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.
The slides for my presentation on BIG DATA EN LAS ESTADÍSTICAS OFICIALES - ECONOMÍA DIGITAL Y EL DESARROLLO, 2019 in Colombia. I was invited to give a talk about the technical aspect of web-scraping and data collection for online resources.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
The slides for my presentation on BIG DATA EN LAS ESTADÍSTICAS OFICIALES - ECONOMÍA DIGITAL Y EL DESARROLLO, 2019 in Colombia. I was invited to give a talk about the technical aspect of web-scraping and data collection for online resources.
Natural Language Processing is a subfield of Artificial Intelligence and linguistics, devoted to make computers understand the statements or words written by humans.
In this seminar we discuss its issues, and its working etc...
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
Transfer Learning for Natural Language ProcessingSebastian Ruder
Slides on Transfer Learning for Natural Language Processing by Sebastian Ruder. Talk given at Natural Language Processing Copenhagen Meetup on 31 May 2017.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
Introduction to Web Scraping using Python and Beautiful SoupTushar Mittal
These are the slides on the topic Introduction to Web Scraping using the Python 3 programming language. Topics covered are-
What is Web Scraping?
Need of Web Scraping
Real Life used cases .
Workflow and Libraries used.
Introduction to Natural Language ProcessingPranav Gupta
the presentation gives a gist about the major tasks and challenges involved in natural language processing. In the second part, it talks about one technique each for Part Of Speech Tagging and Automatic Text Summarization
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing.
Natural language processing provides a way in which human interacts with computer / machines by means of voice.
"Google Search by voice is the best example " which makes use of natural language processing..
Transfer Learning for Natural Language ProcessingSebastian Ruder
Slides on Transfer Learning for Natural Language Processing by Sebastian Ruder. Talk given at Natural Language Processing Copenhagen Meetup on 31 May 2017.
Develop a fundamental overview of Google TensorFlow, one of the most widely adopted technologies for advanced deep learning and neural network applications. Understand the core concepts of artificial intelligence, deep learning and machine learning and the applications of TensorFlow in these areas.
The deck also introduces the Spotle.ai masterclass in Advanced Deep Learning With Tensorflow and Keras.
An on-going project on Natural Language Processing (using Python and the NLTK toolkit), which focuses on the extraction of sentiment from a Question and its title on www.stackoverflow.com and determining the polarity.Based on the above findings, it is verified whether the rules and guidelines imposed by the SO community on the users are strictly followed or not.
Introduction to Web Scraping using Python and Beautiful SoupTushar Mittal
These are the slides on the topic Introduction to Web Scraping using the Python 3 programming language. Topics covered are-
What is Web Scraping?
Need of Web Scraping
Real Life used cases .
Workflow and Libraries used.
Lecture-1-Introduction to Deep learning.pptxJayChauhan100
Introduction To Deep Learning.
This presentation covers everything about deep learning. You will be familier with all the main concepts used in deep learning.
Includes topics like difference between deep learning and machine learning, Feature engineering in detail, Deep learning frameworks , applications of deep learning etc.
This presentation will surely help you to know about the deep learning.
For queries contact on the given email id.
Email - chauhanjay657@gmail.com
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
Unlocking the Potential of Artificial Intelligence_ Machine Learning in Pract...eswaralaldevadoss
Machine learning is a subset of artificial intelligence that involves training computers to learn from data and make predictions or decisions based on that data. It involves building algorithms and models that can learn patterns and relationships from data and use that knowledge to make predictions or take actions.
Here are some key concepts that can help beginners understand machine learning:
Data: Machine learning algorithms require data to learn from. This data can come from a variety of sources such as databases, spreadsheets, or sensors. The quality and quantity of data can greatly impact the accuracy and effectiveness of machine learning models.
Training: In machine learning, training involves feeding data into a model and adjusting its parameters until it can accurately predict outcomes. This process involves testing and tweaking the model to improve its accuracy.
Algorithms: There are many different algorithms used in machine learning, each with its own strengths and weaknesses. Common machine learning algorithms include decision trees, random forests, and neural networks.
Supervised vs. Unsupervised Learning: Supervised learning involves training a model on labeled data, where the desired outcome is already known. Unsupervised learning, on the other hand, involves training a model on unlabeled data and allowing it to identify patterns and relationships on its own.
Evaluation: After training a model, it's important to evaluate its accuracy and performance on new data. This involves testing the model on a separate set of data that it hasn't seen before.
Overfitting vs. Underfitting: Overfitting occurs when a model is too complex and fits the training data too closely, leading to poor performance on new data. Underfitting occurs when a model is too simple and fails to capture important patterns in the data.
Applications: Machine learning is used in a wide range of applications, from predicting stock prices to identifying fraudulent transactions. It's important to understand the specific needs and constraints of each application when building machine learning models.
Overall, machine learning is a powerful tool that can help businesses and organizations make more informed decisions based on data. By understanding the basic concepts and techniques of machine learning, beginners can begin to explore the potential applications and benefits of this exciting field.
Unit I and II Machine Learning MCA CREC.pptxtrishipaul
Machine Learning topics presentation covering the topics:
Unit I – Introduction: Towards Intelligent Machines, Well posed Problems, Example of Applications in diverse fields, Data Representation, Domain Knowledge for Productive use of Machine Learning, Diversity of Data: Structured / Unstructured, Forms of Learning, Machine Learning and Data Mining, Basic Linear Algebra in Machine Learning Techniques.
Unit II – Supervised Learning – Rationale and Basics: Learning from Observations: Why Learning Works, Bias and Variance: Computations Learning Theory, Occam’s Razor Principle and Overfitting Avoidance, Heuristic Search in Inductive Learning, Estimating Generalization Errors, Metrics for Assessing Regression, Metrics for Assessing Classification.
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..
This knolx is about an introduction to machine learning, wherein we see the basics of various different algorithms. This knolx isn't a complete intro to ML but can be a good starting point for anyone who wants to start in ML. In the end, we will take a look at the demo wherein we will analyze the FIFA dataset going through the understanding of various data analysis techniques and use an ML algorithm to derive 5 players that are similar to each other.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Hierarchical Digital Twin of a Naval Power SystemKerry Sado
A hierarchical digital twin of a Naval DC power system has been developed and experimentally verified. Similar to other state-of-the-art digital twins, this technology creates a digital replica of the physical system executed in real-time or faster, which can modify hardware controls. However, its advantage stems from distributing computational efforts by utilizing a hierarchical structure composed of lower-level digital twin blocks and a higher-level system digital twin. Each digital twin block is associated with a physical subsystem of the hardware and communicates with a singular system digital twin, which creates a system-level response. By extracting information from each level of the hierarchy, power system controls of the hardware were reconfigured autonomously. This hierarchical digital twin development offers several advantages over other digital twins, particularly in the field of naval power systems. The hierarchical structure allows for greater computational efficiency and scalability while the ability to autonomously reconfigure hardware controls offers increased flexibility and responsiveness. The hierarchical decomposition and models utilized were well aligned with the physical twin, as indicated by the maximum deviations between the developed digital twin hierarchy and the hardware.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Final project report on grocery store management system..pdfKamal Acharya
In today’s fast-changing business environment, it’s extremely important to be able to respond to client needs in the most effective and timely manner. If your customers wish to see your business online and have instant access to your products or services.
Online Grocery Store is an e-commerce website, which retails various grocery products. This project allows viewing various products available enables registered users to purchase desired products instantly using Paytm, UPI payment processor (Instant Pay) and also can place order by using Cash on Delivery (Pay Later) option. This project provides an easy access to Administrators and Managers to view orders placed using Pay Later and Instant Pay options.
In order to develop an e-commerce website, a number of Technologies must be studied and understood. These include multi-tiered architecture, server and client-side scripting techniques, implementation technologies, programming language (such as PHP, HTML, CSS, JavaScript) and MySQL relational databases. This is a project with the objective to develop a basic website where a consumer is provided with a shopping cart website and also to know about the technologies used to develop such a website.
This document will discuss each of the underlying technologies to create and implement an e- commerce website.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
2. Topics Covered
1.1 Introduction to Machine Learning
Artificial Intelligence
Machine Learning
Application of Machine Learning
1.2 Types of Machine Learning
1.3 Supervised Machine Learning
1.3.1 Classification
1.4 Unsupervised Machine Learning and its Application
1.4.1 Difference between Supervised and Unsupervised Machine
Learning
1.5 Semi-Supervised Machine Learning
1.6 Reinforcement Machine Learning and its Application
1.7 Hypothesis Space and Inductive Bias
1.8 Underfitting and Overfitting
1.9 Evaluation and Sampling Methods
1.9.1 Regression Metrics
1.9.2 Classification Metrics
1.10 Training and Test Dataset and Need of
Cross Validation
1.11 Linear Regression
1.111 Linear Models
1.12 Decision Trees
1.12.1 The Decision Tree Learning Algorithm
1.12.2 Entropy
1.12.3 Information Gain
1.124 Impurity Measures
Exercise
3. Introduction to Machine Learning
Machine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of
data and algorithms to imitate the way that humans learn, gradually improving its accuracy.
Machine Learning is an umbrella term used to describe a variety of different tools and techniques which
allow a machine or a computer program to learn and improve over time.
ML tools and techniques include but are not limited to Statistical Reasoning, Data Mining, Mathematics and
Programming.
To learn, a machine needs Data, Processing Power/Performance and Time. It could be said that if a machine
gets better at something over time and improves its performance as more data is acquired, then this
machine is said to be learning and we could call this process Machine Learning.
4. Introduction to Machine Learning
Machines/computers an ability to learn the way humans do, i.e. without explicitly telling them what to do.
Machine learning gives computers the ability to learn without being explicitly programmed.
Arthur Samuel
Machine learning refers to teaching devices to learn information given to a dataset without manual human
interference.
5. Well Posed Learning Problem
A well-posed learning problem is a task in which the Input, Output, and Learning objective are clearly defined, and there exists a
unique solution to the problem.
A well-posed learning problem has three properties:
1. Existence: The problem must have at least one solution. There must be a possible relationship between the input and output data.
2. Uniqueness: The problem must have a unique solution. There must be only one correct relationship between the input and output
data.
3. Stability: The solution to the problem must be stable with respect to small changes in the input data. The output produced by the
machine learning algorithm should not change significantly when the input data is slightly modified.
4. A well-posed learning problem is essential for the development of effective and reliable machine learning algorithms. Without a
well-posed problem, the algorithm may produce incorrect or unstable results, making it difficult to use in practical applications.
So it is important to carefully define the input, output, and learning objective when formulating a machine learning
problem.
6. Well Posed Learning Problem
A learning problem can be defined as a task in which an agent (such as A Machine Learning
Algorithm or a Human) must learn to perform a specific task or make predictions based on a set of
inputs or data.
Three features that can be identified in a learning problem are:
Input data: This refers to the set of data or information that the agent uses to learn and make
predictions. The input data can be structured or unstructured, and may come from a variety of sources
such as text, images, audio, or sensor data.
Output or prediction: This refers to the task that the agent is trying to learn or the prediction that it is
trying to make based on the input data. The output can be a single value, a set of values, or a
probability distribution over possible outcomes.
7. Well Posed Learning Problem
Evaluation metric / Performance measure: This refers to the measure or metric that is used to evaluate
the performance of the agent on the learning task.
The evaluation metric may vary depending on the specific learning problem and may include metrics such as
Accuracy, Precision, Recall, F1 Score, or Mean Squared Error.
Definition:-
A computer program is said to learn from experience E with respect to some class of tasks T and
performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.
Tom Mitchell
8. Examples of well-posed learning problems:
2. Sentiment analysis: Given a set of text documents,
Task:- Is to learn a model that can predict the sentiment
of new documents (e.g., positive, negative, or neutral).
Input:- Is the text data,
Output:- Is the sentiment label
Learning objective:- Is to minimize the prediction error.
Performance Measure :- Percentage of prediction of
the sentiments of new documents.
Training Experience :- A database of sentiments of
given documents.
1. Image classification: Given a set of labeled images,
Task:- Is to learn a model that can Correctly classify
new images into their respective classes.
Input:- Is the image data
Output:- Is the class label,
Learning objective:- Is to Minimize the Classification
Error.
Performance Measure :- Percentage of images
correctly classified.
Training Experience :- A Database of images with
given classification
9. Examples of well-posed learning problems:
3. Fraud detection: Given a set of transaction data,
Task:- Is to learn a model that can identify fraudulent transactions.
Input:- Is the transaction data
Output:- Is a binary label (fraudulent or not),
Learning objective:- Is to minimize the false positive and false
negative rates.
Performance Measure :- Percentage of False Positive and False
Negative Rates.
4. Regression: Given a set of input features and corresponding target
values,
Task:- Task is to learn a model that can predict the target value for
new input data
Input:- Is the feature data
Output:- Is the target value,
Learning objective:- Is to minimize the prediction error (e.g., mean
squared error).
Performance Measure :- Percentage of the prediction error.
10.
11.
12. History of Machine Learning
Year 1950 : Alan Turing developed the Turing Test during this year.
Year 1957 : Perceptron - The first ever Neural Network
Year 1960 : MIT developed a Natural Language Processing program to act as a therapist. The program was called ELIZA.
Year 1967 : The advent of Nearest Neighbor algorithm, very prominently used in Search and Approximation
Year 1970 : Backpropagation takes shape. Backpropagation is a set of algorithms used extensively in Deep Learning.
Year 1980 : Kunihiko Fukushima successfully built a multilayered Neural Network called ANN.
Year 1981 : Explanation Based Learning
Year 1989 : Reinforcement Learning is finally realized. Q-Learning algorithm.
Year 2009 : ImageNet
Year 2010 : Google Brain and Facebook's DeepFace
Year 2022 : ChatGPT Chat Generative Pre-trained Transformer
https://www.zeolearn.com/magazine/what-is-machine-learning
13. Artificial Intelligence vs. Machine Learning vs. Deep Learning vs. Neural
Networks
Machine learning, Deep learning, and Neural networks are all sub-fields of Artificial Intelligence.
Neural networks is a sub-field of Machine learning, and Deep learning.
Deep" Machine learning can use labeled datasets, also known as Supervised learning. Eliminates some of the
human intervention required and enables the use of larger data sets.
“Non-deep", Machine learning is more dependent on human intervention to learn. Human experts determine
the set of features to understand the differences between data inputs, requiring more structured data to learn.
Neural networks, or artificial neural networks (ANNs), are comprised of node layers, containing an input layer,
one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an
associated weight and threshold.
Deep learning and Neural Networks are accelerate progress in areas such as computer vision, natural language
processing, and speech recognition.
14. Artificial Intelligence vs. Machine Learning vs. Deep Learning vs.
Neural Networks
AI refers to the software and processes that are designed to mimic the way humans think and process
information. It includes computer vision, natural language processing, robotics, autonomous vehicle operating
systems, and machine learning.
With the help of artificial intelligence, Devices are able to learn and identify information in order to solve
problems and offer key insights into various domains.
15. Artificial Intelligence vs. Machine Learning vs. Deep
Learning vs. Neural Networks
AI enables machines to understand data and make decisions based on patterns hidden
in data without any human intervention.
Machines adjust their knowledge based on new inputs.
Example, Self-driving cars , Alexa and Cortana - Conversations with us in our natural
human language
Machine Learning:- Subset of AI
Machine learning with the help of the algorithm can process the surplus of
information and output an accurate prediction within moments. Use deep learning all
the time.
Uses statistical models to explore, analyze and find patterns in large amounts of
data.
Perform tasks without being explicitly programmed, allows them to learn from
experience and improve over time without human intervention.
https://learnerjoy.com/artificial-intelligence-vs-machine-learning-vs-deep-learning-vs-data-science/
16. Artificial Intelligence vs. Machine Learning vs. Deep
Learning vs. Neural Networks
Approaches:- 1. Supervised learning, 2. Unsupervised learning and 3.
Reinforcement learning.
1. Supervised learning:- Requires a human to input labelled data /Past
Labeled data into the machine and outputs a prediction of a new sample.
2. Unsupervised learning:- Takes unlabeled data as input, groups the
data based on its similarity and outputs clusters of similar samples for the
human to analyze further reinforcement. O/p Not known. Algorithms- L-
means, Hierarchical Clustering, PCA , Neural Network.
3. Reinforcement learning. :- Reinforcement learning is also known as
semi-supervised learning. A small amount of labeled data and a large
amount of unlabeled data and utilizes a reward or trial and error system
to learn over time. Good Action and Bad Action
17. Artificial Intelligence vs. Machine Learning vs. Deep
Learning vs. Neural Networks
Deep Learning - Deep learning is the subset of machine learning.
The main idea behind deep learning is machines to learn things like the human
brain.
Human brain is made of multitudes of neurons that allow us to operate the way
we do.
The collection of connected neurons in a human brain, scientists create a multi-
layer network that machines could use to learn from experience and predict.
Techniques
Artificial Neural Networks (ANN):- I/P in the form of Numbers
Convolutional Neural Networks (CNN):- I/P in the form of Images
Recurrent neural networks (RNN). I/P in the form of Time Series Data
Two popular frameworks used in Deep learning are
•PyTorch by Facebook
•TensorFlow by Google
18. Artificial Intelligence vs. Machine Learning vs. Deep
Learning vs. Neural Networks
Data Science
Data science is to perform exploratory analysis to better understand
the data.
It plays a huge role when building ML models. If you have a huge
amount of data, you will get more insights from data and accurate
results that can be applied to business use cases.
Statistical tools –Linear algebra