** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka tutorial will provide you with a detailed and comprehensive knowledge of the Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:
1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes
Check out our playlist for more videos: http://bit.ly/2taym8X
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.
** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka tutorial will provide you with a detailed and comprehensive knowledge of the Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:
1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes
Check out our playlist for more videos: http://bit.ly/2taym8X
Introduction to Bayesian classifier. It describes the basic algorithm and applications of Bayesian classification. Explained with the help of numerical problems.
This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
- - - - - - -
Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...Edureka!
(Python Certification Training for Data Science: https://www.edureka.co/python)
This Edureka video on "Scikit-learn Tutorial" introduces you to machine learning in Python. It will also takes you through regression and clustering techniques along with a demo on SVM classification on the famous iris dataset. This video helps you to learn the below topics:
1. Machine learning Overview
2. Introduction to Scikit-learn
3. Installation of Scikit-learn
4. Regression and Classification
5. Demo
Subscribe to our channel to get video updates. Hit the subscribe button and click the bell icon.
Logistic Regression in Python | Logistic Regression Example | Machine Learnin...Edureka!
** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:
1. What is Regression?
2. What is Logistic Regression?
3. Why use Logistic Regression?
4. Linear vs Logistic Regression
5. Logistic Regression Use Cases
6. Logistic Regression Example Demo in Python
Subscribe to our channel to get video updates. Hit the subscribe button above.
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This Naive Bayes Classifier tutorial presentation will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this presentation, you will also implement Naive Bayes algorithm for text classification in Python.
The topics covered in this Naive Bayes presentation are as follows:
1. What is Naive Bayes?
2. Naive Bayes and Machine Learning
3. Why do we need Naive Bayes?
4. Understanding Naive Bayes Classifier
5. Advantages of Naive Bayes Classifier
6. Demo - Text Classification using Naive Bayes
- - - - - - - -
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
- - - - - - - -
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Backpropagation And Gradient Descent In Neural Networks | Neural Network Tuto...Simplilearn
This presentation about backpropagation and gradient descent will cover the basics of how backpropagation and gradient descent plays a role in training neural networks - using an example on how to recognize the handwritten digits using a neural network. After predicting the results, you will see how to train the network using backpropagation to obtain the results with high accuracy. Backpropagation is the process of updating the parameters of a network to reduce the error in prediction. You will also understand how to calculate the loss function to measure the error in the model. Finally, you will see with the help of a graph, how to find the minimum of a function using gradient descent. Now, let’s get started with learning backpropagation and gradient descent in neural networks.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Linear Regression Algorithm | Linear Regression in Python | Machine Learning ...Edureka!
** Machine Learning Training with Python: https://www.edureka.co/python **
This Linear Regression Algorithm tutorial is designed in a way that you learn about the algorithm in depth. This tutorial is designed in a way that in the first part you will learn about the algorithm from scratch with its mathematical implementation, then you will drill down to the coding part and implement linear regression using python. Below are the topics covered in this tutorial:
1. What is Regression?
2. Regression Use-case
3. Types of Regression – Linear vs Logistic Regression
4. What is Linear Regression?
5. Finding best-fit regression line using Least Square Method
6. Checking goodness of fit using R squared Method
7. Implementation of Linear Regression Algorithm using Python (from scratch)
8. Implementation of Linear Regression Algorithm using Python (scikit lib)
Check out our playlist for more videos: http://bit.ly/2taym8X
Logistic Regression in Python | Logistic Regression Example | Machine Learnin...Edureka!
** Python Data Science Training : https://www.edureka.co/python **
This Edureka Video on Logistic Regression in Python will give you basic understanding of Logistic Regression Machine Learning Algorithm with examples. In this video, you will also get to see demo on Logistic Regression using Python. Below are the topics covered in this tutorial:
1. What is Regression?
2. What is Logistic Regression?
3. Why use Logistic Regression?
4. Linear vs Logistic Regression
5. Logistic Regression Use Cases
6. Logistic Regression Example Demo in Python
Subscribe to our channel to get video updates. Hit the subscribe button above.
Machine Learning Tutorial Playlist: https://goo.gl/UxjTxm
K-Nearest neighbor is one of the most commonly used classifier based in lazy learning. It is one of the most commonly used methods in recommendation systems and document similarity measures. It mainly uses Euclidean distance to find the similarity measures between two data points.
( Python Training: https://www.edureka.co/python )
This Edureka Python Numpy tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) explains what exactly is Numpy and how it is better than Lists. It also explains various Numpy operations with examples.
Check out our Python Training Playlist: https://goo.gl/Na1p9G
This tutorial helps you to learn the following topics:
1. What is Numpy?
2. Numpy v/s Lists
3. Numpy Operations
4. Numpy Special Functions
This Naive Bayes Classifier tutorial presentation will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this presentation, you will also implement Naive Bayes algorithm for text classification in Python.
The topics covered in this Naive Bayes presentation are as follows:
1. What is Naive Bayes?
2. Naive Bayes and Machine Learning
3. Why do we need Naive Bayes?
4. Understanding Naive Bayes Classifier
5. Advantages of Naive Bayes Classifier
6. Demo - Text Classification using Naive Bayes
- - - - - - - -
Simplilearn’s Machine Learning course will make you an expert in Machine Learning, a form of Artificial Intelligence that automates data analysis to enable computers to learn and adapt through experience to do specific tasks without explicit programming. You will master Machine Learning concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, hands-on modeling to develop algorithms and prepare you for the role of Machine Learning Engineer
Why learn Machine Learning?
Machine Learning is rapidly being deployed in all kinds of industries, creating a huge demand for skilled professionals. The Machine Learning market size is expected to grow from USD 1.03 billion in 2016 to USD 8.81 billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
You can gain in-depth knowledge of Machine Learning by taking our Machine Learning certification training course. With Simplilearn’s Machine Learning course, you will prepare for a career as a Machine Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, Naive Bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
- - - - - - - -
Part 1 of the Deep Learning Fundamentals Series, this session discusses the use cases and scenarios surrounding Deep Learning and AI; reviews the fundamentals of artificial neural networks (ANNs) and perceptrons; discuss the basics around optimization beginning with the cost function, gradient descent, and backpropagation; and activation functions (including Sigmoid, TanH, and ReLU). The demos included in these slides are running on Keras with TensorFlow backend on Databricks.
Backpropagation And Gradient Descent In Neural Networks | Neural Network Tuto...Simplilearn
This presentation about backpropagation and gradient descent will cover the basics of how backpropagation and gradient descent plays a role in training neural networks - using an example on how to recognize the handwritten digits using a neural network. After predicting the results, you will see how to train the network using backpropagation to obtain the results with high accuracy. Backpropagation is the process of updating the parameters of a network to reduce the error in prediction. You will also understand how to calculate the loss function to measure the error in the model. Finally, you will see with the help of a graph, how to find the minimum of a function using gradient descent. Now, let’s get started with learning backpropagation and gradient descent in neural networks.
Why Deep Learning?
It is one of the most popular software platforms used for deep learning and contains powerful tools to help you build and implement artificial neural networks.
Advancements in deep learning are being seen in smartphone applications, creating efficiencies in the power grid, driving advancements in healthcare, improving agricultural yields, and helping us find solutions to climate change. With this Tensorflow course, you’ll build expertise in deep learning models, learn to operate TensorFlow to manage neural networks and interpret the results.
And according to payscale.com, the median salary for engineers with deep learning skills tops $120,000 per year.
You can gain in-depth knowledge of Deep Learning by taking our Deep Learning certification training course. With Simplilearn’s Deep Learning course, you will prepare for a career as a Deep Learning engineer as you master concepts and techniques including supervised and unsupervised learning, mathematical and heuristic aspects, and hands-on modeling to develop algorithms. Those who complete the course will be able to:
1. Understand the concepts of TensorFlow, its main functions, operations and the execution pipeline
2. Implement deep learning algorithms, understand neural networks and traverse the layers of data abstraction which will empower you to understand data like never before
3. Master and comprehend advanced topics such as convolutional neural networks, recurrent neural networks, training deep networks and high-level interfaces
4. Build deep learning models in TensorFlow and interpret the results
5. Understand the language and fundamental concepts of artificial neural networks
6. Troubleshoot and improve deep learning models
7. Build your own deep learning project
8. Differentiate between machine learning, deep learning, and artificial intelligence
Learn more at https://www.simplilearn.com/deep-learning-course-with-tensorflow-training
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Linear Regression Algorithm | Linear Regression in Python | Machine Learning ...Edureka!
** Machine Learning Training with Python: https://www.edureka.co/python **
This Linear Regression Algorithm tutorial is designed in a way that you learn about the algorithm in depth. This tutorial is designed in a way that in the first part you will learn about the algorithm from scratch with its mathematical implementation, then you will drill down to the coding part and implement linear regression using python. Below are the topics covered in this tutorial:
1. What is Regression?
2. Regression Use-case
3. Types of Regression – Linear vs Logistic Regression
4. What is Linear Regression?
5. Finding best-fit regression line using Least Square Method
6. Checking goodness of fit using R squared Method
7. Implementation of Linear Regression Algorithm using Python (from scratch)
8. Implementation of Linear Regression Algorithm using Python (scikit lib)
Check out our playlist for more videos: http://bit.ly/2taym8X
A Deep Dive into Classification with Naive Bayes. Along the way we take a look at some basics from Ian Witten's Data Mining book and dig into the algorithm.
Presented on Wed Apr 27 2011 at SeaHUG in Seattle, WA.
A Semi-naive Bayes Classifier with Grouping of CasesNTNU
In this work, we present a semi-naive Bayes classifier that searches for dependent attributes using different filter approaches. In order to avoid that the number of cases of the compound attributes be too high, a grouping procedure is applied each time after two variables are merged. This method tries to group two or more cases of the new variable into an unique value. In an emperical study, we show as this approach outperforms the naive Bayes classifier in a very robust way and reaches the performance of the Pazzani’s semi-naive Bayes [1] without the high cost of a wrapper search.
Wikipedia, Dead Authors, Naive Bayes and Python Abhaya Agarwal
My slides from PyCon 2011. The talk is about identifying Indian authors whose works are now in Public Domain. We use Wikipedia for this purpose and pose it as a document classification problem. Naive Bayes is used for the task.
In non-parametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. A kernel is a non-negative real-valued symmetric and integrable function K. Several types of kernel functions are commonly used: uniform, triangle, Epanechnikov, quartic (biweight), tricube, triweight, Gaussian, quadratic and cosine. In this presentation we will talk about the properties and applications of kernel functions.
Simulation de phénomènes naturels : Explorer comment la modélisation et la simulation sont utilisées pour simuler des phénomènes naturels tels que les catastrophes naturelles (ouragans, tremblements de terre, inondations), les phénomènes météorologiques, etc., afin de mieux comprendre leur comportement et d'améliorer les mesures de prévention et de préparation.
HackYale - Natural Language Processing (Week 1)Nick Hathaway
Slides for a course I taught on Natural Language Processing covering corpus manipulation, word tokenization and text classification tasks using Python's popular Natural Language Toolkit.
1. Building a Naive Bayes Classifier
Eric Wilson
Search Engineer
Manta Media
2. The problem: Undesirable Content
Recommended by 3 people:
Bob Perkins
It is a pleasure to work with Kim! Her work is beautiful and she is
professional, communicative, and friendly.
Fred
She lied and stole my money, STAY AWAY!!!!!
Jane Robinson
Very Quick Turn Around as asked - Synced up Perfectly Great Help!
3. Possible solutions
● First approach: manually remove undesired
content.
● Attempt to filter based on lists of banned
words.
● Use a machine learning algorithm to identify
undesirable content based on a small set of
manually classified examples.
4. Using Naive Bayes isn't too hard!
● We'll need a bit of probability, including the
concept of conditional probability.
● A few natural language processing ideas will
be necessary.
● Facility with any modern programming
language.
● Persistence with many details.
5. Probability 101
Suppose we choose a number from the set:
U = {1,2,3,4,5,6,7,8,9,10}
Let A be the event that the number is even,
and B be the event that the number is prime.
Compute P(A), P(B), P(A|B), and P(B|A),
where P(A|B) is the probability of A given B.
8. A simplistic language model
Consider each document to be a set of words,
along with frequencies.
For example: “The premium quality for the
discount price” is viewed as:
{'the':2, 'premium':1, 'quality':1, 'for':1,
'discount':1, 'price':1}
Same as “The discount quality for the premium
price,” since we don't care about order.
9. That seems … foolish
● English is so complicated that we won't have
any real hope of understanding semantics.
● In many real-life scenarios, text that you want
to classify is not exactly subtle.
● If necessary, we can improve our language
model later.
10. An example:
Type Text Class
Training Good happy good Positive
Training Good good service Positive
Training Good friendly Positive
Training Lousy good cheat Negative
Test Good good good cheat lousy ??
In order to be able to perform all calculations, we
will use an example with extremely small
documents.
11. What was the question?
We are trying to determine whether the last
recommendation was positive or negative.
We want to compute:
P(Pos|good good good lousy cheat)
By Bayes Theorem, this is equal to:
P(Pos)P(good good good lousy cheat|Pos)
P(good good good lousy cheat)
12. What do we know?
P(Pos) = 3/4
P(good|Pos), P(cheat|Pos), P(lousy|Pos)
Are all easily computed by counting using the
training set.
Which is almost what we want ...
13. Wouldn't it be nice ...
Maybe we have all we need? Isn't
P(good good good lousy cheat|Pos) =
P(good|Pos)3P(lousy|Pos)P(cheat|Pos) ?
Well, yes, if these are independent events,
which almost certainly doesn't hold.
The “naive” assumption is that we can
consider these events independent.
14. The Naive Bayes Algorithm
If C1,C2,...,Cn are classes, and an instance has
features F1,F2,...,Fm, then the most likely class
for this instance is the one that maximizes the
following:
P(Ci )P(F1|Ci )P(F2|Ci )...P(Fm|Ci )
15. Wasn't there a denominator?
If our goal was to compute the probability of
the most likely class, we should divide by:
P(F1)P(F2)...P(Fm)
We can ignore this part because, we only care
about which class has the highest probability,
and this term is the same for each class.
16. Interesting theory but …
Won't this break as soon as we encounter a
word that isn't in our training set?
For example, if “goood” is not in our training
set, and occurs in our test set, then since
P(Pos|goood) = 0, so our product is zero for all
classes.
We need nonzero probabilities for all words,
even words that don't exist.
17. Plus-one smoothing
Just count every word one time more than it
actually occurs.
Since we are only concerned with relative
probabilities, this inaccuracy should be of no
concern.
P(word|C) = count(word|C) + 1
count(C) + V
(V is the total vocabulary, so that our
probabilities sum to 1.)
18. Let's try it out:
P(Pos) = ¾ Type Text Class
P(Neg) = ¼ Training Good happy good Positive
Good good service Positive
Good friendly Positive
Lousy good cheat Negative
Test Good good good cheat lousy ??
P(good|Pos) = (5+1)/(8+6) = 3/7
P(cheat|Pos) = (0+1)/(8+6) = 1/14 P(Pos|D5) ~ ¾ * (3/7)3*(1/14)*(1/14)
P(lousy|Pos) = (0+1)/(8+6) = 1/14 = 0.0003
P(good|Neg) = (1+1)/(3+6) = 2/9 P(Neg|D5) ~ ¼ * (2/9)3*(2/9)*(2/9)
P(cheat|Neg) = (1+1)/(3+6) = 2/9 = 0.0001
P(lousy|Neg) = (1+1)/(3+6) = 2/9
19. Training the classifier
● Count instances of classes, store counts in a map.
● Store counts of all words in a nested map:
{'pos':
{'good': 5, 'friendly': 1, 'service': 1, 'happy': 1},
'neg':
{'cheat': 1, 'lousy': 1, 'good': 1}
}
● Should be easy to compute probabilities.
● Should be efficient (training time and memory.)
21. Tokenization
● Use whitespace?
– “food”, “food.”, food,” and “food!” all different.
● Use whitespace and punctuation?
– “won't” tokenized to “won” and “t”
● What about emails? Urls? Phone numbers?
What about the things we haven't thought
about yet?
● Use a library. Lucene is a good choice.
22. Arithmetic
What happens when you multiply a large
amount of small numbers?
To prevent underflow, use sums of logs instead
of products of true probabilities.
Key properties of log:
● log(AB) = log(A) + log(B)
● x > y => log(x) > log(y)
● Turns very small numbers into managable negative
numbers
23. Evaluating a classifier
● Precision and recall
● Confusion matrix
● Divide training set into nine “folds”, train
classifier on nine folds, and check accuracy of
classifying the tenth fold
24. Experiment
● Tokenization strategies
– Stop words
– Capitalization
– Stemming
● Language model
– Ignore multiplicities
– Smoothing