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. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. The course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots perception, controltext understanding web search, anti-spamcomputer vision, medical informatics, audio, database mining, and other areas.
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 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. 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. 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.
This course includes programming assignments designed to help you understand how to implement the learning algorithms in practice. Logistic regression is a method for classifying data into discrete outcomes.Set the seed to and fit a CART model with the rpart method using all predictor variables and default caret settings.
In the final model what would be the final model prediction for cases with the following variable values:. If K is small in a K-fold cross validation is the bias in the estimate of out-of-sample test set accuracy smaller or bigger? If K is small is the variance in the estimate of out-of-sample test set accuracy smaller or bigger. Is K large or small in leave one out cross validation?
Answer: The bias is larger and the variance is smaller.
Coursera: Machine Learning (Week 3) Quiz - Logistic Regression | Andrew NG
Under leave one out cross validation K is equal to the sample size. Fit a classification tree where Area is the outcome variable. Then predict the value of area for the following data frame using the tree command with all defaults. Answer: 2. It is strange because Area should be a qualitative variable - but tree is reporting the average value of Area as a numeric variable in the leaf predicted for newdata. Set the variable y to be a factor variable in both the training and test set.
Then set the seed to Fit a random forest predictor relating the factor variable y to the remaining variables. Calculate the variable importance using the varImp function in the caret package. What is the order of variable importance? Answer: There is no such version of answer.
Possible due to wrong version of some library. Question 2 If K is small in a K-fold cross validation is the bias in the estimate of out-of-sample test set accuracy smaller or bigger? If so, use a 2 level factor as your outcome column. Question 5 Load the vowel. Attaching package: 'randomForest' The following object is masked from 'package:ggplot2': margin set.Github repo for the Course: Stanford Machine Learning Coursera Quiz Needs to be viewed here at the repo because the image solutions cant be viewed as part of a gist.
A doubt in reasoning. The new cost function made by adding two squares can have two local minima. So, why is this option wrong? Why can't we take normal regularisation then option b will be true.
Only the first one seems to be incorrect True or False section of this quiz is very confusing. Skip to content. Instantly share code, notes, and snippets. Code Revisions 1 Stars 15 Forks 5. Embed What would you like to do? Embed Embed this gist in your website.
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Adding many new features gives us more expressive models which are able to better fit our training set. If too many new features are added, this can lead to overfitting of the training set. False Introducing regularization to the model always results in equal or better performance on examples not in the training set.
If we introduce too much regularization, we can underfit the training set and this can lead to worse performance even for examples not in the training set. False Introducing regularization to the model always results in equal or better performance on the training set. If we introduce too much regularization, we can underfit the training set and have worse performance on the training set.
True Adding a new feature to the model always results in equal or better performance on the training set Adding many new features gives us more expressive models which are able to better fit our training set. Question 2 Answer Explanation Adding many new features to the model helps prevent overfitting on the training set. Regularized logistic regression and regularized linear regression are both convex, and thus gradient descent will still converge to the global minimum.Ipad pro review
None needed Question 4 Answer Explanation The hypothesis follows the data points very closely and is highly complicated, indicating that it is overfitting the training set Question 5 Answer Explanation The hypothesis does not predict many data points well, and is thus underfitting the training set. This comment has been minimized. Sign in to view. Copy link Quote reply.Sap print invoice to pdf
Why can't we take normal regularisation then option b will be true yes you can use, but the given statement is not valid for all the possibilities, hence it is false. Question 1 is with the wrong solution.This is the second course in the four-course specialization Python Data Products for Predictive Analytics, building on the data processing covered in Course 1 and introducing the basics of designing predictive models in Python.
In this course, you will understand the fundamental concepts of statistical learning and learn various methods of building predictive models.
At each step in the specialization, you will gain hands-on experience in data manipulation and building your skills, eventually culminating in a capstone project encompassing all the concepts taught in the specialization. Loupe Copy. Classification: Logistic Regression. Enroll for Free. From the lesson. This week, we will learn about classification and several ways you can implement it, such as K-nearest neighbors, logistic regression, and support vector machines.
Supervised Learning: Classification Classification: Nearest Neighbors Classification: Logistic Regression Introduction to Support Vector Machines Taught By. Julian McAuley Assistant Professor. Try the Course for Free. Explore our Catalog Join for free and get personalized recommendations, updates and offers. Get Started.Assembly
All rights reserved.Last week I started with linear regression and gradient descent. This week week three we learned about how to apply a classification algorithm called logistic regression to machine learning problems. As before, here is the ipython notebook of my code.Astm c94
However, in a machine learning context logistic regression is commonly used as a classification algorithm. A classification algorithm is used to assign data into discrete categories, for example filtering our emails into spam or not spam, or diagnosing a tumour as malignant or benign. In its simplest form, we are considering just one outcome, which can be one of two states e.
Why then is this called logistic regression and not logistic classification? Fundamentallythe continuous variable that we are modelling with logistic regression in this context is the probability that our new input belongs to a particular class. Logistic regression only becomes a classification algorithm when we also decide on a probability threshold for assignment into one category or another more on this later.
Ok, but why this function? For example, if we were classifying tumours into malignant or benign based on volume, we could just decide that for every increment of increase in volume, the tumour is incrementally more likely to be malignant, and vice versa.
Why get fancy about it? The logistic function is defined as:. This is very useful for describing probability, since the probability that an event can occur will never be greater than 1 or less than 0. Another reason to use the logistic function is that it nicely captures how changes in the input variable s over certain ranges have more influence on the probability y axis than over others.
The logistic function shows this in the steep section in the middle of the curve. If you want a fuller background explanation into why logistic regression is used for classification problems, I found this to be useful. Given two exam scores for students, we were tasked with predicting whether a given student got into a particular university or not. We have access to admissions data from previous years which will form our training set.
You can see a curve of where the boundary for admittance lies. We want to model where this boundary is and use it to predict the admissions success of future hopefuls. You may recall from my last post that a hypothesis in machine learning refers to an output from our machine learning algorithm.
We represent the logistic regression hypothesis mathematically as:. How do we interpret the outputs of this function? In the context of logistic regression, our hypotheses refer to the probabilities that our inputs belong to a particular class.
This statement can be otherwise be represented as:. Our input z can be a scalar value or a matrix.For quick searching Course can be found here Notes can be found in my Github.
This Specialization from leading researchers at the University of Washington introduces you to the exciting, high-demand field of Machine Learning. Through a series of practical case studies, you will gain applied experience in major areas of Machine Learning including Prediction, Classification, Clustering, and Information Retrieval.
You will learn to analyze large and complex datasets, create systems that adapt and improve over time, and build intelligent applications that can make predictions from data. Course can be found here Lecture slides can be found here Notes can be found in my Github.
About this course: Do you have data and wonder what it can tell you? Do you need a deeper understanding of the core ways in which machine learning can improve your business? Do you want to be able to converse with specialists about anything from regression and classification to deep learning and recommender systems? In this course, you will get hands-on experience with machine learning from a series of practical case-studies. At the end of the first course you will have studied.
Through hands-on practice with these use cases, you will be able to apply machine learning methods in a wide range of domains. This first course treats the machine learning method as a black box.Excel Skills for Business Essentials Weekly Assessment Quiz Answer (Macquarie University) [Coursera]
Using this abstraction, you will focus on understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms.
Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential applications of machine learning in practice. You will learn a broad range of machine learning methods for deriving intelligence from data, and by the end of the course you will be able to implement actual intelligent applications. These applications will allow you to perform predictions, personalized recommendations and retrieval, and much more.
If you continue with the subsequent courses in the Machine Learning specialization, you will delve deeper into the methods and algorithms, giving you the power to develop and deploy new machine learning services.
To begin, we recommend taking a few minutes to explore the course site. These assignments—one per Module 2 through 6—will walk you through Python implementations of intelligent applications for:. This introduction to the specialization provides you with insights into the power of machine learning, and the multitude of intelligent applications you personally will be able to develop and deploy upon completion.
We also discuss who we are, how we got here, and our view of the future of intelligent applications. For those interested, the slides presented in the videos for this module can be downloaded here: intro.
We will explore this idea within the context of our first case study, predicting house prices, where you will create models that predict a continuous value price from input features square footage, number of bedrooms and bathrooms,….If you want to break into cutting-edge AI, this course will help you do so.
Deep learning engineers are highly sought after, and mastering deep learning will give you numerous new career opportunities. Deep learning is also a new "superpower" that will let you build AI systems that just weren't possible a few years ago. In this course, you will learn the foundations of deep learning.
Coursera - Practical Machine Learning - Quiz 3
When you finish this class, you will: - Understand the major technology trends driving Deep Learning - Be able to build, train and apply fully connected deep neural networks - Know how to implement efficient vectorized neural networks - Understand the key parameters in a neural network's architecture This course also teaches you how Deep Learning actually works, rather than presenting only a cursory or surface-level description.
So after completing it, you will be able to apply deep learning to a your own applications. If you are looking for a job in AI, after this course you will also be able to answer basic interview questions. This is the first course of the Deep Learning Specialization. Excellent course!!! Great contribution to the community. Really, really good course. Especially the tips of avoiding possible bugs due to shapes. Also impressed by the heroes' stories.
Genuinely inspired and thoughtfully educated by Professor Ng. Thank you! Loupe Copy. Logistic Regression. Neural Networks and Deep Learning. Course 1 of 5 in the Deep Learning Specialization.
Enroll for Free. From the lesson.
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