across the social, technological, and natural worlds. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and viruses over networks. Problems he investigates are motivated by large scale data, the Web and other on-line media. He also does work on text mining and applications of machine learning.

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The Best Free AI Training Courses for 2024: Upskill Yourself Today

With businesses finding new, inventive ways to make money with ChatGPT every day, it’s no surprise that AI training courses are becoming increasingly sought after.

Workers in all sorts of industries are looking to future-proof their jobs and upskill themselves in line with the rapid technological changes occurring. Luckily, companies like Microsoft and Google offer free AI training courses, as do some higher education institutions.

In this guide, we cover the best AI training courses currently available, as well as the benefits of learning about AI in the current job market. We’ve largely focused on free courses that offer immediate, foundational learning opportunities that you can start applying to your job role or career straight away, rather than paid degree courses that cost hundreds or thousands of dollars.

• Best Free AI Training Courses for Generative AI
• Best Free AI Training Courses For Programmers, Developers & Tech Experts
• Free College AI Courses and Training

The Benefits of Learning About AI

The benefits of developing your soft skills in an automated world.

• AI Training Course FAQs

The 8 Best Free AI Training Courses for Generative AI

Here, we’ve compiled a list of the best free AI training courses that focus on generative AI and how you can harness it, as well as foundational concepts in artificial intelligence. A lot of these courses are designed to be introductory sessions and geared toward beginners.

• Google’s Generative AI Learning Path (5 Courses)
• Microsoft’s “Transform Your Business With AI” Course
• Google’s “Machine Learning Foundational Course”
• LinkedIn’s “Career Essentials In Generative AI” Training Course
• “IBM: AI Foundations for Everyone” Training Course
• Digital Partner’s “Fundamentals of ChatGPT” Training Course
• DeepLearning.AI’s “AI for Everyone” (Coursera)
• Phil Ebner’s AI Crash Course (Udemy)

1. Google’s Generative AI Learning Path (5 short Courses)

One of the more generous courses available in terms of actual hours of learning, Google’s Generative AI Learning path has five short courses on it. All courses take one day to complete.

Seven of the courses are classified as introductory, including “Introduction to Generative AI”, “Introduction to Large Language Models” and “Generative AI Fundamentals”.

There are three courses within the learning path described as intermediate, including “Encoder-Decoder Architecture” and “Attention Mechanism: Overview”.

The first two introductory courses cover a lot of immediately applicable content, such as how to use prompt tuning to get the best out of large language models. There’s also a course on the responsible usage of AI.

Although there’s no official qualification, you will be awarded a completion badge that you can attach to your digital resume. Google has also recently expanded its paid certificates program which can be accessed through Coursera and has made its free courses available in 18 additional languages .

2. Microsoft’s “Transform Your Business With AI” course

This Microsoft learning path is designed, as the tech giant says, to help businesspeople acquire “the knowledge and resources to adopt AI in their organizations”, and explores “planning, strategizing, and scaling AI projects in a responsible way.”

Microsoft says the objectives for this course are to become familiar with existing AI tools, understand basic AI terminology and practices, and use prebuilt AI to build intelligent applications.

To enroll in this course, which is 2 hours and 40 minutes long, Microsoft says you’ll need a “basic understanding” of IT and business concepts. Modules included in the pathway are:

• Leverage AI tools and resources for your business (55 mins)
• Create business value from AI (21 min)
• Embrace responsible AI principles and practices (48 mins)
• Scale AI in your organization (36 mins)

3. Google’s “Machine Learning Foundational Course”

Along with the foundational AI course mentioned above, Google also provides an introductory course on machine learning that’s available for free. You can access the course with your Google Workspace or personal account, and there’s actually a surprising amount of material included, considering it’s free.

Included as part of the material is a “crash course” to machine learning, a section on “problem framing” and a course on how to prepare your data for machine learning workflows. Included are freely accessible videos explaining core concepts, provided by machine learning experts. You’ll then be able to “check your understanding” at the end of each section with a quiz.

While this course is a little more advanced than the other Google course to make this list – and has a bit more of a business focus – it’s worth checking out if your company is looking for new ways to implement AI/machine learning in the workplace.

4. LinkedIn’s “Career Essentials In Generative AI” training course

LinkedIn’s AI Career Essentials Course is made up of five different videos, with a total run time of around four hours. Each video is hosted by a different AI expert, covering a range of core concepts and ethical considerations relating to AI models.

One of the videos provides a detailed explanation of how to streamline your work with Microsoft Bing Chat, while another discusses the key differences between search engines and reasoning engines. Although there’s no accreditation or certification, completing this course will earn you a badge of completion from LinkedIn, which can be displayed on your profile.

The fifth video in the series, entitled “an Introduction to Artificial Intelligence” is an hour and a half long and provides a simplified overview of the best AI tools for businesses, which is handy for those who haven’t taken the plunge yet and implemented a tool in work.

5. IBM’s “AI Foundations for Everyone” training course

IBM offers a course entitled “ AI Foundations for Everyone ” through Coursera, of which over 2,000 people have already signed up for in just February 2024. You can audit the course for free, which will give you access to all of the materials and some of the assignments, but you won’t be graded or get a certificate at the end. For that, you’ll need a Coursera subscription.

It’s geared toward beginners and you don’t need prior experience to enroll, and the schedule is flexible so you can learn at your own pace. Along with AI fundamentals, the course will also ensure you’re familiar with IBM’s own AI services, which help businesses integrate artificial intelligence into their existing infrastructure.

IBM says that, by the end of the course, participants will have had “hands-on interactions with several AI environments and applications”.

The course has three modules: “Introduction to Artificial Intelligence”, “Getting Started with AI using IBM Watson”, and “Building AI-Powered Chatbots Without Programming”. Each module takes between nine and eleven hours to complete.

6. Digital Partner’s “The Fundamentals of ChatGPT” training course

Digital Partner’s course entitled “ The fundamentals of ChatGPT ” is a great option for anyone who wants to take a free, accredited course that covers the basics of Generative AI. As of February 2024, more than 8,000 learners have completed the short course.

During the course, you’ll spend time learning about OpenAI’s role in global AI development, and be able to learn about how ChatGPT works, its advantages and limitations. There’s also a variety of examples included within the course that will show you how to leverage ChatGPT for different tasks, and you’ll learn more about the difference between ChatGPT and ChatGPT Plus.

Modules include “Working With ChatGPT”, “ChatGPT and Its Shortcomings” and “Training a GPT Model”. This free course is available on alison.com, and is published by a digital marketing firm called Digital Partner. The course is CPD accredited and a certificate will be awarded upon completion of a small assessment at the end of the 1.5-3 hour program.

7. DeepLearning.AI’s “AI for Everyone” (Coursera)

Not to be confused with IBM’s similarly-named online AI training course, DeepLearning.AI’s “AI for Everyone” short course is worth enrolling in just to see what the fuss is all about. 1.2 million people have enrolled in this course and more than 40,000 have reviewed it, with an average score of 4,8/5 in February 2024 – perhaps because it’s run by lecturer Andrew Ng, an instructor with over 40 courses published on the site.

This course is split up into four broad modules: What Is AI?, Building AI Projects, Building AI in Your Company, and AI in Society. All four of the modules take less than an hour to complete, so you could feasibly complete the course in a day if you’re prepared to take the quizzes designed to recap each module.

Like most Coursera courses, you’ll be able to audit the course and access all the materials for free, although you’ll have to sign up for a paid account if you want the relevant accreditation once you complete the course.

8. Phil Ebner’s ChatGPT, Midjourney, Firefly, Bard, DALL-E” AI crash course

While some good courses on Udemy will guide you through the ins and outs of MidJourney and other AI generation tools, Phil Ebner covers the most ground, and more than 30,000 students have already enrolled in the course, which has a 4.6/5 rating on Udemy.

The course is almost two hours long and also includes content that will help you better use tools like ChatGPT to generate text responses as well as images.

The “AI for Visual Creativity” section, however, will show you how to use both MidJourney and Dall-E to create “photorealistic images, illustrations, and digital art in a variety of styles. With OpenAI’s Sora video generator seemingly just around the corner, there’s never been a better time to learn the basic principles of AI video generation and the tools that can do it.

On Udemy, you don’t receive certificates of completion for free courses, but if you’re just looking to upskill yourself free of charge, this course is definitely worth a look.

The 6 Best Free AI Training Courses for Programmers, Developers & Tech Experts

Up next, we have more advanced courses geared towards programming and development.

• Harvard University’s “Introduction to Artificial Intelligence with Python”
• DeepLearning.AI’s “ChatGPT Prompt Engineering For Developers” (Coursera)
• Intro to TensorFlow for Machine Learning (Udacity)
• Georgia Tech’s Reinforcement Learning (Udacity)
• Become an AI-Powered Engineer: ChatGPT, GitHub Copilot (Udemy)
• Great Learning’s “ChatGPT for Beginners” training course

1. Harvard University’s “Introduction to Artificial Intelligence with Python”

Harvard University offers a self-paced, 7-week course on the “concepts and algorithms at the foundation of modern artificial intelligence”.

The time commitment of between 10-30 hours a week – but it’s completely free to enroll and you’ll be supported as you complete projects and attend lectures. However, you need to have taken Harvard’s “Introduction to Computer Science” course first to enroll.

2. DeepLearning.AI’s “ChatGPT Prompt Engineering For Developers” (Coursera)

This course will help you utilize OpenAI’s API to write more effective prompts, learn how large language models can be used to carry out tasks like text transformation and summarizing, and teach you how to program and build a custom AI chatbot.

The course is run by AI expert and DeelLearning.AI co-founder Andrew Ng and OpenAI’s Isa Fulford, and it’s only an hour long. DeepLearning.AI says the course is “free for a limited time”. A basic understanding of Python is needed, but aside from that, it’s beginner-friendly.

3. Intro to TensorFlow for Machine Learning (Udacity)

This course teaches participants how to build deep-learning applications with TensorFlow, one of the most popular open-source Python software libraries.

The estimated completion time for the course is approximately two months, and you should have some experience with Python syntax, including variables, functions, and classes, as well as a grasp of basic algebra.

If you take the course, providers Udacity say, you’ll get “hands-on experience building your own state-of-the-art image classifiers” as well as other types of deep learning models.

4. Georgia Tech’s Reinforcement Learning (Udacity)

This Georgia Tech course is free on Udacity and focuses on exploring “automated decision-making from a computer-science perspective”.

At the end of the course – which takes approximately four months to complete, but is also described as self-paced – participants will recreate a result from a published paper on reinforcement learning.

However, it is recommended you have a graduate-level machine-learning qualification and some prior experience with reinforcement learning from previous studies. Experience with Java is also required.

Although there’s no official certificate awarded for completing the course, you can earn a nano degree program certificate by completing Udacity’s 4-month long “Deep Reinforcement Learning”, although this costs $1116.

5. Become an AI-Powered Engineer: ChatGPT, Github Copilot (Udemy)

In this course, students will learn how to create high-quality pieces of code using ChatGPT and integrate it with other text editors. It also covers how to use GitHub Copilot.

This might be a free tutorial, but the course has much better reviews than some of the other AI courses available on Udemy, with 56% of watchers who left a review giving the course five stars, and a further 24% giving it four stars at the time of writing.

The course will be best suited to developers who want to leverage AI tools for coding responsibilities in general, and also, to become more efficient in their coding practices.

6. GreatLearning’s “ChatGPT for Beginners” training course

This is a completely free, two-hour long beginners-focused ChatGPT course . It’s one of the only beginner’s courses on the internet that includes a section on coding prompts, although it also covers quite a bit of other ground, including email prompting.

There are no prerequisites needed for this course, and it has an average rating of 4.61/5, with 75% of reviewers giving the course 5 stars.

The 6 Best Free College AI Courses and Training

A number of universities and colleges offer AI-focused courses.

• Stanford University’s “Machine Learning” Course (Udacity)
• Vanderbilt University’s “Prompt Engineering for ChatGPT” (Coursera)
• Georgia Tech’s “Machine Learning” Course (Udacity)
• The Open University’s “AI Matters” Course (OpenLearn)
• University of Pennsylvania’s “AI For Business” (Coursera)
• University of Helsinki’s “Elements of AI” and “Ethics of AI” Course

1. Stanford University’s “Introduction to Artificial Intelligence” course (Udacity)

This foundational online program , which takes around 10 months to complete at a rate of 10 hours a week, focuses on fundamental AI concepts and practical machine learning skills but is classified as an intermediate course.

The course, which is split into two umbrella sections (“Fundamentals of AI” and “Applications of AI) is completely free if you sign up for Udacity (which also doesn’t cost anything). Unlike Coursera courses, you have access to the full range of materials and teaching, rather than just the ability to audit. It consists of 22 different lessons and a string of interactive quizzes.

2. Vanderbilt University’s “Prompt Engineering for ChatGPT” (Coursera)

Jules White, Vanderbilt University’s associate dean for strategic learning programs and associate professor of computer science, has launched a free online course available through Coursera focusing on prompt engineering.

It goes through the most effective approaches for prompt engineering, covering summarization, simulation, programming, and other useful ways you can harness the power of ChatGPT with your inputs. It’s got one of the highest approval ratings we’ve seen for an AI training course, scoring 4.8/5 with an approval rating of 98% as of February 2024.

The course takes around 18 hours to complete and is made up of an introduction to prompts and three separate sessions on prompt patterns, as well as a 2-hour module on examples. You can either audit this course for free, which means you get access to all the materials but no proof you’ve completed it, or pay for a Coursera subscription (roughly $59 per month). However, you won’t have to pay anything on top of that fee for this course.

3. Georgia Tech’s “Machine Learning” course (Udacity)

In collaboration with Georgia Tech, Udacity has made an intermediate machine-learning course available for free, which takes around 4 months to complete, although the course listing says you can do it at your own pace.

The course is offered as part of an online master’s degree at Georiga Tech, but taking this course won’t earn you credit toward this degree. It includes information on Supervised and Unsupervised Learning, which are two different types of machine learning, and covers how they’re used in AI systems.

However, having a “strong familiarity with Probability Theory, Linear Algebra, and Statistics” and prior experience with statistics is helpful. Students should also have some experience with programming.

4. The Open University’s “AI Matters” course (OpenLearn)

The Open University is a UK-based institution that offers a free course through its learning portal OpenLearn entitled “AI Matters”.

In the course, you’ll learn about the “historical, social, political and economic issues in AI”, explore the benefits and limitations of the technology, and discuss ethical risks relating to AI.

The course is six hours long, and you’ll be eligible for a “statement of completion” from the organization, which has university status in the UK.

5. University of Pennsylvania’s “AI For Business” (Coursera)

The University of Pennsylvania’s “AI for Business” specialization is made up of four different, free courses:

• AI Applications in People Management
• AI Fundamentals for Non-Data Scientists
• AI Applications in Marketing and Finance
• AI Strategy and Finance

In 2023, the University of Pennsylvania’s website detailed that the course itself costs $39 to complete, and you could enroll in the four individual modules for free. Each module took around two hours a week to complete.

However, in 2024, they’ve upped the price to $79. However, we’d recommend accessing it via Coursera – the modules take around 7-9 hours to complete, and if you purchased the 7-day free trial, it could be done in that time. You can also audit the course for free, which means you won’t have proof of completion.

6. University of Helsinki’s “Elements of AI” and “Ethics of AI”

The University of Helsinki has two, free online courses available. The course entitled “ Ethics of AI ” is geared towards “anyone who is interested in the ethical aspects of AI”, the university says.

The course will familiarize you with common questions that arise in AI ethics and the various ways to approach them.

“ Elements of AI ” is a broader course with 6 chapters, focusing on topics such as “neural networks”, “machine learning” and “AI problem solving”.  All you need to do to access the course materials is sign up.

Of course, completing an AI training course can have several benefits. From a personal learning perspective, it’s one of the best ways you can spend your time – AI is here to stay, and getting a better grasp of how it works might just help you out in the near future.

Plus, the things you learn about AI will be applicable to a wide variety of job roles in almost every sector of the economy, so it’s arguably a safer bet than completing a course on a niche or industry-specific topic.

What’s more, right now, businesses are looking for people who understand how generative AI tools like ChatGPT work, and how to leverage them effectively. Employees that are conscious of the limitations of AI tools and able to generate useful responses using prompts are going to become more sought after than employees without these skills.

Completing an AI training course is going to look good on your CV, which will help if you’re applying for a new job. Evidence that you’ve taken the initiative to explore an emerging technology is definitely something an employer will find desirable.

Of course, if you don’t have much of a budget – or you’re not entirely sure what AI training course would be the best use of your time – then trying out some free options is a great place to start.

If you’re looking for AI training courses to enhance your understanding of something that’s going to dominate the business world for years to come, you’re already one step ahead of the crowd.

Completing an AI course isn’t the only way to prepare yourself for the seismic changes to the global economy that will occur as AI is harnessed by more businesses.

One other thing you can do if you want to upskill yourself is to develop your soft skills . These are the “human” skills that AI tools like ChatGPT don’t have. Businesses are always going to need humans to organize, prioritize, and take responsibility for planning and executing projects.

With AI taking control of more and more repetitive administrative tasks, having strong soft skills is going to be increasingly important to stand out in a pool of highly-qualified job applicants or colleagues jostling for promotion.

Developing your soft skills can consist of taking an accredited course – but they are more than often improved by taking experiences, especially in the workplace. It’s vital, then, to put yourself forward for opportunities and experiences that will let you expand your communication, organization, and general leadership skills. You can be taught to work a piece of software in a week, but you can’t click your fingers and make someone a proficient public speaker or project manager.

Is Google's AI certification free?

How do i ai proof my job, can i learn ai on my own, is microsoft's ai course free, how does coursera's pricing work.

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Coursera: Machine Learning (Week 7) [Assignment Solution] - Andrew NG

Recommended Machine Learning Courses: Coursera: Machine Learning    Coursera: Deep Learning Specialization Coursera: Machine Learning with Python Coursera: Advanced Machine Learning Specialization Udemy: Machine Learning LinkedIn: Machine Learning Eduonix: Machine Learning edX: Machine Learning Fast.ai: Introduction to Machine Learning for Coders

• ex6.m - Octave/MATLAB script for the first half of the exercise
• ex6data1.mat - Example Dataset 1
• ex6data2.mat - Example Dataset 2
• ex6data3.mat - Example Dataset 3
• svmTrain.m - SVM training function
• svmPredict.m - SVM prediction function
• plotData.m - Plot 2D data
• visualizeBoundaryLinear.m - Plot linear boundary
• visualizeBoundary.m - Plot non-linear boundary
• linearKernel.m - Linear kernel for SVM
• [*] gaussianKernel.m - Gaussian kernel for SVM
• [*] dataset3Params.m - Parameters to use for Dataset 3
• ex6 spam.m - Octave/MATLAB script for the second half of the exercise
• spamTrain.mat - Spam training set
• spamTest.mat - Spam test set
• emailSample1.txt - Sample email 1
• emailSample2.txt - Sample email 2
• spamSample1.txt - Sample spam 1
• spamSample2.txt - Sample spam 2
• vocab.txt - Vocabulary list
• getVocabList.m - Load vocabulary list
• porterStemmer.m - Stemming function
• readFile.m - Reads a file into a character string
• submit.m - Submission script that sends your solutions to our servers
• [*] processEmail.m - Email preprocessing
• [*] emailFeatures.m - Feature extraction from emails
• Video - YouTube videos featuring Free IOT/ML tutorials

gaussianKernel.m :

Dataset3params.m :, processemail.m :, check-out our free tutorials on iot (internet of things):.

emailFeatures.m :

26 comments.

processEmail code is not running in matlab , it is showing the following error in the command prompt : !! Submission failed: unexpected error: Error using fprintf Function is not defined for 'cell' inputs. Error from file:/MATLAB Drive/machine-learning-ex/ex6/processEmail.m This is line 114 : fprintf('%s ', str); How to resolve it . And , error 2 is catch str = ''; continue; in the above line it is telling variable assigned to variable "str" might be unused . Function:processEmail On line:114 And third error is : word_indices = {word_indices; index}; In the above line it is telling variable "word_indices" tend to change size on every loop iteration . Consider preallocating for speed .

All the above errors mentioned are in the processemail part only .

Hi Alankar, First of all, These are not Errors, These are warnings. You might have made some silly mistakes in your code. I feel you haven't understood the code I have provided above. Please try to understand that and then write you logic. Don't just copy paste blindly.

In this line of code: coderesult = zeros(length(C_list)+length(sigma_list),3) you would get a 16x3 matrix since both arrays are 8 units long. However, wouldn't you need a 64x3 matrix since we need to try out each possibility in C_list and sigma_list, which would mean trying out 64 different permutations?

I have the same question, would be helpful if you could answer this

Well, after posting the comment, I tried to investigate further. It does not matter what size you give for result 1) you can initialize with size(64,3) 2) Even though the size is (16,3) , you can still add more rows like 17 onward. Hope it helps

Yes. In MATLAB, matrix has capability to update. i.e You can change the size of Matrix after initializing it. BUT, If you keep on updating/changing the size of matrix in each iteration, you will get the warning and you code will be slower (not optimized). So, It is always advised to initialize the matrix with it final size (if known) and then only update the values on the matrix not the size.

Thank you for your replying after figuring out the solution. It saved my time.

Your code for dataset3param gives c =0.1and sigma =0.1 which is not correct. Correct value for c and sigma is 0.3 and 0.1 respectively.

Thanks for the feedback. You might be correct. Coursera keep on updating their assignments time to time. All my answers belongs to the time I was doing it. and these were 100% correct answers by then.

Hey Akshay, I have a suggestion for a small optimization.. In the emailFeatures.m we can instead write for i = word_indices x(i) = 1; end Hope its better

This comment has been removed by the author.

In emailfeatures.m rather than using loop x(word_indices,1)=1;

Thanks. Did it work for you?

Yes, works perfect!

why is i showing training " out of time" error

what is the use of @.

What is the value for the features in Gaussian kernel,can you help me in understanding the criteria for selecting x1,x2 in svmTrain.m

Please could you explain to me what's the difference between svmtrain and svmpredict ? what are the results returned? I get a little confused. Thank you in advance

https://www.mathworks.com/matlabcentral/answers/320129-what-does-do This may help

Thanks for sharing the meaning of "@" symbol in MATLAB.

please could you explain to me in the dataset3Params.m why the result = zeros(length(C_list)+length(sigma_list),3); is not result = zeros(length(C_list)*length(sigma_list),3);?

Hi Akshay,a question: in emailFeatures.m length(word_indices) = 53 why are there 45 but not 53 non-zero entries???

Hi! Thank you for your code! It is useful to see different ways to solve the exercises. In my case I followed the tutorial indications and I didn't use any for loop in emailFeature.m, so I just wrote: x(word_indices) = 1; And that's all! It worked and submitted perfectly so it seems to be fine and it's just one line :D

Hi!! I use online matlab to execute code. For both parameters to be used for data set 3 and process email code it takes a long time for training or execution and matlab session gets timed out and the process starts all over again. Please can you help me out with proper parameter values or any another solution to solve this problem. Thank you!!

i am using octave and while submitting it shows "training...... done training..... done...." but My assignment is not submitting. i dont know why.............someone plz help me.............................

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Personal Solutions to Programming Assignments on Matlab

koushal95/Coursera-Machine-Learning-Assignments-Personal-Solutions

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Coursera-machine-learning-assignments-personal-solutions.

Exercises are done on Matlab R2017a

This repository consists my personal solutions to the programming assignments of Andrew Ng's Machine Learning course on Coursera.

Course Schedule

Introduction

Linear Regression with One Variable

Linear Algebra Review

Linear Regression with Multiple Variables

Octave / Matlab Tutorial

Programming Exercise 1

Logistic Regression

Regularization

Programming Exercise 2

Neural Network Representation

Programming Exercise 3

Neural Networks: Learning

Programming Exercise 4

Advice for Applying Machine Learning

Programming Exercise 5

Machine Learning System Design

Support Vector Machines

Programming Exercise 6

Unsupervised Learning

Dimensionality Reduction

Programming Exercise 7

Anomaly Detection

Recommender Systems

Programming Exercise 8

Large Scale Machine Learning

Application Example: Photo OCR

• MATLAB 100.0%