In this Python Machine Learning Tutorial, Machine Learning also termed ML. It is a subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn by making use of statistical techniques. It deals with algorithms that can look at data to learn from it and make predictions.
Noel Moses Mwadende is a 2-year computer science and information security student in Tanzania who writes books on various cybersecurity and programming topics. He has written books on topics like WiFi hacking, malware analysis, and machine learning. He is currently employed making tutorials on these topics for the MoTech YouTube channel. This document provides biographical information about Mwadende and introduces his book on machine learning basics, which aims to provide an overview of concepts for beginners.
This document discusses CAPTCHAs, which are challenges used to distinguish humans from bots by testing patterns recognition. It begins by defining CAPTCHAs and providing background on why they were developed, such as to prevent spam. It then covers various types of CAPTCHAs, including text, image, and audio-based, as well as their applications and how they work. The document also addresses issues with CAPTCHAs, such as accessibility and usability problems, as well as methods that have been used to break existing CAPTCHAs. In conclusion, while CAPTCHAs are generally effective against bots, their implementations face challenges to be improved in terms of issues like accessibility, compatibility and security.
CAPTCHAs are automated tests used to distinguish humans from bots online. They work by generating tests that are easy for humans to pass but difficult for computers. The first CAPTCHA was created in 2000 by researchers at Carnegie Mellon University to prevent bots from generating spam email accounts. Modern CAPTCHAs come in various forms like distorted text, math problems, or image recognition tests. While CAPTCHAs aim to keep bots out, researchers continue developing techniques to circumvent them through improved image recognition software or using humans to solve the tests.
Machine learning interview questions and answerskavinilavuG
Machine learning interview questions and answers are provided. Key points include:
1) Machine learning is a form of AI that automates data analysis to enable computers to learn and adapt through experience without explicit programming.
2) Candidate sampling in machine learning involves calculating probabilities for a random sample of negative labels in addition to all positive labels, to reduce computational costs during training.
3) The difference between data mining and machine learning is that data mining extracts patterns from unstructured data, while machine learning relates to designing algorithms that allow computers to learn without being explicitly programmed.
This document provides an overview of wireless communication. It discusses key topics such as features of wireless communication including transmission distance and applications. It also outlines some advantages of wireless communication such as mobility and lack of need for wires. Finally, it introduces various terms used in mobile telephony and multiple access techniques including FDMA, TDMA, CDMA and more that allow multiple users to access the network simultaneously.
This document discusses CAPTCHAs, which are programs that generate tests to distinguish humans from bots by having users decipher distorted text or images. It describes the background and need for CAPTCHAs, and various types including text, distorted-word, and graphic-based CAPTCHAs. It also covers how CAPTCHAs work, challenges in constructing and breaking them, and issues with usability and compatibility.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
The document discusses CAPTCHAs, which are programs that can distinguish humans from computers by generating tests that are easy for humans but difficult for computers. It provides background on CAPTCHAs, describes common types including text, graphic, and audio CAPTCHAs, and discusses techniques for breaking existing CAPTCHAs. It then proposes a new approach to CAPTCHAs that uses distorted images of concrete objects from Google Images and requires users to identify the object from a list of words to address limitations in other CAPTCHAs.
Noel Moses Mwadende is a 2-year computer science and information security student in Tanzania who writes books on various cybersecurity and programming topics. He has written books on topics like WiFi hacking, malware analysis, and machine learning. He is currently employed making tutorials on these topics for the MoTech YouTube channel. This document provides biographical information about Mwadende and introduces his book on machine learning basics, which aims to provide an overview of concepts for beginners.
This document discusses CAPTCHAs, which are challenges used to distinguish humans from bots by testing patterns recognition. It begins by defining CAPTCHAs and providing background on why they were developed, such as to prevent spam. It then covers various types of CAPTCHAs, including text, image, and audio-based, as well as their applications and how they work. The document also addresses issues with CAPTCHAs, such as accessibility and usability problems, as well as methods that have been used to break existing CAPTCHAs. In conclusion, while CAPTCHAs are generally effective against bots, their implementations face challenges to be improved in terms of issues like accessibility, compatibility and security.
CAPTCHAs are automated tests used to distinguish humans from bots online. They work by generating tests that are easy for humans to pass but difficult for computers. The first CAPTCHA was created in 2000 by researchers at Carnegie Mellon University to prevent bots from generating spam email accounts. Modern CAPTCHAs come in various forms like distorted text, math problems, or image recognition tests. While CAPTCHAs aim to keep bots out, researchers continue developing techniques to circumvent them through improved image recognition software or using humans to solve the tests.
Machine learning interview questions and answerskavinilavuG
Machine learning interview questions and answers are provided. Key points include:
1) Machine learning is a form of AI that automates data analysis to enable computers to learn and adapt through experience without explicit programming.
2) Candidate sampling in machine learning involves calculating probabilities for a random sample of negative labels in addition to all positive labels, to reduce computational costs during training.
3) The difference between data mining and machine learning is that data mining extracts patterns from unstructured data, while machine learning relates to designing algorithms that allow computers to learn without being explicitly programmed.
This document provides an overview of wireless communication. It discusses key topics such as features of wireless communication including transmission distance and applications. It also outlines some advantages of wireless communication such as mobility and lack of need for wires. Finally, it introduces various terms used in mobile telephony and multiple access techniques including FDMA, TDMA, CDMA and more that allow multiple users to access the network simultaneously.
This document discusses CAPTCHAs, which are programs that generate tests to distinguish humans from bots by having users decipher distorted text or images. It describes the background and need for CAPTCHAs, and various types including text, distorted-word, and graphic-based CAPTCHAs. It also covers how CAPTCHAs work, challenges in constructing and breaking them, and issues with usability and compatibility.
This document provides an introduction to machine learning and data science. It discusses key concepts like supervised vs. unsupervised learning, classification algorithms, overfitting and underfitting data. It also addresses challenges like having bad quality or insufficient training data. Python and MATLAB are introduced as suitable software for machine learning projects.
The document discusses CAPTCHAs, which are programs that can distinguish humans from computers by generating tests that are easy for humans but difficult for computers. It provides background on CAPTCHAs, describes common types including text, graphic, and audio CAPTCHAs, and discusses techniques for breaking existing CAPTCHAs. It then proposes a new approach to CAPTCHAs that uses distorted images of concrete objects from Google Images and requires users to identify the object from a list of words to address limitations in other CAPTCHAs.
This document discusses machine learning applications and different machine learning techniques. It provides examples of common machine learning applications such as image recognition, speech recognition, traffic prediction, product recommendations, self-driving cars, email filtering, and virtual assistants. It also discusses supervised learning for classification and regression problems, unsupervised learning for exploring patterns in unlabeled data, and reinforcement learning where agents learn through trial-and-error interactions with an environment.
CAPTCHA is an acronym that stands for "Completely Automated Public Turing test to tell Computers and Humans Apart". It is a challenge-response test used to determine if a user is human. A common type of CAPTCHA requires a user to type distorted letters or numbers from an image. CAPTCHAs are designed to be easy for humans but difficult for computers to solve through optical character recognition. They are used to prevent spam and automated programs from accessing certain online services.
The document discusses various topics related to machine learning including machine learning applications like spam filtering and recommendation systems. It provides definitions and examples of different machine learning categories like supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves providing input and desired output for classification while unsupervised learning allows machines to classify without prior information. Reinforcement learning uses rewards and penalties to direct unsupervised learning through experiences.
CAPTCHAs are automated tests used to distinguish humans from computers by presenting problems that are easy for humans to solve but difficult for computers. The term was coined in 2000 by researchers at Carnegie Mellon University and stands for "Completely Automated Public Turing test to tell Computers and Humans Apart". There are different types of CAPTCHAs including visual CAPTCHAs with distorted images or text, audio CAPTCHAs with distorted spoken words, and text-based CAPTCHAs involving text identification or simple questions. CAPTCHAs are commonly used to protect online polls, web registrations, and to prevent comment spam and email spam while allowing free access to services. However, they can sometimes be difficult for users
A CAPTCHA is a type of challenge-response test used to determine if a user is human. It stands for "Completely Automated Public Turing test to tell Computers and Humans Apart." CAPTCHAs were developed to protect websites from bots by displaying distorted text images that humans can read but current optical character recognition programs cannot. Common types of CAPTCHAs include text-based challenges, audio challenges, and reCAPTCHA which uses words from digitized texts that machines have not yet learned to read perfectly.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
This document discusses CAPTCHAs (Completely Automated Public Turing tests to tell Computers & Humans Apart). It defines CAPTCHAs, provides background on why they were created, and describes different types including text, image, audio. It covers how CAPTCHAs are constructed, how some have been broken, and issues around usability, accessibility, and compatibility. The document concludes that while CAPTCHAs are effective against bots and spam, current implementations have limitations representing challenges to improve accessibility and security.
The document discusses CAPTCHAs, which are tests used to distinguish humans from computers on websites. CAPTCHAs are needed to prevent automated programs ("bots") from exploiting websites by creating fake accounts or spamming users. The document outlines the history and development of CAPTCHAs, from early methods using distorted text to current visual and audio tests. It also explains how CAPTCHAs are related to the Turing Test for determining machine intelligence and discusses different types of CAPTCHAs, including text, visual, and audio tests.
A CAPTCHA is a program that protects websites against bots by generating and grading tests that humans can pass but current computer programs cannot.
It is used, commonly, to protect your sites.
This document provides an overview of CAPTCHAs, including definitions, examples, types (text, graphic, audio), applications (preventing spam), advantages (distinguishing humans and bots), and disadvantages (difficulties for those with visual impairments). It discusses common CAPTCHAs like reCAPTCHA and MSN Passport and concludes that while CAPTCHAs are generally effective against bots, current implementations have room for improvement.
in this captcha report, you get everything those you need in a seminar. hope you like this report..
please check it out. and use it
for more report contect me. my email id is rkrakeshkumar99@gmail.com
CAPTCHA- Newly Attractive Presentation for YouthWebCrazyLabs
A CAPTCHA is a program that protects websites against bots by generating and grading tests that humans can pass but current computer programs cannot.
It is used, commonly, to protect your sites.
This document discusses CAPTCHAs, which are programs that generate tests to distinguish humans from bots attempting to access websites. It defines CAPTCHAs and provides a brief history, describing the first CAPTCHA developed by AltaVista in 1997. The document outlines different types of CAPTCHAs, including text, graphics, and audio-based, and discusses some applications and how CAPTCHAs can be broken, along with benefits and drawbacks.
This document discusses CAPTCHAs, which are challenges used to distinguish humans from computers on websites. It provides a brief history of CAPTCHAs, describing their development in 1997 by Alta Vista and formal naming in 2000. It then describes the main types of CAPTCHAs, including text-based, graphic-based, and audio-based challenges. The document outlines some examples of each type and explains the general process that CAPTCHAs use to generate and present challenges to users. It also discusses techniques for breaking older CAPTCHAs and introduces reCAPTCHA as an improved approach.
This document provides a summary of 41 essential machine learning interview questions organized into categories. It begins by explaining the importance of being prepared for machine learning interview questions and then divides the questions into sections on algorithms/theory, programming skills, industry trends, and company-specific topics. Under the algorithms/theory section, it provides 13 sample questions that test understanding of concepts like bias/variance tradeoff, supervised vs unsupervised learning, KNN vs k-means clustering, ROC curves, precision/recall, Bayes' theorem, and regularization. It includes brief explanations or references for further reading for each question.
The document discusses CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart). It provides a brief history of CAPTCHAs, describes different types including text, graphics, and audio-based CAPTCHAs. Applications of CAPTCHAs include preventing automated actions on email services and dictionary attacks. The document also covers processes used in CAPTCHAs, techniques for breaking CAPTCHAs, improvements like reCAPTCHA, potential benefits and drawbacks.
CAPTCHA stands for "Completely Automated Public Turing test to tell Computers and Humans Apart". It is an automated test used to distinguish humans from bots online. CAPTCHAs work by displaying distorted text images that are easy for humans to read but difficult for computer programs to decipher. They are useful for preventing bots from abusing internet services like spamming forums or faking online polls. While effective, CAPTCHAs are not perfect and new circumvention methods are constantly being developed, so they require ongoing improvement and alternative approaches.
This document is a seminar report on CAPTCHA security submitted by Ganesh B. Dhage to the Department of Computer Engineering at Sinhgad School of Engineering, Pune, India in partial fulfillment of requirements for a computer engineering degree. It discusses CAPTCHAs, which are tests used to distinguish humans from bots attempting to access websites. The report covers the history and motivation for CAPTCHAs, different types of CAPTCHAs, how to construct and break CAPTCHAs, issues with CAPTCHAs, and applications of CAPTCHAs.
Enhancing Web-Security with Stronger CaptchasEditor IJCATR
Captcha are used widely over the World Wide Web to prevent automated programs in order to scrape a data from
websites. Captcha is a challenge response test used to ensure that the response is generated by a person not by a computer. Users
are asked to read and type a string of distorted characters in order to ensure that the user is human or not. Automation is real
problem for web application. Automated attacks can exploit many services:
1. Blogs 2. Forums 3. Phishing 4. Theft of data
Registration Websites use CAPTCHA (completely automated public turing test to tell computers and human apart) systems to
prevent the bot programs from wasting their resources. Today is the Era of where technologies are changes very rapidly. So
spammers are hackers are also trying something new to cracking captcha. That’s why it is necessary to developing an advanced
technology to generating a captcha. Just like simply generating a Captcha Images from text, or rotating an object within images.
Linear algebra provides the tools needed for machine learning algorithms by allowing complex operations to be described using matrices and vectors. It is widely used in machine learning because operations can be parallelized efficiently. Linear algebra also provides the foundation and notation used in other fields like calculus and probability that are important for machine learning. Machine learning involves feeding training data to algorithms that produce mathematical models to make predictions without being explicitly programmed. It works by learning from experience to improve performance at tasks over time. There are various applications of machine learning like image recognition, speech recognition, recommendations, and fraud detection.
Machine learning (ML) is a type of artificial intelligence that allows software to become more accurate at predicting outcomes without being explicitly programmed. ML uses historical data as input to predict new output values. Common uses of ML include recommendation engines, fraud detection, and predictive maintenance. There are four main types of ML: supervised learning where the input and output are defined, unsupervised learning which looks for patterns in unlabeled data, semi-supervised which uses some labeled and some unlabeled data, and reinforcement learning which programs an algorithm to seek rewards and avoid punishments to accomplish a goal.
This document discusses machine learning applications and different machine learning techniques. It provides examples of common machine learning applications such as image recognition, speech recognition, traffic prediction, product recommendations, self-driving cars, email filtering, and virtual assistants. It also discusses supervised learning for classification and regression problems, unsupervised learning for exploring patterns in unlabeled data, and reinforcement learning where agents learn through trial-and-error interactions with an environment.
CAPTCHA is an acronym that stands for "Completely Automated Public Turing test to tell Computers and Humans Apart". It is a challenge-response test used to determine if a user is human. A common type of CAPTCHA requires a user to type distorted letters or numbers from an image. CAPTCHAs are designed to be easy for humans but difficult for computers to solve through optical character recognition. They are used to prevent spam and automated programs from accessing certain online services.
The document discusses various topics related to machine learning including machine learning applications like spam filtering and recommendation systems. It provides definitions and examples of different machine learning categories like supervised learning, unsupervised learning, and reinforcement learning. Supervised learning involves providing input and desired output for classification while unsupervised learning allows machines to classify without prior information. Reinforcement learning uses rewards and penalties to direct unsupervised learning through experiences.
CAPTCHAs are automated tests used to distinguish humans from computers by presenting problems that are easy for humans to solve but difficult for computers. The term was coined in 2000 by researchers at Carnegie Mellon University and stands for "Completely Automated Public Turing test to tell Computers and Humans Apart". There are different types of CAPTCHAs including visual CAPTCHAs with distorted images or text, audio CAPTCHAs with distorted spoken words, and text-based CAPTCHAs involving text identification or simple questions. CAPTCHAs are commonly used to protect online polls, web registrations, and to prevent comment spam and email spam while allowing free access to services. However, they can sometimes be difficult for users
A CAPTCHA is a type of challenge-response test used to determine if a user is human. It stands for "Completely Automated Public Turing test to tell Computers and Humans Apart." CAPTCHAs were developed to protect websites from bots by displaying distorted text images that humans can read but current optical character recognition programs cannot. Common types of CAPTCHAs include text-based challenges, audio challenges, and reCAPTCHA which uses words from digitized texts that machines have not yet learned to read perfectly.
Machine Learning jobs are one of the top emerging jobs in the industry currently, and standing out during an interview is key for landing your desired job. Here are some Machine Learning interview questions you should know about, if you plan to build a successful career in the field.
This document discusses CAPTCHAs (Completely Automated Public Turing tests to tell Computers & Humans Apart). It defines CAPTCHAs, provides background on why they were created, and describes different types including text, image, audio. It covers how CAPTCHAs are constructed, how some have been broken, and issues around usability, accessibility, and compatibility. The document concludes that while CAPTCHAs are effective against bots and spam, current implementations have limitations representing challenges to improve accessibility and security.
The document discusses CAPTCHAs, which are tests used to distinguish humans from computers on websites. CAPTCHAs are needed to prevent automated programs ("bots") from exploiting websites by creating fake accounts or spamming users. The document outlines the history and development of CAPTCHAs, from early methods using distorted text to current visual and audio tests. It also explains how CAPTCHAs are related to the Turing Test for determining machine intelligence and discusses different types of CAPTCHAs, including text, visual, and audio tests.
A CAPTCHA is a program that protects websites against bots by generating and grading tests that humans can pass but current computer programs cannot.
It is used, commonly, to protect your sites.
This document provides an overview of CAPTCHAs, including definitions, examples, types (text, graphic, audio), applications (preventing spam), advantages (distinguishing humans and bots), and disadvantages (difficulties for those with visual impairments). It discusses common CAPTCHAs like reCAPTCHA and MSN Passport and concludes that while CAPTCHAs are generally effective against bots, current implementations have room for improvement.
in this captcha report, you get everything those you need in a seminar. hope you like this report..
please check it out. and use it
for more report contect me. my email id is rkrakeshkumar99@gmail.com
CAPTCHA- Newly Attractive Presentation for YouthWebCrazyLabs
A CAPTCHA is a program that protects websites against bots by generating and grading tests that humans can pass but current computer programs cannot.
It is used, commonly, to protect your sites.
This document discusses CAPTCHAs, which are programs that generate tests to distinguish humans from bots attempting to access websites. It defines CAPTCHAs and provides a brief history, describing the first CAPTCHA developed by AltaVista in 1997. The document outlines different types of CAPTCHAs, including text, graphics, and audio-based, and discusses some applications and how CAPTCHAs can be broken, along with benefits and drawbacks.
This document discusses CAPTCHAs, which are challenges used to distinguish humans from computers on websites. It provides a brief history of CAPTCHAs, describing their development in 1997 by Alta Vista and formal naming in 2000. It then describes the main types of CAPTCHAs, including text-based, graphic-based, and audio-based challenges. The document outlines some examples of each type and explains the general process that CAPTCHAs use to generate and present challenges to users. It also discusses techniques for breaking older CAPTCHAs and introduces reCAPTCHA as an improved approach.
This document provides a summary of 41 essential machine learning interview questions organized into categories. It begins by explaining the importance of being prepared for machine learning interview questions and then divides the questions into sections on algorithms/theory, programming skills, industry trends, and company-specific topics. Under the algorithms/theory section, it provides 13 sample questions that test understanding of concepts like bias/variance tradeoff, supervised vs unsupervised learning, KNN vs k-means clustering, ROC curves, precision/recall, Bayes' theorem, and regularization. It includes brief explanations or references for further reading for each question.
The document discusses CAPTCHA (Completely Automated Public Turing Test to Tell Computers and Humans Apart). It provides a brief history of CAPTCHAs, describes different types including text, graphics, and audio-based CAPTCHAs. Applications of CAPTCHAs include preventing automated actions on email services and dictionary attacks. The document also covers processes used in CAPTCHAs, techniques for breaking CAPTCHAs, improvements like reCAPTCHA, potential benefits and drawbacks.
CAPTCHA stands for "Completely Automated Public Turing test to tell Computers and Humans Apart". It is an automated test used to distinguish humans from bots online. CAPTCHAs work by displaying distorted text images that are easy for humans to read but difficult for computer programs to decipher. They are useful for preventing bots from abusing internet services like spamming forums or faking online polls. While effective, CAPTCHAs are not perfect and new circumvention methods are constantly being developed, so they require ongoing improvement and alternative approaches.
This document is a seminar report on CAPTCHA security submitted by Ganesh B. Dhage to the Department of Computer Engineering at Sinhgad School of Engineering, Pune, India in partial fulfillment of requirements for a computer engineering degree. It discusses CAPTCHAs, which are tests used to distinguish humans from bots attempting to access websites. The report covers the history and motivation for CAPTCHAs, different types of CAPTCHAs, how to construct and break CAPTCHAs, issues with CAPTCHAs, and applications of CAPTCHAs.
Enhancing Web-Security with Stronger CaptchasEditor IJCATR
Captcha are used widely over the World Wide Web to prevent automated programs in order to scrape a data from
websites. Captcha is a challenge response test used to ensure that the response is generated by a person not by a computer. Users
are asked to read and type a string of distorted characters in order to ensure that the user is human or not. Automation is real
problem for web application. Automated attacks can exploit many services:
1. Blogs 2. Forums 3. Phishing 4. Theft of data
Registration Websites use CAPTCHA (completely automated public turing test to tell computers and human apart) systems to
prevent the bot programs from wasting their resources. Today is the Era of where technologies are changes very rapidly. So
spammers are hackers are also trying something new to cracking captcha. That’s why it is necessary to developing an advanced
technology to generating a captcha. Just like simply generating a Captcha Images from text, or rotating an object within images.
Linear algebra provides the tools needed for machine learning algorithms by allowing complex operations to be described using matrices and vectors. It is widely used in machine learning because operations can be parallelized efficiently. Linear algebra also provides the foundation and notation used in other fields like calculus and probability that are important for machine learning. Machine learning involves feeding training data to algorithms that produce mathematical models to make predictions without being explicitly programmed. It works by learning from experience to improve performance at tasks over time. There are various applications of machine learning like image recognition, speech recognition, recommendations, and fraud detection.
Machine learning (ML) is a type of artificial intelligence that allows software to become more accurate at predicting outcomes without being explicitly programmed. ML uses historical data as input to predict new output values. Common uses of ML include recommendation engines, fraud detection, and predictive maintenance. There are four main types of ML: supervised learning where the input and output are defined, unsupervised learning which looks for patterns in unlabeled data, semi-supervised which uses some labeled and some unlabeled data, and reinforcement learning which programs an algorithm to seek rewards and avoid punishments to accomplish a goal.
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.
This document provides an introduction to machine learning fundamentals. It defines machine learning as giving computers the ability to learn from data rather than being explicitly programmed. The document discusses the differences between artificial intelligence, machine learning, deep learning, and data science. It also covers applications of machine learning, when to use and not use machine learning, and types of machine learning problems and workflows.
This document discusses cognitive automation and artificial intelligence. It begins with definitions of cognition and automation. It then provides a brief history of automation and examples of current automation technologies. It discusses different types of artificial intelligence from narrow to general to super intelligence. It also discusses machine learning and deep learning approaches. The document outlines various applications of cognitive automation and artificial intelligence, as well as challenges. It emphasizes that cognitive automation will change but not eliminate jobs for humans. The presentation aims to inspire students to help build the future of cognitive automation and artificial intelligence.
What is Machine Learning4-converted.pptxvinod756504
The document provides an overview of a Python with Machine Learning internship presentation. It begins with an introduction to Python, describing its origins and features. It then discusses Python operators and flow control. The document defines machine learning and compares it to traditional programming. It outlines the main types of machine learning - supervised, unsupervised, reinforcement, and semi-supervised learning - and describes models based on algorithm outputs. Finally, it provides more detailed explanations of supervised and unsupervised learning, including their categories, computational complexity, accuracy, and provides references used in the presentation.
Machine learning applications nurturing growth of various business domainsShrutika Oswal
Machine learning is a science in which machines are becoming smarter and helping humans to make the best decisions based on previous data recommended practices. This technique is not new but is occupying fresh momentum. Machine Learning Algorithm learns from the previous records and analyses the data. Without any human interrupt, it will generate its own recommendation. A machine will add that recommendation as experience in its database and use it for further processing. In short, the machine learns from its own experience and gives you better and better output.
Machine learning is an iterative process as the more data added to machines learn from fresh feeds of data and then independently adapt new features to handle new data without constant human intervention. Machine learning was earlier used to predict what’s happing with the business but now the machine learning algorithm will suggest what action needs be taken by moving our business forward.
This PowerPoint presentation presents the results of a literature survey of machine learning applications nurturing the growth of various business domains. More specifically, it gives a brief introduction of Machine Learning, four major types of Machine Learning, enhancement in various business domains by the use of various machine learning algorithms.
The world today is evolving and so are the needs and requirements of people. Furthermore, we are witnessing a fourth industrial revolution of data.
Machine Learning has revolutionized industries like medicine, healthcare, manufacturing, banking, and several other industries. Therefore, Machine Learning has become an essential part of modern industry.
what-is-machine-learning-and-its-importance-in-todays-world.pdfTemok IT Services
Machine Learning is an AI method for teaching computers to learn from their mistakes. Machine learning algorithms can “learn” data directly from data without using an equation as a model by employing computational methods.
https://bit.ly/RightContactDataSpecialists
Ever since the companies have realized that the regular software are not going to address the growing competition and that they need something additional to pull them, concepts like Data Science and Machine Learning have started gaining momentum. Whether it is Voice Recognition based searching, Fraud Detection Systems, or a Recommendation System by Amazon or Netflix, Machine Learning has been the most implemented technology over the period of time.
1. The document discusses machine learning types including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of applications like spam filtering, recommendations, and fraud detection.
2. Key challenges in machine learning are discussed such as poor quality data, lack of training data, and imperfections when data grows.
3. The difference between data science and machine learning is explained - data science is a broader field that includes extracting insights from data using tools and models, while machine learning focuses specifically on making predictions using algorithms.
Hello guys! The ppt consists of a machine learning introduction.
What are the things we will be learning on this ppt?
1. Prerequisites before learning machine learning
- Python(programming language)
- Python libraries
2. Machine learning
3. Types of machine learning
4. Applications of Machine learning
5. Advantages of Machine learning
6. Simple Example of Machine learning
This document summarizes a 15-day practical training undertaken by Kirti Sharma from August 11-25, 2022 at Udemy on the topic of "Data Science and Machine Learning with Python Bootcamp". The training was undertaken to fulfill partial requirements for a Bachelor of Technology degree in Computer Science Engineering. The training covered topics such as Python programming, machine learning libraries and algorithms, and their applications.
Machine Learning is a subset of artificial intelligence that allows computers to learn without being explicitly programmed. It uses algorithms to recognize patterns in data and make predictions. The document discusses common machine learning algorithms like linear regression, logistic regression, decision trees, and k-means clustering. It also provides examples of machine learning applications such as face detection, speech recognition, fraud detection, and smart cars. Machine learning is expected to have an increasingly important role in the future.
While machine learning is an exciting subject, it is wrong to assume that it will solve all your problems. Scroll down to take a look at some myths in the machine learning field and how to overcome them.
Artificial Intelligence with Python | EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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This document provides an introduction to machine learning, covering various topics. It defines machine learning as a branch of artificial intelligence that uses algorithms and data to enable machines to learn. It discusses different types of machine learning, including supervised, unsupervised, and reinforcement learning. It also covers important machine learning concepts like overfitting, evaluation metrics, and well-posed learning problems. The history of machine learning is reviewed, from early work in the 1950s to recent advances in deep learning.
How to use Artificial Intelligence with Python? EdurekaEdureka!
YouTube Link: https://youtu.be/7O60HOZRLng
* Machine Learning Engineer Masters Program: https://www.edureka.co/masters-program/machine-learning-engineer-training *
This Edureka PPT on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
Follow us to never miss an update in the future.
YouTube: https://www.youtube.com/user/edurekaIN
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Machine learning is a term thrown around in technology circles with an ever-increasing intensity. Major
technology companies have attached themselves to
this buzzword to receive capital investments, and every
major technology company is pushing its even shinier
parentartificial intelligence (AI).
Artificial neural network for machine learninggrinu
An Artificial Neurol Network (ANN) is a computational model. It is based on the structure and functions of biological neural networks. It works like the way human brain processes information. ANN includes a large number of connected processing units that work together to process information. They also generate meaningful results from it.
The 21st century; oh, what a time to be alive! With the world at your fingertips, it is easier than ever to dream big. But the question is- where to begin? With a wide range of programming languages to choose from to begin with, this article isn’t a gimmick for Python. Through this piece of writing, we hope to open you up to the realities of the world of Python. We will let you know the reasons why should I learn Python programming, what are the benefits of learning Python, what can I do with Python programming language and how can I start a career in Python Programming.
Python standard library & list of important librariesgrinu
We know that a module is a file with some Python code, and a package is a directory for sub packages and modules. But the line between a package and a Python library is quite blurred.
A Python library is a reusable chunk of code that you may want to include in your programs/ projects. Compared to languages like C++ or C, a Python libraries do not pertain to any specific context in Python. Here, a ‘library’ loosely describes a collection of core modules. Essentially, then, a library is a collection of modules. A package is a library that can be installed using a package manager like rubygems or npm.
Data Mining is a set of method that applies to large and complex databases. This is to eliminate the randomness and discover the hidden pattern. As these data mining methods are almost always computationally intensive. We use data mining tools, methodologies, and theories for revealing patterns in data. There are too many driving forces present. And, this is the reason why data mining has become such an important area of study.
A Heuristic is a technique to solve a problem faster than classic methods, or to find an approximate solution when classic methods cannot. This is a kind of a shortcut as we often trade one of optimality, completeness, accuracy, or precision for speed. A Heuristic (or a heuristic function) takes a look at search algorithms. At each branching step, it evaluates the available information and makes a decision on which branch to follow.
Wee are discussing 20 best applications of deep learning with Python, that you must know. Let’s discuss them one by one:
i. Restoring Color in B&W Photos and Videos
With Deep Learning, it is possible to restore color in black and white photos and videos. This can give a new life to such media. The ACM Digital Library is one such project that colorizes grayscale images combining global priors and local image features. This is based on Convolutional Neural Networks.
The Deep Learning network learns patterns that naturally occur within photos. This includes blue skies, white and gray clouds, and the greens of grasses. It uses past experience to learn this. Although sometimes, it can make mistakes, it is efficient and accurate most of the times.
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A Visual Guide to 1 Samuel | A Tale of Two HeartsSteve Thomason
These slides walk through the story of 1 Samuel. Samuel is the last judge of Israel. The people reject God and want a king. Saul is anointed as the first king, but he is not a good king. David, the shepherd boy is anointed and Saul is envious of him. David shows honor while Saul continues to self destruct.
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Whether you're new to SEO or looking to refine your existing strategies, this webinar will provide you with actionable insights and practical tips to elevate your nonprofit's online presence.
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A Free 200-Page eBook ~ Brain and Mind Exercise.pptxOH TEIK BIN
(A Free eBook comprising 3 Sets of Presentation of a selection of Puzzles, Brain Teasers and Thinking Problems to exercise both the mind and the Right and Left Brain. To help keep the mind and brain fit and healthy. Good for both the young and old alike.
Answers are given for all the puzzles and problems.)
With Metta,
Bro. Oh Teik Bin 🙏🤓🤔🥰
How to Manage Reception Report in Odoo 17Celine George
A business may deal with both sales and purchases occasionally. They buy things from vendors and then sell them to their customers. Such dealings can be confusing at times. Because multiple clients may inquire about the same product at the same time, after purchasing those products, customers must be assigned to them. Odoo has a tool called Reception Report that can be used to complete this assignment. By enabling this, a reception report comes automatically after confirming a receipt, from which we can assign products to orders.
CapTechTalks Webinar Slides June 2024 Donovan Wright.pptxCapitolTechU
Slides from a Capitol Technology University webinar held June 20, 2024. The webinar featured Dr. Donovan Wright, presenting on the Department of Defense Digital Transformation.
2. Contents
PythonMachineLearningTutorial........................................................................................................1
Introduction to Machine Learning With Python...........................................................................................3
Tasks in Machine Learning Using Python....................................................................................................3
a. Supervised Learning..............................................................................................................................4
b. Unsupervised Learning .........................................................................................................................5
Steps in Python Machine Learning...............................................................................................................5
Applications of Python Machine Learning...................................................................................................6
a. Fighting and filtering webspam and malware.......................................................................................6
b. Refining search-engine results..............................................................................................................7
c. Virtual Personal Assistants....................................................................................................................7
d. Social Media Services............................................................................................................................7
e. Online customer support......................................................................................................................8
f. Product recommendations....................................................................................................................8
g. Online fraud detection..........................................................................................................................8
h. Video Surveillance.................................................................................................................................8
i. Automatic Translation............................................................................................................................9
Companies Using Python Machine Learning ...............................................................................................9
a. Apple .....................................................................................................................................................9
b. Google.................................................................................................................................................10
c. Microsoft.............................................................................................................................................11
d. Twitter.................................................................................................................................................11
e. Intel .....................................................................................................................................................12
f. Baidu....................................................................................................................................................13
g. IBM......................................................................................................................................................13
h. Salesforce............................................................................................................................................14
i. Pindrop.................................................................................................................................................14
j. Qubit ....................................................................................................................................................15
Python Machine Learning Tutorial – Conclusion.......................................................................................15
3. Python Machine Learning Tutorial – Tasks and Applications
Introduction to Machine Learning With Python
In this Python Machine Learning Tutorial, Machine Learning also termed ML. It is a
subset of AI (Artificial Intelligence) and aims to grants computers the ability to learn
by making use of statistical techniques. It deals with algorithms that can look at data
to learn from it and make predictions.
Do you know about statistics in Python
Tasks in Machine Learning Using Python
With Python Machine Learning, we divide the tasks of Machine Learning
Algorithms in Python into two broad categories- Supervised and Unsupervised.
4. Python Machine Learning Tutorial – Tasks of Machine learning
a. Supervised Learning
Here, a learning signal/ feedback is available to the system; we give it to sample data
to learn from. The computer holds example inputs and desired outputs with the goal of
learning a general rule that maps inputs to outputs. One such example of Python
Machine Learning will be to search for images on Facebook using keywords centered
around the contents of the image. Under Supervised Learning, we have the following
kinds of Python machine Learning-
Semi-Supervised Learning- The computer receives an incomplete training
signal. This is a training set with some target outputs missing.
Active Learning- The computer can secure training labels for only some
instances. It also needs to make an optimal choice of objects to secure labels.
Reinforcement Learning- In this, the training data comes as feedback on how a
program acts in a dynamic environment. Examples of this include driving a
vehicle or playing against an opponent.
Steps involved in Supervised Machine Learning-
5. Training
testing
Among many Supervised Machine Learning Algorithms for beginners we observe,
here we list some-
Let’s discuss Machine Learning Applications
Decision trees
Support Vector Machines
Naïve Bayes
k-nearest neighbor
Linear regression
b. Unsupervised Learning
In unsupervised learning, the Python Machine Learning Algorithm receives no labels;
we only give the machine a set of inputs. It must rely on itself to find structure in its
input. This kind of learning can be a goal or a means toward future learning. We can
classify unsupervised learning as-
Clustering- The act of grouping data inherently. One example of this will be to
group consumers by their shopping habits so they can target the right consumers
to advertise.
Association- In association, we identify rules explaining large sets of our data.
One example will be to associate books around author/ category.
Of the many Unsupervised Machine Learning Algorithms, we observe, here are a
couple-
K-means clustering
Hierarchical clustering
Steps in Python Machine Learning
We follow the following steps in Machine Learning Using Python-
1. Collecting data.
2. Filtering data.
6. 3. Analyzing data.
4. Training algorithms.
5. Testing algorithms.
6. Using algorithms for future predictions.
Applications of Python Machine Learning
Where does machine learning with Python come to use? Let’s learn Applications of
Machine Learning with Python:
Python Machine Learning Tutorial – Applications of Machine learning
a. Fighting and filtering webspam and malware
With rule-based spam filtering, latest tricks by spammers can go unnoticed. e-mail
clients make use of machine learning to ensure its spam filters stay updated. Other
than that, imagine getting to Google and searching for something only to find
irrelevant listings right at the top. To fight these situations, Google uses ‘deep
7. learning’, a neural network that takes data from users and from NLP, and determines
the nature of the email in question. Some spam-filtering techniques under ML are
Multi-Layer Perceptron and C 4.5 Decision Tree Induction.
Let’s have a look at Python ML Techniques
b. Refining search-engine results
Suppose you went up to Google and typed in the keywords “DIY lampshade”. If you
visit one or more of the top listings and stay for a while, Google assumes it did a good
job serving your request. If, however, you end up on the third page and have not
visited any result, Google knows it could have done better. So, it improves search
results next time.
c. Virtual Personal Assistants
With assistants like Siri, Alexa, and Google Now, the term virtual personal assistant
needs no explanation. This help finds information for you, make calls, set alarms, and
check the weather among all other things they can do. And to make this easy for you,
all they need you to do is use your voice and command them to do it for you. When
you’ve got your hands filthy, or if you’ve just woken up and do not wish to lay your
eyes on the light of a screen, this comes in handy. Not to forget the huge importance
of this for those handicapped.
How you involve with them helps them collect and refine that information. This is
machine learning and this is how they generate better results next time.
d. Social Media Services
On social media, facilities like ‘People You May Know’ and ‘Face Recognition’ work
via machine learning. Considering your activity like the profiles you visit, the people
you befriend, the people you tag, Facebook curates a list of suggestions for you to
enrich your experience and make you stay.
8. e. Online customer support
Some websites will pop a live chat option up to make your stay in case you need a
query to be answered. For some, it isn’t live but is a chatbot. Such a bot pulls
information from the website and delivers it to the customer. The machine learning
algorithms make it possible to improve this experience.
Let’s discuss Train and Test Set in Python ML
f. Product recommendations
Shopping giants like Jabong and Amazon curate a list of products similar to the ones
you’re visiting. They also mail you shopping suggestions. This is machine learning
behind the scenes; it pays attention to your past purchases, wishlist, cart contents,
brand preferences, and so.
g. Online fraud detection
Companies like PayPal use ML to fight against issues like money laundering. They
compare millions of transactions to differentiate between those legitimate and
illegitimate.
h. Video Surveillance
With ML, video surveillance systems can detect a possible crime ahead of it. Risque
behavior like people standing motionless for a while monitoring a situation, napping
on a bench, and following another individual can alert human attendants. When this
can prevent a mishap and save a life, incidents like these help improve such
surveillance services.
Let’s know why we should learn Machine Learning
9. i. Automatic Translation
ML makes it possible to translate text from one language to another. The algorithm
learns how words fit together and use that to improve the translation. This is also
possible to text on images. This is done with neural networks to identify letters in the
images. It translates the text and then puts it back onto the picture.
Companies Using Python Machine Learning
Of many others, the following 10 companies make use of machine learning tools and
technologies to grow and improve their functions.
Python Machine Learning Tutorial – Companies
a. Apple
10. Apple was the first to ship a voice assistant on a smartphone. And with HomePod, it
aspires to take this a step further.
Python Machine Learning Tutorial – Apple
With the rising competition, it is the technology and the end user that benefits. Apple
paid $200 to purchase Lattice Data, which can convert unstructured data into a
structured form using ML. It also develops in-house machine learning systems.
b. Google
11. Python Machine Learning Tutorial – Google
Google offers, to developers, multiple cloud-based services. One of these is the
Google Cloud AI machine learning tools. Recently, Google launched an AI chatbot
that will answer messages for you. This is like a sophisticated auto-response email.
c. Microsoft
Python Machine Learning Tutorial – Microsoft
Microsoft purchased LinkedIn a few years ago at $26 billion and has lately been the
third-biggest spender on acquisitions. Maluuba, a Canadian tech company that houses
a very impressive deep learning research lab for Natural Language Understanding.
d. Twitter
12. Python Machine Learning Tutorial – Twitter
Ever since Facebook changed its algorithm to favor posts from friends and family
over news articles from reputed sources, Twitter’s profitability has raised. Here,
machine learning makes it possible to find out what people might be interested in and
curate content for them.
e. Intel
Python Machine Learning Tutorial – Intel
Intel is the largest chipmaker in the world. In the last few years, it acquired Nervana
Systems (manufacturer of chips for data center servers) at a capital of $400 million.
Nervana chips can transfer data at around 2.4 terabytes per second at a low latency.
Have a look at the advantages & disadvantages of Machine learning
13. f. Baidu
Python Machine Learning Tutorial – Baidu
Baidu is a Chinese search giant and takes a keen interest in Natural Language
Processing. It also aims to develop a functioning voice-activated search facility.
Recently, it acquired Kitt.ai, which has a portfolio of chatbots and voice-based
applications. Very easily, Baidu is the 10th largest spender on acquisitions.
g. IBM
Python Machine Learning Tutorial – IBM
14. Back in the 1990s, IBM challenged Garry Kasparov, Russia’s greatest chess player, to
a match against Deep Blue, a computer by IBM. Kasparov won the first match and
flunked the next few. Later, computer Watson AI beat contestants on the quiz
show Jeopardy!. More recently, the machine won the ancient board game ‘Go’ in a
recent human-vs-machine contest.
h. Salesforce
Python Machine Learning Tutorial – Salesforce
Salesforce is the sixth-largest buyer of AI companies over the last five years, CB
Insights claims. Recently, it said it had a year of ‘Einstein’ technology- one that
analyzes each aspect of a customer’s relationship with a company.
i. Pindrop
15. Python Machine Learning Tutorial – Pindrop
Pindrop claims to present a pioneering technology for recognizing fraudulent activity
over the phone channel. In what it calls ‘phoneprinting’, for every call, it analyzes
1,300 unique call features and creates an audio fingerprint for each. Such features
include noise, location, number history, and call type. It flags suspicious calls and can
spot ID spoofing, voice distortion, and social engineering.
j. Qubit
Python Machine Learning Tutorial- Qubit
Qubit has an AI-powered personalized shopping app, Aura. This has a database of
products in a range of categories like fashion, clothing, and cosmetics. Pending
patents suggest an Instagram-like feed of product images.
So, this was all in Python Machine Learning Tutorial. Hope you like our explanation
of Machine Learning Python Course.
Python Machine Learning Tutorial – Conclusion
16. Hence, in this Python Machine Learning Tutorial, we discussed what is Python
Machine Learning and tasks in Python and Machine Learning. Moreover, we
discussed applications of Python Machine Learning. Also, we saw companies using
Machine Learning with Python. By now, we realize machine learning is powerful.
Let’s delve into the world of ML and learn something new. Still, if you have any
doubt regarding Python Machine Learning, ask in the comment tab.