Machine learning is a branch of computer science that deals with systems that can learn and improve automatically through experience. The document discusses key machine learning concepts like overfitting, cross-validation techniques to avoid overfitting, supervised vs unsupervised learning, and popular machine learning algorithms like decision trees, neural networks, and support vector machines. It also covers ensemble methods, dimensionality reduction techniques, and applications of machine learning in areas like computer vision and natural language processing.
Overview of Machine learning concepts – Over fitting and train/test splits, Types of Machine learning – Supervised, Unsupervised, Reinforced learning, Introduction to Bayes Theorem, Linear Regression- model assumptions, regularization (lasso, ridge, elastic net), Classification and Regression algorithms- Naïve Bayes, K-Nearest Neighbors, logistic regression, support vector machines (SVM), decision trees, and random forest, Classification Errors..
Top 10 Most Important Interview Question and Answer on Machine LearningDucatNoida1
Top 10 Most Important Interview Question and Answer on Machine Learning.
It will be very helpful for you to get knowledge and help to get job in MNC.
If You want to learn the advance Machine Learning course then you can join our online as well as offline classes from industry expert trainers. For More info, call Us: 70-70-90-50-90
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Overview of Machine learning concepts – Over fitting and train/test splits, Types of Machine learning – Supervised, Unsupervised, Reinforced learning, Introduction to Bayes Theorem, Linear Regression- model assumptions, regularization (lasso, ridge, elastic net), Classification and Regression algorithms- Naïve Bayes, K-Nearest Neighbors, logistic regression, support vector machines (SVM), decision trees, and random forest, Classification Errors..
Top 10 Most Important Interview Question and Answer on Machine LearningDucatNoida1
Top 10 Most Important Interview Question and Answer on Machine Learning.
It will be very helpful for you to get knowledge and help to get job in MNC.
If You want to learn the advance Machine Learning course then you can join our online as well as offline classes from industry expert trainers. For More info, call Us: 70-70-90-50-90
This paper presents a review & performs a comparative evaluation of few known machine learning
algorithms in terms of their suitability & code performance on any given data set of any size. In this paper,
we describe our Machine Learning ToolBox that we have built using python programming language. The
algorithms used in the toolbox consists of supervised classification algorithms such as Naïve Bayes,
Decision Trees, SVM, K-nearest Neighbors and Neural Network (Backpropagation). The algorithms are
tested on iris and diabetes dataset and are compared on the basis of their accuracy under different
conditions. However using our tool one can apply any of the implemented ML algorithms on any dataset of
any size. The main goal of building a toolbox is to provide users with a platform to test their datasets on
different Machine Learning algorithms and use the accuracy results to determine which algorithms fits the
data best. The toolbox allows the user to choose a dataset of his/her choice either in structured or
unstructured form and then can choose the features he/she wants to use for training the machine We have
given our concluding remarks on the performance of implemented algorithms based on experimental
analysis
Machine learning is a technology design to build intelligent systems. These systems also have the ability to learn from past experience or analyze historical data. It provides results according to its experience.
Alpavdin defines Machine Learning as-
“Optimizing a performance criterion using example data and past experience”.
Data is the key concept of machine learning. We can also apply its algorithms on data to identify hidden patterns and gain insights. These patterns and gained knowledge help systems to learn and improve their performance.
Machine learning technology involves both statistics and computer science. Statistics allows one to draw inferences from the given data. To implement efficient algorithms we can also use computer science. It represents the required model, and evaluate the performance of the model.
Machine Learning Interview Questions and AnswersSatyam Jaiswal
Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Top 20 Data Science Interview Questions and Answers in 2023.pdfAnanthReddy38
Here are the top 20 data science interview questions along with their answers:
What is data science?
Data science is an interdisciplinary field that involves extracting insights and knowledge from data using various scientific methods, algorithms, and tools.
What are the different steps involved in the data science process?
The data science process typically involves the following steps:
a. Problem formulation
b. Data collection
c. Data cleaning and preprocessing
d. Exploratory data analysis
e. Feature engineering
f. Model selection and training
g. Model evaluation and validation
h. Deployment and monitoring
What is the difference between supervised and unsupervised learning?
Supervised learning involves training a model on labeled data, where the target variable is known, to make predictions or classify new instances. Unsupervised learning, on the other hand, deals with unlabeled data and aims to discover patterns, relationships, or structures within the data.
What is overfitting, and how can it be prevented?
Overfitting occurs when a model learns the training data too well, resulting in poor generalization to new, unseen data. To prevent overfitting, techniques like cross-validation, regularization, and early stopping can be employed.
What is feature engineering?
Feature engineering involves creating new features from the existing data that can improve the performance of machine learning models. It includes techniques like feature extraction, transformation, scaling, and selection.
Explain the concept of cross-validation.
Cross-validation is a resampling technique used to assess the performance of a model on unseen data. It involves partitioning the available data into multiple subsets, training the model on some subsets, and evaluating it on the remaining subset. Common types of cross-validation include k-fold cross-validation and holdout validation.
What is the purpose of regularization in machine learning?
Regularization is used to prevent overfitting by adding a penalty term to the loss function during model training. It discourages complex models and promotes simpler ones, ultimately improving generalization performance.
What is the difference between precision and recall?
Precision is the ratio of true positives to the total predicted positives, while recall is the ratio of true positives to the total actual positives. Precision measures the accuracy of positive predictions, whereas recall measures the coverage of positive instances.
Explain the term “bias-variance tradeoff.”
The bias-variance tradeoff refers to the relationship between a model’s bias (error due to oversimplification) and variance (error due to sensitivity to fluctuations in the training data). Increasing model complexity reduces bias but increases variance, and vice versa. The goal is to find the right balance that minimizes overall error.
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make a futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Overview of Supervised Machine Learning Paradigms and their ClassifiersIJAEMSJORNAL
Artificial Intelligence (AI) is the theory and development of computer systems capable of performing complex tasks that historically requires human intelligence such as recognizing speech, making decisions and identifying patterns. These tasks cannot be accomplished without the ability of the systems to learn. Machine learning is the ability of machines to learn from their past experiences. Just like humans, when machines learn under supervision, it is termed supervised learning. In this work, an in-depth knowledge on machine learning was expounded. Relevant literatures were reviewed with the aim of presenting the different types of supervised machine learning paradigms, their categories and classifiers.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Few Useful Things to Know about Machine Learningnep_test_account
Machine learning algorithms can figure out how to perform
important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming
is not. As more data becomes available, more ambitious
problems can be tackled. As a result, machine learning is
widely used in computer science and other fields. However,
developing successful machine learning applications requires
a substantial amount of “black art” that is hard to find in
textbooks. This article summarizes twelve key lessons that
machine learning researchers and practitioners have learned.
These include pitfalls to avoid, important issues to focus on,
and answers to common questions.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Top 20 Data Science Interview Questions and Answers in 2023.pdfAnanthReddy38
Here are the top 20 data science interview questions along with their answers:
What is data science?
Data science is an interdisciplinary field that involves extracting insights and knowledge from data using various scientific methods, algorithms, and tools.
What are the different steps involved in the data science process?
The data science process typically involves the following steps:
a. Problem formulation
b. Data collection
c. Data cleaning and preprocessing
d. Exploratory data analysis
e. Feature engineering
f. Model selection and training
g. Model evaluation and validation
h. Deployment and monitoring
What is the difference between supervised and unsupervised learning?
Supervised learning involves training a model on labeled data, where the target variable is known, to make predictions or classify new instances. Unsupervised learning, on the other hand, deals with unlabeled data and aims to discover patterns, relationships, or structures within the data.
What is overfitting, and how can it be prevented?
Overfitting occurs when a model learns the training data too well, resulting in poor generalization to new, unseen data. To prevent overfitting, techniques like cross-validation, regularization, and early stopping can be employed.
What is feature engineering?
Feature engineering involves creating new features from the existing data that can improve the performance of machine learning models. It includes techniques like feature extraction, transformation, scaling, and selection.
Explain the concept of cross-validation.
Cross-validation is a resampling technique used to assess the performance of a model on unseen data. It involves partitioning the available data into multiple subsets, training the model on some subsets, and evaluating it on the remaining subset. Common types of cross-validation include k-fold cross-validation and holdout validation.
What is the purpose of regularization in machine learning?
Regularization is used to prevent overfitting by adding a penalty term to the loss function during model training. It discourages complex models and promotes simpler ones, ultimately improving generalization performance.
What is the difference between precision and recall?
Precision is the ratio of true positives to the total predicted positives, while recall is the ratio of true positives to the total actual positives. Precision measures the accuracy of positive predictions, whereas recall measures the coverage of positive instances.
Explain the term “bias-variance tradeoff.”
The bias-variance tradeoff refers to the relationship between a model’s bias (error due to oversimplification) and variance (error due to sensitivity to fluctuations in the training data). Increasing model complexity reduces bias but increases variance, and vice versa. The goal is to find the right balance that minimizes overall error.
In a world of data explosion, the rate of data generation and consumption is on the increasing side, there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection,
but making ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make a futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
Machine learning(ML) is the scientific study of algorithms and statistical models that computer systems used to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “Training Data", in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in the applications of email filtering, detection of network intruders and computer vision, where it is infeasible to develop an algorithm of specific instructions for performing the task. Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a field of study within machine learning and focuses on exploratory data analysis through unsupervised learning. In its application across business problems, Machine learning is the study of computer systems that learn from data and experience. It is applied in an incredibly wide variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need to make sense of data is a potential customer of machine learning.
In a world of data explosion, the rate of data generation and consumption is on the increasing side,
there comes the buzzword - Big Data.
Big Data is the concept of fast-moving, large-volume data in varying dimensions (sources) and
highly unpredicted sources.
The 4Vs of Big Data
● Volume - Scale of Data
● Velocity - Analysis of Streaming Data
● Variety - Different forms of Data
● Veracity - Uncertainty of Data
With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
A blend of both gives a robust analysis of data for the past, now and the future.
There is a thin line between data analytics and Machine learning which becomes very obvious
when you dig deep.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
An Overview of Supervised Machine Learning Paradigms and their ClassifiersIJAEMSJORNAL
Artificial Intelligence (AI) is the theory and development of computer systems capable of performing complex tasks that historically requires human intelligence such as recognizing speech, making decisions and identifying patterns. These tasks cannot be accomplished without the ability of the systems to learn. Machine learning is the ability of machines to learn from their past experiences. Just like humans, when machines learn under supervision, it is termed supervised learning. In this work, an in-depth knowledge on machine learning was expounded. Relevant literatures were reviewed with the aim of presenting the different types of supervised machine learning paradigms, their categories and classifiers.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
A Few Useful Things to Know about Machine Learningnep_test_account
Machine learning algorithms can figure out how to perform
important tasks by generalizing from examples. This is often feasible and cost-effective where manual programming
is not. As more data becomes available, more ambitious
problems can be tackled. As a result, machine learning is
widely used in computer science and other fields. However,
developing successful machine learning applications requires
a substantial amount of “black art” that is hard to find in
textbooks. This article summarizes twelve key lessons that
machine learning researchers and practitioners have learned.
These include pitfalls to avoid, important issues to focus on,
and answers to common questions.
This slide gives brief overview of supervised, unsupervised and reinforcement learning. Algorithms discussed are Naive Bayes, K nearest neighbour, SVM,decision tree, Markov model.
Difference between regression and classification. difference between supervised and reinforcement, iterative functioning of Markov model and machine learning applications.
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Transcript: Selling digital books in 2024: Insights from industry leaders - T...BookNet Canada
The publishing industry has been selling digital audiobooks and ebooks for over a decade and has found its groove. What’s changed? What has stayed the same? Where do we go from here? Join a group of leading sales peers from across the industry for a conversation about the lessons learned since the popularization of digital books, best practices, digital book supply chain management, and more.
Link to video recording: https://bnctechforum.ca/sessions/selling-digital-books-in-2024-insights-from-industry-leaders/
Presented by BookNet Canada on May 28, 2024, with support from the Department of Canadian Heritage.
Encryption in Microsoft 365 - ExpertsLive Netherlands 2024Albert Hoitingh
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.
PHP Frameworks: I want to break free (IPC Berlin 2024)Ralf Eggert
In this presentation, we examine the challenges and limitations of relying too heavily on PHP frameworks in web development. We discuss the history of PHP and its frameworks to understand how this dependence has evolved. The focus will be on providing concrete tips and strategies to reduce reliance on these frameworks, based on real-world examples and practical considerations. The goal is to equip developers with the skills and knowledge to create more flexible and future-proof web applications. We'll explore the importance of maintaining autonomy in a rapidly changing tech landscape and how to make informed decisions in PHP development.
This talk is aimed at encouraging a more independent approach to using PHP frameworks, moving towards a more flexible and future-proof approach to PHP development.
UiPath Test Automation using UiPath Test Suite series, part 4DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 4. In this session, we will cover Test Manager overview along with SAP heatmap.
The UiPath Test Manager overview with SAP heatmap webinar offers a concise yet comprehensive exploration of the role of a Test Manager within SAP environments, coupled with the utilization of heatmaps for effective testing strategies.
Participants will gain insights into the responsibilities, challenges, and best practices associated with test management in SAP projects. Additionally, the webinar delves into the significance of heatmaps as a visual aid for identifying testing priorities, areas of risk, and resource allocation within SAP landscapes. Through this session, attendees can expect to enhance their understanding of test management principles while learning practical approaches to optimize testing processes in SAP environments using heatmap visualization techniques
What will you get from this session?
1. Insights into SAP testing best practices
2. Heatmap utilization for testing
3. Optimization of testing processes
4. Demo
Topics covered:
Execution from the test manager
Orchestrator execution result
Defect reporting
SAP heatmap example with demo
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
SAP Sapphire 2024 - ASUG301 building better apps with SAP Fiori.pdfPeter Spielvogel
Building better applications for business users with SAP Fiori.
• What is SAP Fiori and why it matters to you
• How a better user experience drives measurable business benefits
• How to get started with SAP Fiori today
• How SAP Fiori elements accelerates application development
• How SAP Build Code includes SAP Fiori tools and other generative artificial intelligence capabilities
• How SAP Fiori paves the way for using AI in SAP apps
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using Deplo...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
GenAISummit 2024 May 28 Sri Ambati Keynote: AGI Belongs to The Community in O...
Top 50 ML Ques & Ans.pdf
1. https://career.guru99.com/
Top 50 Machine Learning Interview
Questions & Answers
1) What is Machine learning?
Machine learning is a branch of computer science which deals with system programming in order to
automatically learn and improve with experience. For example: Robots are programed so that they
can perform the task based on data they gather from sensors. It automatically learns programs from
data.
2) Mention the difference between Data Mining and Machine learning?
Machine learning relates with the study, design and development of the algorithms that give
computers the capability to learn without being explicitly programmed. While, data mining can be
defined as the process in which the unstructured data tries to extract knowledge or unknown
interesting patterns. During this process machine, learning algorithms are used.
3) What is ‘Overfitting’ in Machine learning?
In machine learning, when a statistical model describes random error or noise instead of underlying
relationship ‘overfitting’ occurs. When a model is excessively complex, overfitting is normally
observed, because of having too many parameters with respect to the number of training data types.
The model exhibits poor performance which has been overfit.
4) Why overfitting happens?
The possibility of overfitting exists as the criteria used for training the model is not the same as the
criteria used to judge the efficacy of a model.
5) How can you avoid overfitting ?
By using a lot of data overfitting can be avoided, overfitting happens relatively as you have a small
dataset, and you try to learn from it. But if you have a small database and you are forced to come
with a model based on that. In such situation, you can use a technique known as cross validation. In
this method the dataset splits into two section, testing and training datasets, the testing dataset will
only test the model while, in training dataset, the datapoints will come up with the model.
In this technique, a model is usually given a dataset of a known data on which training (training data
set) is run and a dataset of unknown data against which the model is tested. The idea of cross
validation is to define a dataset to “test” the model in the training phase.
6) What is inductive machine learning?
The inductive machine learning involves the process of learning by examples, where a system, from a
set of observed instances tries to induce a general rule.
2. 7) What are the five popular algorithms of Machine Learning?
a) Decision Trees
b) Neural Networks (back propagation)
c) Probabilistic networks
d) Nearest Neighbor
e) Support vector machines
8) What are the different Algorithm techniques in Machine Learning?
The different types of techniques in Machine Learning are
a) Supervised Learning
b) Unsupervised Learning
c) Semi-supervised Learning
d) Reinforcement Learning
e) Transduction
f) Learning to Learn
9) What are the three stages to build the hypotheses or model in machine learning?
a) Model building
b) Model testing
c) Applying the model
10) What is the standard approach to supervised learning?
The standard approach to supervised learning is to split the set of example into the training set and
the test.
11) What is ‘Training set’ and ‘Test set’?
In various areas of information science like machine learning, a set of data is used to discover the
potentially predictive relationship known as ‘Training Set’. Training set is an examples given to the
learner, while Test set is used to test the accuracy of the hypotheses generated by the learner, and it
is the set of example held back from the learner. Training set are distinct from Test set.
12) List down various approaches for machine learning?
The different approaches in Machine Learning are
a) Concept Vs Classification Learning
b) Symbolic Vs Statistical Learning
3. c) Inductive Vs Analytical Learning
13) What is not Machine Learning?
a) Artificial Intelligence
b) Rule based inference
14) Explain what is the function of ‘Unsupervised Learning’?
a) Find clusters of the data
b) Find low-dimensional representations of the data
c) Find interesting directions in data
d) Interesting coordinates and correlations
e) Find novel observations/ database cleaning
15) Explain what is the function of ‘Supervised Learning’?
a) Classifications
b) Speech recognition
c) Regression
d) Predict time series
e) Annotate strings
16) What is algorithm independent machine learning?
Machine learning in where mathematical foundations is independent of any particular classifier or
learning algorithm is referred as algorithm independent machine learning?
17) What is the difference between artificial learning and machine learning?
Designing and developing algorithms according to the behaviours based on empirical data are known
as Machine Learning. While artificial intelligence in addition to machine learning, it also covers other
aspects like knowledge representation, natural language processing, planning, robotics etc.
18) What is classifier in machine learning?
A classifier in a Machine Learning is a system that inputs a vector of discrete or continuous feature
values and outputs a single discrete value, the class.
19) What are the advantages of Naive Bayes?
In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so
you need less training data. The main advantage is that it can’t learn interactions between features.
20) In what areas Pattern Recognition is used?
Pattern Recognition can be used in
4. a) Computer Vision
b) Speech Recognition
c) Data Mining
d) Statistics
e) Informal Retrieval
f) Bio-Informatics
21) What is Genetic Programming?
Genetic programming is one of the two techniques used in machine learning. The model is based on
the testing and selecting the best choice among a set of results.
22) What is Inductive Logic Programming in Machine Learning?
Inductive Logic Programming (ILP) is a subfield of machine learning which uses logical programming
representing background knowledge and examples.
23) What is Model Selection in Machine Learning?
The process of selecting models among different mathematical models, which are used to describe
the same data set is known as Model Selection. Model selection is applied to the fields of statistics,
machine learning and data mining.
24) What are the two methods used for the calibration in Supervised Learning?
The two methods used for predicting good probabilities in Supervised Learning are
a) Platt Calibration
b) Isotonic Regression
These methods are designed for binary classification, and it is not trivial.
25) Which method is frequently used to prevent overfitting?
When there is sufficient data ‘Isotonic Regression’ is used to prevent an overfitting issue.
26) What is the difference between heuristic for rule learning and heuristics for decision
trees?
The difference is that the heuristics for decision trees evaluate the average quality of a number of
disjointed sets while rule learners only evaluate the quality of the set of instances that is covered with
the candidate rule.
27) What is Perceptron in Machine Learning?
In Machine Learning, Perceptron is an algorithm for supervised classification of the input into one of
several possible non-binary outputs.
28) Explain the two components of Bayesian logic program?
5. Bayesian logic program consists of two components. The first component is a logical one ; it consists
of a set of Bayesian Clauses, which captures the qualitative structure of the domain. The second
component is a quantitative one, it encodes the quantitative information about the domain.
29) What are Bayesian Networks (BN) ?
Bayesian Network is used to represent the graphical model for probability relationship among a set of
variables .
30) Why instance based learning algorithm sometimes referred as Lazy learning
algorithm?
Instance based learning algorithm is also referred as Lazy learning algorithm as they delay the
induction or generalization process until classification is performed.
31) What are the two classification methods that SVM ( Support Vector Machine) can
handle?
a) Combining binary classifiers
b) Modifying binary to incorporate multiclass learning
32) What is ensemble learning?
To solve a particular computational program, multiple models such as classifiers or experts are
strategically generated and combined. This process is known as ensemble learning.
33) Why ensemble learning is used?
Ensemble learning is used to improve the classification, prediction, function approximation etc of a
model.
34) When to use ensemble learning?
Ensemble learning is used when you build component classifiers that are more accurate and
independent from each other.
35) What are the two paradigms of ensemble methods?
The two paradigms of ensemble methods are
a) Sequential ensemble methods
b) Parallel ensemble methods
36) What is the general principle of an ensemble method and what is bagging and
boosting in ensemble method?
The general principle of an ensemble method is to combine the predictions of several models built
with a given learning algorithm in order to improve robustness over a single model. Bagging is a
method in ensemble for improving unstable estimation or classification schemes. While boosting
method are used sequentially to reduce the bias of the combined model. Boosting and Bagging both
can reduce errors by reducing the variance term.
37) What is bias-variance decomposition of classification error in ensemble method?
6. The expected error of a learning algorithm can be decomposed into bias and variance. A bias term
measures how closely the average classifier produced by the learning algorithm matches the target
function. The variance term measures how much the learning algorithm’s prediction fluctuates for
different training sets.
38) What is an Incremental Learning algorithm in ensemble?
Incremental learning method is the ability of an algorithm to learn from new data that may be
available after classifier has already been generated from already available dataset.
39) What is PCA, KPCA and ICA used for?
PCA (Principal Components Analysis), KPCA ( Kernel based Principal Component Analysis) and ICA (
Independent Component Analysis) are important feature extraction techniques used for
dimensionality reduction.
40) What is dimension reduction in Machine Learning?
In Machine Learning and statistics, dimension reduction is the process of reducing the number of
random variables under considerations and can be divided into feature selection and feature
extraction
41) What are support vector machines?
Support vector machines are supervised learning algorithms used for classification and regression
analysis.
42) What are the components of relational evaluation techniques?
The important components of relational evaluation techniques are
a) Data Acquisition
b) Ground Truth Acquisition
c) Cross Validation Technique
d) Query Type
e) Scoring Metric
f) Significance Test
43) What are the different methods for Sequential Supervised Learning?
The different methods to solve Sequential Supervised Learning problems are
a) Sliding-window methods
b) Recurrent sliding windows
c) Hidden Markow models
d) Maximum entropy Markow models
e) Conditional random fields
7. f) Graph transformer networks
44) What are the areas in robotics and information processing where sequential
prediction problem arises?
The areas in robotics and information processing where sequential prediction problem arises are
a) Imitation Learning
b) Structured prediction
c) Model based reinforcement learning
45) What is batch statistical learning?
Statistical learning techniques allow learning a function or predictor from a set of observed data that
can make predictions about unseen or future data. These techniques provide guarantees on the
performance of the learned predictor on the future unseen data based on a statistical assumption on
the data generating process.
46) What is PAC Learning?
PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to
analyze learning algorithms and their statistical efficiency.
47) What are the different categories you can categorized the sequence learning
process?
a) Sequence prediction
b) Sequence generation
c) Sequence recognition
d) Sequential decision
48) What is sequence learning?
Sequence learning is a method of teaching and learning in a logical manner.
49) What are two techniques of Machine Learning ?
The two techniques of Machine Learning are
a) Genetic Programming
b) Inductive Learning
50) Give a popular application of machine learning that you see on day to day basis?
The recommendation engine implemented by major ecommerce websites uses Machine Learning
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