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The document discusses support vector machines (SVM), a supervised machine learning algorithm. SVM can be used for both classification and regression problems by finding a hyperplane that separates classes with the maximum margin. It explains key SVM concepts like support vectors, kernels, hyperparameters like gamma and C, and evaluation techniques like cross-validation. Applications mentioned include text categorization, bioinformatics, face recognition and image classification.

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Machine Learning Course | Edureka

The document describes a machine learning certification training offered by Edureka. It covers topics like introduction to data science, machine learning applications, types of machine learning including supervised, unsupervised and reinforcement learning. For supervised learning, it discusses algorithms like linear regression, logistic regression, decision trees, random forest and Naive Bayes classifier. It also explains machine learning life cycle and concepts like model fitting, clustering and applications of machine learning.

Machine Learning and Real-World Applications

This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.

KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...

This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
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.
Learn more at: https://www.simplilearn.com

Support vector machine

Support Vactor Machine
Linerar Separable data
Non-Linear Separable data
Application
Advantage of SVM
DisAdvantage sOf SVM

What is Machine Learning | Introduction to Machine Learning | Machine Learnin...

This presentation on Introduction to Machine Learning will explain what is Machine Learning and how does Machine Learning works. By the end of this presentation, you will be able to understand what are the types of Machine Learning, Machine Learning algorithms and some of the breakthroughs in Machine Learning industry. You will also learn what Machine Learning has to offer to us in terms of career opportunities.
This Machine Learning presentation will cover the following topics:
1. Real life applications of Machine Learning
2. Machine Learning Challenges
3. How did Machine Learning evolve?
4. Why Machine Learning / Machine Learning benefits
5. What is Machine Learning?
6. Types of Machine Learning ( Supervised, Unsupervised & Reinforcement Learning )
7. Machine Learning algorithms
8. Breakthroughs in Machine Learning
9. Machine Learning Future
10. Machine Learning Career
11. Machine Learning job trends
- - - - - - - -
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
- - - - - - -

Lecture1 introduction to machine learning

Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.

Naive bayes

This document discusses Naive Bayes classifiers. It begins with an overview of probabilistic classification and the Naive Bayes approach. The Naive Bayes classifier makes a strong independence assumption that features are conditionally independent given the class. It then presents the algorithm for Naive Bayes classification with discrete and continuous features. An example of classifying whether to play tennis is used to illustrate the learning and classification phases. The document concludes with a discussion of some relevant issues and a high-level summary of Naive Bayes.

Support Vector machine

Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression analysis. It works by finding a hyperplane in an N-dimensional space that distinctly classifies the data points. SVM selects the hyperplane that has the largest distance to the nearest training data points of any class, since larger the margin lower the generalization error of the classifier. SVM can efficiently perform nonlinear classification by implicitly mapping their inputs into high-dimensional feature spaces.

Machine Learning Course | Edureka

The document describes a machine learning certification training offered by Edureka. It covers topics like introduction to data science, machine learning applications, types of machine learning including supervised, unsupervised and reinforcement learning. For supervised learning, it discusses algorithms like linear regression, logistic regression, decision trees, random forest and Naive Bayes classifier. It also explains machine learning life cycle and concepts like model fitting, clustering and applications of machine learning.

Machine Learning and Real-World Applications

This presentation was created by Ajay, Machine Learning Scientist at MachinePulse, to present at a Meetup on Jan. 30, 2015. These slides provide an overview of widely used machine learning algorithms. The slides conclude with examples of real world applications.
Ajay Ramaseshan, is a Machine Learning Scientist at MachinePulse. He holds a Bachelors degree in Computer Science from NITK, Suratkhal and a Master in Machine Learning and Data Mining from Aalto University School of Science, Finland. He has extensive experience in the machine learning domain and has dealt with various real world problems.

KNN Algorithm - How KNN Algorithm Works With Example | Data Science For Begin...

This K-Nearest Neighbor Classification Algorithm presentation (KNN Algorithm) will help you understand what is KNN, why do we need KNN, how do we choose the factor 'K', when do we use KNN, how does KNN algorithm work and you will also see a use case demo showing how to predict whether a person will have diabetes or not using KNN algorithm. KNN algorithm can be applied to both classification and regression problems. Apparently, within the Data Science industry, it's more widely used to solve classification problems. It’s a simple algorithm that stores all available cases and classifies any new cases by taking a majority vote of its k neighbors. Now lets deep dive into these slides to understand what is KNN algorithm and how does it actually works.
Below topics are explained in this K-Nearest Neighbor Classification Algorithm (KNN Algorithm) tutorial:
1. Why do we need KNN?
2. What is KNN?
3. How do we choose the factor 'K'?
4. When do we use KNN?
5. How does KNN algorithm work?
6. Use case - Predict whether a person will have diabetes or not
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.
Learn more at: https://www.simplilearn.com

Support vector machine

Support Vactor Machine
Linerar Separable data
Non-Linear Separable data
Application
Advantage of SVM
DisAdvantage sOf SVM

What is Machine Learning | Introduction to Machine Learning | Machine Learnin...

This presentation on Introduction to Machine Learning will explain what is Machine Learning and how does Machine Learning works. By the end of this presentation, you will be able to understand what are the types of Machine Learning, Machine Learning algorithms and some of the breakthroughs in Machine Learning industry. You will also learn what Machine Learning has to offer to us in terms of career opportunities.
This Machine Learning presentation will cover the following topics:
1. Real life applications of Machine Learning
2. Machine Learning Challenges
3. How did Machine Learning evolve?
4. Why Machine Learning / Machine Learning benefits
5. What is Machine Learning?
6. Types of Machine Learning ( Supervised, Unsupervised & Reinforcement Learning )
7. Machine Learning algorithms
8. Breakthroughs in Machine Learning
9. Machine Learning Future
10. Machine Learning Career
11. Machine Learning job trends
- - - - - - - -
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
- - - - - - -

Lecture1 introduction to machine learning

Machine Learning is a field of computer science which deals with the study of computer algorithms that improve automatically through experience. In this PPT we discuss the following concepts - Prerequisite, Definition, Introduction to Machine Learning (ML), Fields associated with ML, Need for ML, Difference between Artificial Intelligence, Machine Learning, Deep Learning, Types of learning in ML, Applications of ML, Limitations of Machine Learning.

Naive bayes

This document discusses Naive Bayes classifiers. It begins with an overview of probabilistic classification and the Naive Bayes approach. The Naive Bayes classifier makes a strong independence assumption that features are conditionally independent given the class. It then presents the algorithm for Naive Bayes classification with discrete and continuous features. An example of classifying whether to play tennis is used to illustrate the learning and classification phases. The document concludes with a discussion of some relevant issues and a high-level summary of Naive Bayes.

Support Vector machine

Support Vector Machine (SVM) is a supervised machine learning algorithm that can be used for both classification and regression analysis. It works by finding a hyperplane in an N-dimensional space that distinctly classifies the data points. SVM selects the hyperplane that has the largest distance to the nearest training data points of any class, since larger the margin lower the generalization error of the classifier. SVM can efficiently perform nonlinear classification by implicitly mapping their inputs into high-dimensional feature spaces.

Backpropagation And Gradient Descent In Neural Networks | Neural Network Tuto...

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

Support vector machine

This document provides an overview of support vector machines (SVM). It explains that SVM is a supervised machine learning algorithm used for classification and regression. It works by finding the optimal separating hyperplane that maximizes the margin between different classes of data points. The document discusses key SVM concepts like slack variables, kernels, hyperparameters like C and gamma, and how the kernel trick allows SVMs to fit non-linear decision boundaries.

Support vector machines (svm)

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.

Credit card fraud detection using python machine learning

COMPANY_NAME provides data-driven business transformation services using advanced analytics and artificial intelligence. It helps businesses contextualize data, generate insights from complex problems, and make data-driven decisions. The document then discusses using machine learning for credit card fraud detection. It explains supervised learning as inferring a function from labeled training and test data to map inputs to outputs with minimal error. Screenshots are provided of exploring and preprocessing a credit card transaction dataset for outlier detection, correlation, and preparing the data for machine learning models.

Introduction to Recurrent Neural Network

Basic concepts of RNN and introduction to Long short term memory network; Presented at Houston Machine Learning meetup.

Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...

This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
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
- - - - - - -

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...

** Python for Data Science: https://www.edureka.co/python **
This Edureka tutorial on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this tutorial includes:
1. What is KNN Algorithm?
2. Industrial Use case of KNN Algorithm
3. How things are predicted using KNN Algorithm
4. How to choose the value of K?
5. KNN Algorithm Using Python
6. Implementation of KNN Algorithm from scratch
Check out our playlist: http://bit.ly/2taym8X

Introduction to Transformers for NLP - Olga Petrova

Olga Petrova gives an introduction to transformers for natural language processing (NLP). She begins with an overview of representing words using tokenization, word embeddings, and one-hot encodings. Recurrent neural networks (RNNs) are discussed as they are important for modeling sequential data like text, but they struggle with long-term dependencies. Attention mechanisms were developed to address this by allowing the model to focus on relevant parts of the input. Transformers use self-attention and have achieved state-of-the-art results in many NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) provides contextualized word embeddings trained on large corpora.

Classification Based Machine Learning Algorithms

This slide focuses on working procedure of some famous classification based machine learning algorithms

Deep Learning for Natural Language Processing

This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart

Naive Bayes

- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.

K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
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...

(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.

Python Machine Learning Tutorial | Machine Learning Algorithms | Python Train...

This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Supervised Learning
5. KNN algorithm
6. Unsupervised Learning
7. K-means Clustering Algorithm

Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...

This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
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.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/

Machine Learning - Ensemble Methods

Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.

Random forest algorithm

The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.

Support Vector Machine ppt presentation

Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.

Module-3_SVM_Kernel_KNN.pptx

- Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression problems, but primarily for classification.
- The goal of SVM is to find the optimal separating hyperplane that maximizes the margin between two classes of data points.
- Support vectors are the data points that are closest to the hyperplane and influence its position. SVM aims to position the hyperplane to best separate the support vectors of different classes.

Feature selection

This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.

National Security Agency - NSA mobile device best practices

Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.

Artificial Intelligence for XMLDevelopment

In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.

Backpropagation And Gradient Descent In Neural Networks | Neural Network Tuto...

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

Support vector machine

This document provides an overview of support vector machines (SVM). It explains that SVM is a supervised machine learning algorithm used for classification and regression. It works by finding the optimal separating hyperplane that maximizes the margin between different classes of data points. The document discusses key SVM concepts like slack variables, kernels, hyperparameters like C and gamma, and how the kernel trick allows SVMs to fit non-linear decision boundaries.

Support vector machines (svm)

A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane. In other words, given labeled training data (supervised learning), the algorithm outputs an optimal hyperplane which categorizes new examples. In two dimentional space this hyperplane is a line dividing a plane in two parts where in each class lay in either side.

Credit card fraud detection using python machine learning

COMPANY_NAME provides data-driven business transformation services using advanced analytics and artificial intelligence. It helps businesses contextualize data, generate insights from complex problems, and make data-driven decisions. The document then discusses using machine learning for credit card fraud detection. It explains supervised learning as inferring a function from labeled training and test data to map inputs to outputs with minimal error. Screenshots are provided of exploring and preprocessing a credit card transaction dataset for outlier detection, correlation, and preparing the data for machine learning models.

Introduction to Recurrent Neural Network

Basic concepts of RNN and introduction to Long short term memory network; Presented at Houston Machine Learning meetup.

Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...

This Machine Learning Algorithms presentation will help you learn you what machine learning is, and the various ways in which you can use machine learning to solve a problem. At the end, you will see a demo on linear regression, logistic regression, decision tree and random forest. This Machine Learning Algorithms presentation is designed for beginners to make them understand how to implement the different Machine Learning Algorithms.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. Real world applications of Machine Learning
2. What is Machine Learning?
3. Processes involved in Machine Learning
4. Type of Machine Learning Algorithms
5. Popular Algorithms with a hands-on demo
- Linear regression
- Logistic regression
- Decision tree and Random forest
- N Nearest neighbor
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
- - - - - - -

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...

** Python for Data Science: https://www.edureka.co/python **
This Edureka tutorial on KNN Algorithm will help you to build your base by covering the theoretical, mathematical and implementation part of the KNN algorithm in Python. Topics covered under this tutorial includes:
1. What is KNN Algorithm?
2. Industrial Use case of KNN Algorithm
3. How things are predicted using KNN Algorithm
4. How to choose the value of K?
5. KNN Algorithm Using Python
6. Implementation of KNN Algorithm from scratch
Check out our playlist: http://bit.ly/2taym8X

Introduction to Transformers for NLP - Olga Petrova

Olga Petrova gives an introduction to transformers for natural language processing (NLP). She begins with an overview of representing words using tokenization, word embeddings, and one-hot encodings. Recurrent neural networks (RNNs) are discussed as they are important for modeling sequential data like text, but they struggle with long-term dependencies. Attention mechanisms were developed to address this by allowing the model to focus on relevant parts of the input. Transformers use self-attention and have achieved state-of-the-art results in many NLP tasks. Bidirectional Encoder Representations from Transformers (BERT) provides contextualized word embeddings trained on large corpora.

Classification Based Machine Learning Algorithms

This slide focuses on working procedure of some famous classification based machine learning algorithms

Deep Learning for Natural Language Processing

This talk is about how we applied deep learning techinques to achieve state-of-the-art results in various NLP tasks like sentiment analysis and aspect identification, and how we deployed these models at Flipkart

Naive Bayes

- Naive Bayes is a classification technique based on Bayes' theorem that uses "naive" independence assumptions. It is easy to build and can perform well even with large datasets.
- It works by calculating the posterior probability for each class given predictor values using the Bayes theorem and independence assumptions between predictors. The class with the highest posterior probability is predicted.
- It is commonly used for text classification, spam filtering, and sentiment analysis due to its fast performance and high success rates compared to other algorithms.

K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

This K-Means clustering algorithm presentation will take you through the machine learning introduction, types of clustering algorithms, k-means clustering, how does K-Means clustering work and at least explains K-Means clustering by taking a real life use case. This Machine Learning algorithm tutorial video is ideal for beginners to learn how K-Means clustering work.
Below topics are covered in this K-Means Clustering Algorithm presentation:
1. Types of Machine Learning?
2. What is K-Means Clustering?
3. Applications of K-Means Clustering
4. Common distance measure
5. How does K-Means Clustering work?
6. K-Means Clustering Algorithm
7. Demo: k-Means Clustering
8. Use case: Color compression
- - - - - - - -
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...

(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.

Python Machine Learning Tutorial | Machine Learning Algorithms | Python Train...

This Edureka Python tutorial (Python Tutorial Blog: https://goo.gl/wd28Zr) gives an introduction to Machine Learning and how to implement machine learning algorithms in Python. Below are the topics covered in this tutorial:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Supervised Learning
5. KNN algorithm
6. Unsupervised Learning
7. K-means Clustering Algorithm

Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...

This presentation on Machine Learning will help you understand what is clustering, K-Means clustering, flowchart to understand K-Means clustering along with demo showing clustering of cars into brands, what is logistic regression, logistic regression curve, sigmoid function and a demo on how to classify a tumor as malignant or benign based on its features. Machine Learning algorithms can help computers play chess, perform surgeries, and get smarter and more personal. K-Means & logistic regression are two widely used Machine learning algorithms which we are going to discuss in this video. Logistic Regression is used to estimate discrete values (usually binary values like 0/1) from a set of independent variables. It helps to predict the probability of an event by fitting data to a logit function. It is also called logit regression. K-means clustering is an unsupervised learning algorithm. In this case, you don't have labeled data unlike in supervised learning. You have a set of data that you want to group into and you want to put them into clusters, which means objects that are similar in nature and similar in characteristics need to be put together. This is what k-means clustering is all about. Now, let us get started and understand K-Means clustering & logistic regression in detail.
Below topics are explained in this Machine Learning tutorial part -2 :
1. Clustering
- What is clustering?
- K-Means clustering
- Flowchart to understand K-Means clustering
- Demo - Clustering of cars based on brands
2. Logistic regression
- What is logistic regression?
- Logistic regression curve & Sigmoid function
- Demo - Classify a tumor as malignant or benign based on features
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.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/

Machine Learning - Ensemble Methods

Abstract: This PDSG workshop introduces basic concepts of ensemble methods in machine learning. Concepts covered are Condercet Jury Theorem, Weak Learners, Decision Stumps, Bagging and Majority Voting.
Level: Fundamental
Requirements: No prior programming or statistics knowledge required.

Random forest algorithm

The document discusses the random forest algorithm. It introduces random forest as a supervised classification algorithm that builds multiple decision trees and merges them to provide a more accurate and stable prediction. It then provides an example pseudocode that randomly selects features to calculate the best split points to build decision trees, repeating the process to create a forest of trees. The document notes key advantages of random forest are that it avoids overfitting and can be used for both classification and regression tasks.

Support Vector Machine ppt presentation

Support vector machines (SVM) is a supervised machine learning algorithm used for both classification and regression problems. However, it is primarily used for classification. The goal of SVM is to create the best decision boundary, known as a hyperplane, that separates clusters of data points. It chooses extreme data points as support vectors to define the hyperplane. SVM is effective for problems that are not linearly separable by transforming them into higher dimensional spaces. It works well when there is a clear margin of separation between classes and is effective for high dimensional data. An example use case in Python is presented.

Module-3_SVM_Kernel_KNN.pptx

- Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression problems, but primarily for classification.
- The goal of SVM is to find the optimal separating hyperplane that maximizes the margin between two classes of data points.
- Support vectors are the data points that are closest to the hyperplane and influence its position. SVM aims to position the hyperplane to best separate the support vectors of different classes.

Feature selection

This document summarizes a machine learning workshop on feature selection. It discusses typical feature selection methods like single feature evaluation using metrics like mutual information and Gini indexing. It also covers subset selection techniques like sequential forward selection and sequential backward selection. Examples are provided showing how feature selection improves performance for logistic regression on large datasets with more features than samples. The document outlines the workshop agenda and provides details on when and why feature selection is important for machine learning models.

Backpropagation And Gradient Descent In Neural Networks | Neural Network Tuto...

Backpropagation And Gradient Descent In Neural Networks | Neural Network Tuto...

Support vector machine

Support vector machine

Support vector machines (svm)

Support vector machines (svm)

Credit card fraud detection using python machine learning

Credit card fraud detection using python machine learning

Introduction to Recurrent Neural Network

Introduction to Recurrent Neural Network

Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...

Machine Learning Algorithms | Machine Learning Tutorial | Data Science Algori...

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...

KNN Algorithm using Python | How KNN Algorithm works | Python Data Science Tr...

Introduction to Transformers for NLP - Olga Petrova

Introduction to Transformers for NLP - Olga Petrova

Classification Based Machine Learning Algorithms

Classification Based Machine Learning Algorithms

Deep Learning for Natural Language Processing

Deep Learning for Natural Language Processing

Naive Bayes

Naive Bayes

K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

K Means Clustering Algorithm | K Means Clustering Example | Machine Learning ...

Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...

Scikit Learn Tutorial | Machine Learning with Python | Python for Data Scienc...

Python Machine Learning Tutorial | Machine Learning Algorithms | Python Train...

Python Machine Learning Tutorial | Machine Learning Algorithms | Python Train...

Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...

Machine Learning Tutorial Part - 2 | Machine Learning Tutorial For Beginners ...

Machine Learning - Ensemble Methods

Machine Learning - Ensemble Methods

Random forest algorithm

Random forest algorithm

Support Vector Machine ppt presentation

Support Vector Machine ppt presentation

Module-3_SVM_Kernel_KNN.pptx

Module-3_SVM_Kernel_KNN.pptx

Feature selection

Feature selection

National Security Agency - NSA mobile device best practices

Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.

Artificial Intelligence for XMLDevelopment

In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.

Microsoft - Power Platform_G.Aspiotis.pdf

Revolutionizing Application Development
with AI-powered low-code, presentation by George Aspiotis, Sr. Partner Development Manager, Microsoft

Pushing the limits of ePRTC: 100ns holdover for 100 days

At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.

Mind map of terminologies used in context of Generative AI

Mind map of common terms used in context of Generative AI.

GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024

Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.

PCI PIN Basics Webinar from the Controlcase Team

PCI PIN Basics

GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...

Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.

“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...

“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...Edge AI and Vision Alliance

For the full video of this presentation, please visit: https://www.edge-ai-vision.com/2024/06/building-and-scaling-ai-applications-with-the-nx-ai-manager-a-presentation-from-network-optix/
Robin van Emden, Senior Director of Data Science at Network Optix, presents the “Building and Scaling AI Applications with the Nx AI Manager,” tutorial at the May 2024 Embedded Vision Summit.
In this presentation, van Emden covers the basics of scaling edge AI solutions using the Nx tool kit. He emphasizes the process of developing AI models and deploying them globally. He also showcases the conversion of AI models and the creation of effective edge AI pipelines, with a focus on pre-processing, model conversion, selecting the appropriate inference engine for the target hardware and post-processing.
van Emden shows how Nx can simplify the developer’s life and facilitate a rapid transition from concept to production-ready applications.He provides valuable insights into developing scalable and efficient edge AI solutions, with a strong focus on practical implementation.Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI

Enchancing adoption of Open Source Libraries. A case study on Albumentations.AIVladimir Iglovikov, Ph.D.

Presented by Vladimir Iglovikov:
- https://www.linkedin.com/in/iglovikov/
- https://x.com/viglovikov
- https://www.instagram.com/ternaus/
This presentation delves into the journey of Albumentations.ai, a highly successful open-source library for data augmentation.
Created out of a necessity for superior performance in Kaggle competitions, Albumentations has grown to become a widely used tool among data scientists and machine learning practitioners.
This case study covers various aspects, including:
People: The contributors and community that have supported Albumentations.
Metrics: The success indicators such as downloads, daily active users, GitHub stars, and financial contributions.
Challenges: The hurdles in monetizing open-source projects and measuring user engagement.
Development Practices: Best practices for creating, maintaining, and scaling open-source libraries, including code hygiene, CI/CD, and fast iteration.
Community Building: Strategies for making adoption easy, iterating quickly, and fostering a vibrant, engaged community.
Marketing: Both online and offline marketing tactics, focusing on real, impactful interactions and collaborations.
Mental Health: Maintaining balance and not feeling pressured by user demands.
Key insights include the importance of automation, making the adoption process seamless, and leveraging offline interactions for marketing. The presentation also emphasizes the need for continuous small improvements and building a friendly, inclusive community that contributes to the project's growth.
Vladimir Iglovikov brings his extensive experience as a Kaggle Grandmaster, ex-Staff ML Engineer at Lyft, sharing valuable lessons and practical advice for anyone looking to enhance the adoption of their open-source projects.
Explore more about Albumentations and join the community at:
GitHub: https://github.com/albumentations-team/albumentations
Website: https://albumentations.ai/
LinkedIn: https://www.linkedin.com/company/100504475
Twitter: https://x.com/albumentationsLarge Language Model (LLM) and it’s Geospatial Applications

Large Language Model (LLM) and it’s Geospatial Applications.

GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024

Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

In this session I delve into the encryption technology used in Microsoft 365 and Microsoft Purview. Including the concepts of Customer Key and Double Key Encryption.

Presentation of the OECD Artificial Intelligence Review of Germany

Consult the full report at https://www.oecd.org/digital/oecd-artificial-intelligence-review-of-germany-609808d6-en.htm

Full-RAG: A modern architecture for hyper-personalization

Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.

Introduction to CHERI technology - Cybersecurity

Introduction to CHERI technology

RESUME BUILDER APPLICATION Project for students

A mini project idea for students

Communications Mining Series - Zero to Hero - Session 1

This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A

Essentials of Automations: The Art of Triggers and Actions in FME

In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!

Removing Uninteresting Bytes in Software Fuzzing

Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.

National Security Agency - NSA mobile device best practices

National Security Agency - NSA mobile device best practices

Artificial Intelligence for XMLDevelopment

Artificial Intelligence for XMLDevelopment

Microsoft - Power Platform_G.Aspiotis.pdf

Microsoft - Power Platform_G.Aspiotis.pdf

Pushing the limits of ePRTC: 100ns holdover for 100 days

Pushing the limits of ePRTC: 100ns holdover for 100 days

Mind map of terminologies used in context of Generative AI

Mind map of terminologies used in context of Generative AI

GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024

GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024

PCI PIN Basics Webinar from the Controlcase Team

PCI PIN Basics Webinar from the Controlcase Team

GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...

GraphSummit Singapore | Enhancing Changi Airport Group's Passenger Experience...

“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...

“Building and Scaling AI Applications with the Nx AI Manager,” a Presentation...

Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI

Enchancing adoption of Open Source Libraries. A case study on Albumentations.AI

Large Language Model (LLM) and it’s Geospatial Applications

Large Language Model (LLM) and it’s Geospatial Applications

GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024

GraphSummit Singapore | Neo4j Product Vision & Roadmap - Q2 2024

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

Encryption in Microsoft 365 - ExpertsLive Netherlands 2024

Presentation of the OECD Artificial Intelligence Review of Germany

Presentation of the OECD Artificial Intelligence Review of Germany

Full-RAG: A modern architecture for hyper-personalization

Full-RAG: A modern architecture for hyper-personalization

Introduction to CHERI technology - Cybersecurity

Introduction to CHERI technology - Cybersecurity

RESUME BUILDER APPLICATION Project for students

RESUME BUILDER APPLICATION Project for students

Communications Mining Series - Zero to Hero - Session 1

Communications Mining Series - Zero to Hero - Session 1

Essentials of Automations: The Art of Triggers and Actions in FME

Essentials of Automations: The Art of Triggers and Actions in FME

Removing Uninteresting Bytes in Software Fuzzing

Removing Uninteresting Bytes in Software Fuzzing

- 1. MCS 7103: Machine Learning Simon Alex and Nambaale Support Vector Machines Simon Alex and Nambaale MCS 7101 October 8, 2019 1 / 28
- 2. Overview 1 Support Vector Machines Applications of SVM What is Machine Learning? What Is SVM? Features of SVM How Does SVM Work? Non-Linear SVM SVM Use Case Simon Alex and Nambaale MCS 7101 October 8, 2019 2 / 28
- 3. Applications of Support Vector Machines Text Categorization Bioinformatics Face Recognition Image Classiﬁcation Simon Alex and Nambaale MCS 7101 October 8, 2019 3 / 28
- 4. What is Machine Learning? Simon Alex and Nambaale MCS 7101 October 8, 2019 4 / 28
- 5. Supervised Learning Simon Alex and Nambaale MCS 7101 October 8, 2019 5 / 28
- 6. Unsupervised Learning Simon Alex and Nambaale MCS 7101 October 8, 2019 6 / 28
- 7. Type of Problems in Machine Learning Simon Alex and Nambaale MCS 7101 October 8, 2019 7 / 28
- 8. Where are Support Vector Machines? Simon Alex and Nambaale MCS 7101 October 8, 2019 8 / 28
- 9. What is Support Vector Machine? Support Vector Machine (SVM) is a supervised learning method used for classiﬁcation and regression. SVM separates data using a hyperplane which acts like a decision boundary between the various classes. SVM works well for classifying higher-dimensional data (lots of fea- tures). Simon Alex and Nambaale MCS 7101 October 8, 2019 9 / 28
- 10. Features of Support Vector Machine SVM is a supervised learning algorithm. It can be used for both classiﬁcation and regression problems. SVM can be used for classifying non-linear data using the kernel trick. Simon Alex and Nambaale MCS 7101 October 8, 2019 10 / 28
- 11. How Does SVM Work? Simon Alex and Nambaale MCS 7101 October 8, 2019 11 / 28
- 12. How Does SVM Work? Simon Alex and Nambaale MCS 7101 October 8, 2019 12 / 28
- 13. What is a Support Vector in SVM Simon Alex and Nambaale MCS 7101 October 8, 2019 13 / 28
- 14. What is a Support Vector in SVM Simon Alex and Nambaale MCS 7101 October 8, 2019 14 / 28
- 15. What is a Support Vector in SVM Simon Alex and Nambaale MCS 7101 October 8, 2019 15 / 28
- 16. Non-Linear Support Vector Machine Simon Alex and Nambaale MCS 7101 October 8, 2019 16 / 28
- 17. Non-Linear Support Vector Machine Simon Alex and Nambaale MCS 7101 October 8, 2019 17 / 28
- 18. Deﬁning the Separating Hyperplane Form of equation deﬁning the decision surface separating the classes is a hyperplane of the form: wT x + b = 0 Where: w is a weight vector x is an input vector b is bias Simon Alex and Nambaale MCS 7101 October 8, 2019 18 / 28
- 19. Deﬁning the Separating Hyperplane Simon Alex and Nambaale MCS 7101 October 8, 2019 19 / 28
- 20. Support Vector Classiﬁcation In practice, SVM uses SVC to classify data. Diﬀerent kernels can be used with SVC. These kernels include: linear, RBF and polynomial. Some kernels work better than others for a given dataset. Simon Alex and Nambaale MCS 7101 October 8, 2019 20 / 28
- 21. Gamma and C Gamma controls the shape of the ’peaks’ where the points are raised. The gamma parameter deﬁnes the degree of non-linearity, with low val- ues tending high linearity and high values leading to high non-linearity. Simon Alex and Nambaale MCS 7101 October 8, 2019 21 / 28
- 22. Inﬂuence of Gamma on Training Data Simon Alex and Nambaale MCS 7101 October 8, 2019 22 / 28
- 23. Gamma and C C controls the cost of misclassiﬁcation on the training data. A high C tries to minimize the misclassiﬁcation of training data leading to overﬁtting∗ and a low value tries to maintain a smooth classiﬁca- tion. ∗ Underﬁtting is where the model neither performs well in training nor testing. Simon Alex and Nambaale MCS 7101 October 8, 2019 23 / 28
- 24. Inﬂuence of C on Training Data Simon Alex and Nambaale MCS 7101 October 8, 2019 24 / 28
- 25. Cross-Validation Cross-Validation is a statistical method of evaluating and comparing learning algorithms by dividing data into two segments: one used to learn or train a model and the other used to validate the model. The basic form of cross-validation is k-fold cross-validation. Simon Alex and Nambaale MCS 7101 October 8, 2019 25 / 28
- 26. K-Fold Cross Validation Simon Alex and Nambaale MCS 7101 October 8, 2019 26 / 28
- 27. Questions? Simon Alex and Nambaale MCS 7101 October 8, 2019 27 / 28
- 28. SVM Use Case Simon Alex and Nambaale MCS 7101 October 8, 2019 28 / 28