This document provides an overview of a 7-day machine learning training program using Python. Day 1 introduces machine learning concepts and completing tasks related to datasets. Day 2 covers RapidMiner, data preprocessing, and creating a Google Colab notebook. Day 3 reviews Python concepts and provides practice problems. Day 4 focuses on data preprocessing in Python. Day 5 discusses model evaluation, linear regression, and completing practice problems. Days 6-7 cover simple/multiple linear regression and decision tree regression with more practice problems. Days 8-10 cover support vector machine regression, logistic regression, and different classification algorithms. Day 11 explains the Naive Bayes algorithm.
The ABC of Implementing Supervised Machine Learning with Python.pptxRuby Shrestha
It is to our fact that machine learning has taken a significant height. However, knowing and understanding how small problems can be solved from a machine learning perspective is necessary to form a good base, appreciate the process of implementation and get started in this domain. Therefore, in this post, I would like to talk about the ABC of implementing Supervised Machine Learning with Python by navigating through a simple example, which is, adding two numbers. So, to put it in simple terms, I would like to make a machine learn to add. This can be put in other words; I would like to develop a predictive model that can add. Sounds simple, right? View the presentation for more details.
The document provides an overview and agenda for an introduction to running AI workloads on PowerAI. It discusses PowerAI and how it combines popular deep learning frameworks, development tools, and accelerated IBM Power servers. It then demonstrates AI workloads using TensorFlow and PyTorch, including running an MNIST workload to classify handwritten digits using basic linear regression and convolutional neural networks in TensorFlow, and an introduction to PyTorch concepts like tensors, modules, and softmax cross entropy loss.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Apache Cassandra Lunch #54: Machine Learning with Spark + Cassandra Part 2Anant Corporation
In Apache Cassandra Lunch #54, we will discuss how you can use Apache Spark and Apache Cassandra to perform additional basic Machine Learning tasks.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-54-machine-learning-with-spark--cassandra-part-2/
Accompanying YouTube Video: https://youtu.be/3roCSBWQzRk
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
In Apache Cassandra Lunch #50, we will discuss how you can use Apache Spark and Apache Cassandra to perform basic Machine Learning tasks.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-50-machine-learning-with-spark--cassandra/
Accompanying YouTube video: https://youtu.be/myIX0kkpL9U
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Inteligencia artificial para android como empezarIsabel Palomar
Aprenderás los conceptos basico de deep learning y como crear tu aplicación de Android que puede detectar y etiquetar imágenes utilizando un modelo de Tensorflow Lite
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
This document provides an overview of machine learning using Python. It introduces machine learning applications and key Python concepts for machine learning like data types, variables, strings, dates, conditional statements, loops, and common machine learning libraries like NumPy, Matplotlib, and Pandas. It also covers important machine learning topics like statistics, probability, algorithms like linear regression, logistic regression, KNN, Naive Bayes, and clustering. It distinguishes between supervised and unsupervised learning, and highlights algorithm types like regression, classification, decision trees, and dimensionality reduction techniques. Finally, it provides examples of potential machine learning projects.
The ABC of Implementing Supervised Machine Learning with Python.pptxRuby Shrestha
It is to our fact that machine learning has taken a significant height. However, knowing and understanding how small problems can be solved from a machine learning perspective is necessary to form a good base, appreciate the process of implementation and get started in this domain. Therefore, in this post, I would like to talk about the ABC of implementing Supervised Machine Learning with Python by navigating through a simple example, which is, adding two numbers. So, to put it in simple terms, I would like to make a machine learn to add. This can be put in other words; I would like to develop a predictive model that can add. Sounds simple, right? View the presentation for more details.
The document provides an overview and agenda for an introduction to running AI workloads on PowerAI. It discusses PowerAI and how it combines popular deep learning frameworks, development tools, and accelerated IBM Power servers. It then demonstrates AI workloads using TensorFlow and PyTorch, including running an MNIST workload to classify handwritten digits using basic linear regression and convolutional neural networks in TensorFlow, and an introduction to PyTorch concepts like tensors, modules, and softmax cross entropy loss.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Apache Cassandra Lunch #54: Machine Learning with Spark + Cassandra Part 2Anant Corporation
In Apache Cassandra Lunch #54, we will discuss how you can use Apache Spark and Apache Cassandra to perform additional basic Machine Learning tasks.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-54-machine-learning-with-spark--cassandra-part-2/
Accompanying YouTube Video: https://youtu.be/3roCSBWQzRk
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
In Apache Cassandra Lunch #50, we will discuss how you can use Apache Spark and Apache Cassandra to perform basic Machine Learning tasks.
Accompanying Blog: https://blog.anant.us/apache-cassandra-lunch-50-machine-learning-with-spark--cassandra/
Accompanying YouTube video: https://youtu.be/myIX0kkpL9U
Sign Up For Our Newsletter: http://eepurl.com/grdMkn
Join Cassandra Lunch Weekly at 12 PM EST Every Wednesday: https://www.meetup.com/Cassandra-DataStax-DC/events/
Cassandra.Link:
https://cassandra.link/
Follow Us and Reach Us At:
Anant:
https://www.anant.us/
Awesome Cassandra:
https://github.com/Anant/awesome-cassandra
Cassandra.Lunch:
https://github.com/Anant/Cassandra.Lunch
Email:
solutions@anant.us
LinkedIn:
https://www.linkedin.com/company/anant/
Twitter:
https://twitter.com/anantcorp
Eventbrite:
https://www.eventbrite.com/o/anant-1072927283
Facebook:
https://www.facebook.com/AnantCorp/
Inteligencia artificial para android como empezarIsabel Palomar
Aprenderás los conceptos basico de deep learning y como crear tu aplicación de Android que puede detectar y etiquetar imágenes utilizando un modelo de Tensorflow Lite
Start machine learning in 5 simple stepsRenjith M P
Simple steps to get started with machine learning.
The use case uses python programming. Target audience is expected to have a very basic python knowledge.
This document provides an overview of machine learning using Python. It introduces machine learning applications and key Python concepts for machine learning like data types, variables, strings, dates, conditional statements, loops, and common machine learning libraries like NumPy, Matplotlib, and Pandas. It also covers important machine learning topics like statistics, probability, algorithms like linear regression, logistic regression, KNN, Naive Bayes, and clustering. It distinguishes between supervised and unsupervised learning, and highlights algorithm types like regression, classification, decision trees, and dimensionality reduction techniques. Finally, it provides examples of potential machine learning projects.
Machine Learning with Python discusses machine learning concepts and the Python tools used for machine learning. It introduces machine learning terminology and different types of learning. It describes the Pandas, Matplotlib and scikit-learn frameworks for data analysis and machine learning in Python. Examples show simple programs for supervised learning using linear regression and unsupervised learning using K-means clustering.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
Fyber implemented XGBoost models for two main use cases: Audience Vault Reach prediction and CTR prediction for their offer wall. For Audience Vault Reach, XGBoost with Spark was used to predict audience size over the next 14 days using historical user activity data. For CTR prediction, XGBoost ranked offers based on attributes to better estimate performance compared to old manual configurations. Both models involved data preprocessing, feature engineering, training XGBoost pipelines on Spark, and integrating the models into products.
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
Using Apache Spark with IBM SPSS Modeler with Dr. Steve Poulin.
An introduction to Apache Spark and its relevant integration with IBM SPSS Modeler. Why integrate? What type of benefits?
A review the integration process high level and advise which enhanced features to pay attention to, and common pitfalls to avoid.
Creating a custom Machine Learning Model for your applications - Java Dev Day...Isabel Palomar
Aprenderás como puede ser creado un modelo de Machine Learning que puedas implementar en tu aplicación móvil o Java. Iré mostrando cada uno de los pasos que se tienen que seguir, los tipos de problemas que se pueden resolver, los datos que necesitas para que funcione y por último, las opciones para realizar la implementación de nuestro modelo en nuestras aplicaciones.
The document discusses Spark streaming and machine learning concepts like logistic regression, linear regression, and clustering algorithms. It provides code examples in Scala and Python showing how to perform binary classification on streaming data using Spark MLlib. Links and documentation are referenced for setting up streaming machine learning pipelines to train models on streaming data in real-time and make predictions.
Team knowledge sharing presentation covering topics of decision trees, XGBoost, logistic regression, neural networks, and deep learning using scikit-learn, statsmodels, and Keras over TensorFlow in python within PowerBI, Azure Notebooks, AWS SageMaker notebooks, and Google Colab notebooks
Python is the choice llanguage for data analysis,
The aim of this slide is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of the steps you need to learn to use Python for data analysis.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Machine learning Algorithms with a Sagemaker demoHridyesh Bisht
An algorithm is a set of steps to solve a problem. Supervised learning uses labeled training data to teach models patterns which they can then use to predict labels for new unlabeled data. Unsupervised learning uses clustering and pattern detection to analyze and group unlabeled data. SageMaker is a fully managed service that allows users to build, train and deploy machine learning models and includes components for managing notebooks, labeling data, and deploying models through endpoints.
This document provides an overview of an introductory course on algorithms and data structures. It discusses key topics that will be covered including introduction to algorithms, complexity analysis, algorithm design strategies like divide and conquer, and data structures. Specific examples of algorithms and data structures are provided like sorting, searching, linked lists, stacks, queues, trees and graphs. Implementation tools for algorithms like pseudo code and flowcharts are also introduced.
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks
Description
Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Standardizing on a single N-dimensional array API for PythonRalf Gommers
MXNet workshop Dec 2020 presentation on the array API standardization effort ongoing in the Consortium for Python Data API Standards - see data-apis.org
A Hands-on Intro to Data Science and R Presentation.pptSanket Shikhar
Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
Machine Learning with Python discusses machine learning concepts and the Python tools used for machine learning. It introduces machine learning terminology and different types of learning. It describes the Pandas, Matplotlib and scikit-learn frameworks for data analysis and machine learning in Python. Examples show simple programs for supervised learning using linear regression and unsupervised learning using K-means clustering.
Big Data Analytics (ML, DL, AI) hands-onDony Riyanto
Ini adalah slide tambahan dari materi pengenalan Big Data Analytics (di file berikutnya), yang mengajak kita mulai hands-on dengan beberapa hal terkait Machine/Deep Learning, Big Data (batch/streaming), dan AI menggunakan Tensor Flow
This presentation is aimed at fitting a Simple Linear Regression model in a Python program. IDE used is Spyder. Screenshots from a working example are used for demonstration.
Fyber implemented XGBoost models for two main use cases: Audience Vault Reach prediction and CTR prediction for their offer wall. For Audience Vault Reach, XGBoost with Spark was used to predict audience size over the next 14 days using historical user activity data. For CTR prediction, XGBoost ranked offers based on attributes to better estimate performance compared to old manual configurations. Both models involved data preprocessing, feature engineering, training XGBoost pipelines on Spark, and integrating the models into products.
The document describes developing a model to predict house prices using deep learning techniques. It proposes using a dataset with house features without labels and applying regression algorithms like K-nearest neighbors, support vector machine, and artificial neural networks. The models are trained and tested on split data, with the artificial neural network achieving the lowest mean absolute percentage error of 18.3%, indicating it is the most accurate model for predicting house prices based on the data.
The document discusses algorithms and data structures. It begins by introducing common data structures like arrays, stacks, queues, trees, and hash tables. It then explains that data structures allow for organizing data in a way that can be efficiently processed and accessed. The document concludes by stating that the choice of data structure depends on effectively representing real-world relationships while allowing simple processing of the data.
Using Apache Spark with IBM SPSS Modeler with Dr. Steve Poulin.
An introduction to Apache Spark and its relevant integration with IBM SPSS Modeler. Why integrate? What type of benefits?
A review the integration process high level and advise which enhanced features to pay attention to, and common pitfalls to avoid.
Creating a custom Machine Learning Model for your applications - Java Dev Day...Isabel Palomar
Aprenderás como puede ser creado un modelo de Machine Learning que puedas implementar en tu aplicación móvil o Java. Iré mostrando cada uno de los pasos que se tienen que seguir, los tipos de problemas que se pueden resolver, los datos que necesitas para que funcione y por último, las opciones para realizar la implementación de nuestro modelo en nuestras aplicaciones.
The document discusses Spark streaming and machine learning concepts like logistic regression, linear regression, and clustering algorithms. It provides code examples in Scala and Python showing how to perform binary classification on streaming data using Spark MLlib. Links and documentation are referenced for setting up streaming machine learning pipelines to train models on streaming data in real-time and make predictions.
Team knowledge sharing presentation covering topics of decision trees, XGBoost, logistic regression, neural networks, and deep learning using scikit-learn, statsmodels, and Keras over TensorFlow in python within PowerBI, Azure Notebooks, AWS SageMaker notebooks, and Google Colab notebooks
Python is the choice llanguage for data analysis,
The aim of this slide is to provide a comprehensive learning path to people new to python for data analysis. This path provides a comprehensive overview of the steps you need to learn to use Python for data analysis.
Jay Yagnik at AI Frontiers : A History Lesson on AIAI Frontiers
We have reached a remarkable point in history with the evolution of AI, from applying this technology to incredible use cases in healthcare, to addressing the world's biggest humanitarian and environmental issues. Our ability to learn task-specific functions for vision, language, sequence and control tasks is getting better at a rapid pace. This talk will survey some of the current advances in AI, compare AI to other fields that have historically developed over time, and calibrate where we are in the relative advancement timeline. We will also speculate about the next inflection points and capabilities that AI can offer down the road, and look at how those might intersect with other emergent fields, e.g. Quantum computing.
Machine learning Algorithms with a Sagemaker demoHridyesh Bisht
An algorithm is a set of steps to solve a problem. Supervised learning uses labeled training data to teach models patterns which they can then use to predict labels for new unlabeled data. Unsupervised learning uses clustering and pattern detection to analyze and group unlabeled data. SageMaker is a fully managed service that allows users to build, train and deploy machine learning models and includes components for managing notebooks, labeling data, and deploying models through endpoints.
This document provides an overview of an introductory course on algorithms and data structures. It discusses key topics that will be covered including introduction to algorithms, complexity analysis, algorithm design strategies like divide and conquer, and data structures. Specific examples of algorithms and data structures are provided like sorting, searching, linked lists, stacks, queues, trees and graphs. Implementation tools for algorithms like pseudo code and flowcharts are also introduced.
MLFlow: Platform for Complete Machine Learning Lifecycle Databricks
Description
Data Science and ML development bring many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work.
MLflow addresses some of these challenges during an ML model development cycle.
Abstract
ML development brings many new complexities beyond the traditional software development lifecycle. Unlike in traditional software development, ML developers want to try multiple algorithms, tools, and parameters to get the best results, and they need to track this information to reproduce work. In addition, developers need to use many distinct systems to productionize models. To address these problems, many companies are building custom “ML platforms” that automate this lifecycle, but even these platforms are limited to a few supported algorithms and to each company’s internal infrastructure.
In this session, we introduce MLflow, a new open source project from Databricks that aims to design an open ML platform where organizations can use any ML library and development tool of their choice to reliably build and share ML applications. MLflow introduces simple abstractions to package reproducible projects, track results, and encapsulate models that can be used with many existing tools, accelerating the ML lifecycle for organizations of any size.
With a short demo, you see a complete ML model life-cycle example, you will walk away with: MLflow concepts and abstractions for models, experiments, and projects How to get started with MLFlow Using tracking Python APIs during model training Using MLflow UI to visually compare and contrast experimental runs with different tuning parameters and evaluate metrics
Standardizing on a single N-dimensional array API for PythonRalf Gommers
MXNet workshop Dec 2020 presentation on the array API standardization effort ongoing in the Consortium for Python Data API Standards - see data-apis.org
A Hands-on Intro to Data Science and R Presentation.pptSanket Shikhar
Using popular data science tools such as Python and R, the book offers many examples of real-life applications, with practice ranging from small to big data.
Xavier Amatriain, VP of Engineering, Quora at MLconf SF - 11/13/15MLconf
10 More Lessons Learned from Building Real-Life ML Systems: A year ago I presented a collection of 10 lessons in MLConf. These goal of the presentation was to highlight some of the practical issues that ML practitioners encounter in the field, many of which are not included in traditional textbooks and courses. The original 10 lessons included some related to issues such as feature complexity, sampling, regularization, distributing/parallelizing algorithms, or how to think about offline vs. online computation.
Since that presentation and associated material was published, I have been asked to complement it with more/newer material. In this talk I will present 10 new lessons that not only build upon the original ones, but also relate to my recent experiences at Quora. I will talk about the importance of metrics, training data, and debuggability of ML systems. I will also describe how to combine supervised and non-supervised approaches or the role of ensembles in practical ML systems.
10 more lessons learned from building Machine Learning systemsXavier Amatriain
1. Machine learning applications at Quora include answer ranking, feed ranking, topic recommendations, user recommendations, and more. A variety of models are used including logistic regression, gradient boosted decision trees, neural networks, and matrix factorization.
2. Implicit signals like watching and clicking tend to be more useful than explicit signals like ratings. However, both implicit and explicit signals combined can better represent long-term goals.
3. The outputs of machine learning models will often become inputs to other models, so models need to be designed with this in mind to avoid issues like feedback loops.
Redefining brain tumor segmentation: a cutting-edge convolutional neural netw...IJECEIAES
Medical image analysis has witnessed significant advancements with deep learning techniques. In the domain of brain tumor segmentation, the ability to
precisely delineate tumor boundaries from magnetic resonance imaging (MRI)
scans holds profound implications for diagnosis. This study presents an ensemble convolutional neural network (CNN) with transfer learning, integrating
the state-of-the-art Deeplabv3+ architecture with the ResNet18 backbone. The
model is rigorously trained and evaluated, exhibiting remarkable performance
metrics, including an impressive global accuracy of 99.286%, a high-class accuracy of 82.191%, a mean intersection over union (IoU) of 79.900%, a weighted
IoU of 98.620%, and a Boundary F1 (BF) score of 83.303%. Notably, a detailed comparative analysis with existing methods showcases the superiority of
our proposed model. These findings underscore the model’s competence in precise brain tumor localization, underscoring its potential to revolutionize medical
image analysis and enhance healthcare outcomes. This research paves the way
for future exploration and optimization of advanced CNN models in medical
imaging, emphasizing addressing false positives and resource efficiency.
International Conference on NLP, Artificial Intelligence, Machine Learning an...gerogepatton
International Conference on NLP, Artificial Intelligence, Machine Learning and Applications (NLAIM 2024) offers a premier global platform for exchanging insights and findings in the theory, methodology, and applications of NLP, Artificial Intelligence, Machine Learning, and their applications. The conference seeks substantial contributions across all key domains of NLP, Artificial Intelligence, Machine Learning, and their practical applications, aiming to foster both theoretical advancements and real-world implementations. With a focus on facilitating collaboration between researchers and practitioners from academia and industry, the conference serves as a nexus for sharing the latest developments in the field.
CHINA’S GEO-ECONOMIC OUTREACH IN CENTRAL ASIAN COUNTRIES AND FUTURE PROSPECTjpsjournal1
The rivalry between prominent international actors for dominance over Central Asia's hydrocarbon
reserves and the ancient silk trade route, along with China's diplomatic endeavours in the area, has been
referred to as the "New Great Game." This research centres on the power struggle, considering
geopolitical, geostrategic, and geoeconomic variables. Topics including trade, political hegemony, oil
politics, and conventional and nontraditional security are all explored and explained by the researcher.
Using Mackinder's Heartland, Spykman Rimland, and Hegemonic Stability theories, examines China's role
in Central Asia. This study adheres to the empirical epistemological method and has taken care of
objectivity. This study analyze primary and secondary research documents critically to elaborate role of
china’s geo economic outreach in central Asian countries and its future prospect. China is thriving in trade,
pipeline politics, and winning states, according to this study, thanks to important instruments like the
Shanghai Cooperation Organisation and the Belt and Road Economic Initiative. According to this study,
China is seeing significant success in commerce, pipeline politics, and gaining influence on other
governments. This success may be attributed to the effective utilisation of key tools such as the Shanghai
Cooperation Organisation and the Belt and Road Economic Initiative.
Discover the latest insights on Data Driven Maintenance with our comprehensive webinar presentation. Learn about traditional maintenance challenges, the right approach to utilizing data, and the benefits of adopting a Data Driven Maintenance strategy. Explore real-world examples, industry best practices, and innovative solutions like FMECA and the D3M model. This presentation, led by expert Jules Oudmans, is essential for asset owners looking to optimize their maintenance processes and leverage digital technologies for improved efficiency and performance. Download now to stay ahead in the evolving maintenance landscape.
The CBC machine is a common diagnostic tool used by doctors to measure a patient's red blood cell count, white blood cell count and platelet count. The machine uses a small sample of the patient's blood, which is then placed into special tubes and analyzed. The results of the analysis are then displayed on a screen for the doctor to review. The CBC machine is an important tool for diagnosing various conditions, such as anemia, infection and leukemia. It can also help to monitor a patient's response to treatment.
4. INTRODUCTION TO ML
● GOOGLE DEF :
Machine learning (ML) is a subfield of artificial intelligence focused on
training machine learning algorithms with data sets to produce machine learning
models capable of performing complex tasks, such as sorting images, forecasting sales, or
analyzing big data.
● MOST PREFERRED DEF :
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 T, as measured
by P , improves with experience E.
5. AI vs ML vs DL :
FOR MORE INFO :
https://pythongeeks.org/ai-vs-data-science-vs-deep-learning-vs-ml/
15. GOOGLE COLABORATORY :
Google Colab is a Jupyter notebook environment that runs completely
on a cloud. It handles all the setup and configuration required for your program.
So that you can start writing your first program.
Colaboratory, or “Colab” for short, is a product from Google
Research. Colaballows anybody to write and execute arbitrary python code
through the browser, and is especially well suited to machine learning, data
analysis and education.
16. How to run a code in Google Colab?
Running code in Google Colab is as easy as opening any website.
It requires just 2 steps.
1. Sign into Google colab.
2. Create a new notebook.
To Sign in to google colab, you need to go to Google Colab url:
https://colab.research.google.com
19. You can create/open a notebook from:
1.A recent notebook you have created.
2.A notebook you have saved in Google drive.
3.Cloning of notebook from the git repositories .
4.Upload from the local storage.
5.Or simply create a new one from the Colab itself.
20. Creating a new notebook in the Colab:
1.Select a new notebook option from pop up window shown in the above
picture. However, you can create a new notebook by going to the file menu
and select “New notebook”.
21. 2. You can change the name of the notebook by double clicking on the file name
at the top left near google drive logo. However, donot change the extension of
the file. The notebook always should have the “ipynb” extension.
22. 3. You are ready to write your python code. There are two option in the main page to write
your code or text.
a) Text is for the information or the description of the code. It is just for display.
b) Code is where you write your code.
You can hover in the center of the screen to get the option to write code or text as show in
figure below.
23. 4. Now you can start writing your code in Google Colab.
24. 5. Click on the play button at the left side of the code editor to run your
program. You can create multiple code editors as shown below.
25. DATA PREPROCESSING :
1. Data Preprocessing
2. Importing the libraries
3. Importing Dataset
4. Handling Missing Data
5. Encoding Categorical Data
6. Encoding independent variables
7. Encoding dependent variables
8. Splitting data into Test set & Training Set
9. Feature Scaling
TASK 3 of D2 : CREATE A COLAB NOTEBOOK OF OWN
28. •Large Set of Libraries
•Code Simplicity
•Platform Independence
•Community Support
•Visualization Ability
•Flexibility
29. PYTHON CONCEPTS :
1. LIST
2. TUPLE
3. SET
4. DICTIONARY
LIST FOR PRACTICE :
https://drive.google.com/drive/folders/1dllsH2PLBR9Cn3z0yDDxxa44tUgMlzj?usp=sharing
30.
31. Here are some of the most commonly used basic Python libraries in AI and ML:
•Pandas for general-purpose data analysis
•NumPy for high-performance scientific calculation and data analysis
•TensorFlow for high-performance numerical computation
•SciPy for advanced computation
•Scikit-learn for handling ML algorithms (like clustering, decision trees, linear and
logistic regressions, and classification)
•Keras for building and designing a neural network
•Matplotlib for data visualization (i.e. developing 2D plots, histograms, charts, and
other forms of visualization)
•Natural Language Toolkit (NLTK) for working with computational linguistics and
natural language recognition& processing
•Scikit-image for image processing& analysis
•PyBrain for implementing machine learning algorithms and architectures ranging from
areas such as supervised learning and reinforcement learning
•StatsModels for data exploration, statistical model estimation, and performing
statistical tests
32. PRACTICE PRACTIEC PRACTICE !!!!
LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
34. Data preprocessing is a process of preparing the raw data and making it
suitable for a machine learning model. It is the first and crucial step while
creating a machine learning model.
When creating a machine learning project, it is not always a case that we
come across the clean and formatted data. And while doing any operation
with data, it is mandatory to clean it and put in a formatted way. So for
this, we use data preprocessing task.
WHAT IS DATA PREPROCESS
35. PRACTICE PRACTIEC PRACTICE !!!!
LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
40. Linear regression algorithm shows a linear relationship between a dependent
(y) and one or more independent (y) variables, hence called as linear
regression. Since linear regression shows the linear relationship, which
means it finds how the value of the dependent variable is changing
according to the value of the independent variable.
LINEAR REGRESSION :
41. Mathematically, we can represent a linear regression as:
y= a0+a1x+ ε
Here,
Y= Dependent Variable (Target Variable)
X= Independent Variable (predictor Variable)
a0= intercept of the line (Gives an additional degree of freedom)
a1 = Linear regression coefficient (scale factor to each input value).
ε = random error
The values for x and y variables are training datasets for Linear Regression
model representation.
42. Types of Linear Regression
Linear regression can be further divided into two types of the
algorithm:
1. SIMPLE LINEAR REGRESSION :
If a single independent variable is used to predict the value of a
numerical dependent variable, then such a Linear Regression
algorithm is called Simple Linear Regression.
2. MULTIPLE LINEAR REGRESSION :
If more than one independent variable is used to predict the value
of a numerical dependent variable, then such a Linear Regression
algorithm is called Multiple Linear Regression.
43. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
49. WHAT IS DECISION TREE ALGORITHM ?
Decision tree builds regression or classification models in the
form of a tree structure. It breaks down a dataset into smaller and smaller
subsets while at the same time an associated decision tree is
incrementally developed. The final result is a tree with decision
nodes and leaf nodes. A decision node (e.g., Outlook) has two or more
branches (e.g., Sunny, Overcast and Rainy), each representing values for
the attribute tested. Leaf node (e.g., Hours Played) represents a decision
on the numerical target. The topmost decision node in a tree which
corresponds to the best predictor called root node. Decision trees can
handle both categorical and numerical data.
50.
51. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
53. Support Vector Machines
● A Support Vector Machine (SVM) is a classifier that
tries to maximize the margin between training data
and the classification boundary (the plane defined by
𝑋𝛽 = 0)
54. Support Vector Machines
● The idea is that maximizing the margin maximizes
the chance that classification will be correct on
new data. We assume the new data of each class is
near the training data of that type.
55. SVM Training
SVMs can be trained using SGD. Recall that the Logistic
gradient was (this time assuming 𝒚𝒊 ∈ −𝟏, +𝟏 ):
𝑑𝐴
𝑑𝛽
=
𝑖=1
𝑁
𝑦𝑖𝑝𝑖 1 − 𝑝𝑖 𝑋𝑖
The SVM gradient can be defined as (here 𝑝𝑖 = 𝑋𝑖𝛽)
𝑑𝐴
𝑑𝛽
=
𝑖=1
𝑁
if 𝑝𝑖𝑦𝑖 < 1 then 𝑦𝑖𝑋𝑖 else 0
The expression 𝑝𝑖𝑦𝑖 < 1 tests whether the point 𝑋𝑖 is in
the margin, and if so adds it with sign 𝑦𝑖
. It ignores
other points.
Both methods weight points “near the middle” with
sign 𝒚𝒊
.
56. SVM Training
This SGD training method (called Pegasos) is much faster
and competitive with Logistic Regression.
Its also capable of training in less than one pass over a
dataset.
LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
58. Logistic Regression
● Logistic regression is probably the most widely used
general-purpose classifier.
● Its very scalable and can be very fast to train. It’s
used for
○ Spam filtering
○ News message classification
○ Web site classification
○ Product classification
○ Most classification problems with large, sparse
feature sets.
● The only caveat is that it can overfit on very sparse
data, so its often used with Regularization
59. Logistic Regression
● NOTE : Regression (predicting a real value) and
classification (predicting a discrete value).
● Logistic regression is designed as a binary classifier
(output say {0,1}) but actually outputs the probability
that the input instance is in the “1” class.
● A logistic classifier has the form:
𝑝 𝑋 =
1
1 + exp −𝑋𝛽
where 𝑋 = 𝑋1, … , 𝑋𝑛 is a vector of features.
60. Logistic Regression
● Logistic regression maps the “regression” value −𝑋𝛽
in
(-,) to the range [0,1] using a “logistic” function:
𝑝 𝑋 =
1
1 + exp −𝑋𝛽
● i.e. the logistic function maps any value on the real
line to a probability in the range [0,1]
61.
62. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
63. DAY 10
INTRODUCTION TO CLASSIFICATION AND DIFFERENT
TYPES OF CLASSIFICATION ALGORITHMS AND
MODEL SELECTION
64. INTRODUCTION TO CLASSIFICATION :
Classification may be defined as the process of predicting
class or category from observed values or given data points. The
categorized output can have the form such as “Black” or “White”
or “spam” or “no spam”.
Mathematically, classification is the task of
approximating a mapping function (f) from input variables (X) to
output variables (Y). It is basically belongs to the supervised
machine learning in which targets are also provided along with the
input data set.
65. TYPES OF LEARNERS IN CLASSIFICATION :
We have two types of learners in respective to classification
problems −
1. Lazy Learners
As the name suggests, such kind of learners waits for the testing data to be
appeared after storing the training data. Classification is done only after
getting the testing data. They spend less time on training but more time on
predicting. Examples of lazy learners are K-nearest neighbor and case-based
reasoning.
2. Eager Learners
As opposite to lazy learners, eager learners construct classification model
without waiting for the testing data to be appeared after storing the training
data. They spend more time on training but less time on predicting. Examples
of eager learners are Decision Trees, Naïve Bayes and Artificial Neural
Networks (ANN).
66.
67. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
69. •Naïve Bayes algorithm is a supervised learning algorithm, which is
based on Bayes theorem and used for solving classification problems.
•It is mainly used in text classification that includes a high-dimensional
training dataset.
•It is a probabilistic classifier, which means it predicts on the basis of
the probability of an object.
•Naïve: It is called Naïve because it assumes that the occurrence of a
certain feature is independent of the occurrence of other features. Such
as if the fruit is identified on the bases of color, shape, and taste, then
red, spherical, and sweet fruit is recognized as an apple. Hence each
feature individually contributes to identify that it is an apple without
depending on each other.
•Bayes: It is called Bayes because it depends on the principle of Bayes'
Theorem.
70. Bayes’ Theorem
P(A|B) = probability of A given that B is true.
P(A|B) =
In practice we are most interested in dealing with events e
and data D.
e = “I have a cold”
D = “runny nose,” “watery eyes,” “coughing”
P(e|D)=
So Bayes’ theorem is “diagnostic”.
P(B|A)P(A)
P(B)
P(D|e)P(e)
P(D)
72. Bayes’ Theorem
D = Data, e = some event
P(e|D) =
P(e) is called the prior probability of e. Its what we know (or
think we know) about e with no other evidence.
P(D|e) is the conditional probability of D given that e
happened, or just the likelihood of D. This can often be
measured or computed precisely – it follows from your
model assumptions.
P(e|D) is the posterior probability of e given D. It’s the
answer we want, or the way we choose a best answer.
You can see that the posterior is heavily colored by the prior,
so Bayes’ has a GIGO liability. e.g. its not used to test
hypotheses
P(D|e)P(e)
P(D)
73. Naïve Bayes Classifier
Let’s assume we have an instance (e.g. a document d) with
a set of features 𝑋1, … , 𝑋𝑘 and a set of classes 𝑐𝑗 to
which the document might belong.
We want to find the most likely class that the document
belongs to, given its features.
The joint probability of the class and features is:
Pr 𝑋1, … , 𝑋𝑘, 𝑐𝑗
74. Naïve Bayes Classifier
Key Assumption: (Naïve) the features are generated
independently given 𝑐𝑗. Then the joint probability factors:
Pr 𝑋, 𝑐𝑗 = Pr 𝑋1, … , 𝑋𝑘 | 𝑐𝑗 Pr 𝑐𝑗 = Pr 𝑐𝑗
𝑖=1
𝑘
Pr 𝑋𝑖|𝑐𝑗
We would like to figure out the most likely class for (i.e. to
classify) the document, which is the 𝑐𝑗 which maximizes:
Pr 𝑐𝑗 | 𝑋1, … , 𝑋𝑘
75. Naïve Bayes Classifier
Now from Bayes we know that:
Pr 𝑐𝑗 | 𝑋1, … , 𝑋𝑘 = Pr 𝑋1, … , 𝑋𝑘 | 𝑐𝑗 Pr 𝑐𝑗 / Pr 𝑋1, … , 𝑋𝑘
But to choose the best 𝑐𝑗, we can ignore Pr 𝑋1, … , 𝑋𝑘 since
it’s the same for every class. So we just have to maximize:
Pr 𝑋1, … , 𝑋𝑘 | 𝑐𝑗 Pr 𝑐𝑗
So finally we pick the category 𝑐𝑗 that maximizes:
Pr 𝑋1, … , 𝑋𝑘 | 𝑐𝑗 Pr 𝑐𝑗 = Pr 𝑐𝑗
𝑖=1
𝑘
Pr 𝑋𝑖|𝑐𝑗
76. Naïve Bayes Classifier
Now from Bayes we know that:
Pr 𝑐𝑗 | 𝑋1, … , 𝑋𝑘 = Pr 𝑋1, … , 𝑋𝑘 | 𝑐𝑗 Pr 𝑐𝑗 / Pr 𝑋1, … , 𝑋𝑘
But to choose the best 𝑐𝑗, we can ignore Pr 𝑋1, … , 𝑋𝑘 since
it’s the same for every class. So we just have to maximize:
Pr 𝑋1, … , 𝑋𝑘 | 𝑐𝑗 Pr 𝑐𝑗
So finally we pick the category 𝑐𝑗 that maximizes:
Pr 𝑋1, … , 𝑋𝑘 | 𝑐𝑗 Pr 𝑐𝑗 = Pr 𝑐𝑗
𝑖=1
𝑘
Pr 𝑋𝑖|𝑐𝑗
A A A
B B B
77. Data for Naïve Bayes
In order to find the best class, we need two pieces of data:
• Pr 𝑐𝑗 the prior probability for the class 𝑐𝑗.
• Pr 𝑋𝑖|𝑐𝑗 the conditional probability of the feature 𝑋𝑖
given the class 𝑐𝑗.
78. Advantage and Disadvantage of NB Classifiers
● Simple and fast. Depend only on term frequency data
for the classes. One shot, no iteration.
● Very well-behaved numerically. Term weight
depends only on frequency of that term. Decoupled
from other terms.
● Can work very well with sparse data, where
combinations of dependent terms are rare.
● Subject to error and bias when term probabilities are
not independent (e.g. URL prefixes).
● Can’t model patterns in the data.
● Typically not as accurate as other methods.
79. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
81. •K-Nearest Neighbour is one of the simplest Machine Learning
algorithms based on Supervised Learning technique.
•K-NN algorithm assumes the similarity between the new
case/data and available cases and put the new case into the
category that is most similar to the available categories.
•K-NN algorithm stores all the available data and classifies a new
data point based on the similarity. This means when new data
appears then it can be easily classified into a well suite category
by using K- NN algorithm.
K-NN is a non-parametric algorithm, which means it does not
make any assumption on underlying data.
•It is also called a lazy learner algorithm because it does not
learn from the training set immediately instead it stores the
dataset and at the time of classification, it performs an action on
the dataset.
82. •Example: Suppose, we have an image of a creature
that looks similar to cat and dog, but we want to know
either it is a cat or dog. So for this identification, we can
use the KNN algorithm, as it works on a similarity
measure. Our KNN model will find the similar features of
the new data set to the cats and dogs images and
based on the most similar features it will put it in either
cat or dog category.
83.
84. The K-NN working can be explained on the basis of the
below algorithm:
•Step-1: Select the number K of the neighbors
•Step-2: Calculate the Euclidean distance of K number
of neighbors
•Step-3: Take the K nearest neighbors as per the
calculated Euclidean distance.
•Step-4: Among these k neighbors, count the number of
the data points in each category.
•Step-5: Assign the new data points to that category for
which the number of the neighbor is maximum.
•Step-6: Our model is ready.
85.
86. How to select the value of K in the K-NN Algorithm?
Below are some points to remember while selecting the value of K in the
K-NN algorithm:
•There is no particular way to determine the best value for "K", so we
need to try some values to find the best out of them. The most preferred
value for K is 5.
•A very low value for K such as K=1 or K=2, can be noisy and lead to the
effects of outliers in the model.
•Large values for K are good, but it may find some difficulties.
LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
88. •Decision Tree is a Supervised learning technique that can be
used for both classification and Regression problems, but mostly
it is preferred for solving Classification problems. It is a tree-
structured classifier, where internal nodes represent the
features of a dataset, branches represent the decision
rules and each leaf node represents the outcome.
•It is a graphical representation for getting all the possible
solutions to a problem/decision based on given conditions.
89. Example: Suppose there is a candidate who has a job offer and
wants to decide whether he should accept the offer or Not. So, to
solve this problem, the decision tree starts with the root node
(Salary attribute by ASM). The root node splits further into the next
decision node (distance from the office) and one leaf node based
on the corresponding labels. The next decision node further gets
split into one decision node (Cab facility) and one leaf node. Finally,
the decision node splits into two leaf nodes (Accepted offers and
Declined offer). Consider the below diagram:
90.
91. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
93. Support Vector Machine or SVM is one of the most
popular Supervised Learning algorithms, which is
used for Classification as well as Regression
problems. However, primarily, it is used for
Classification problems in Machine Learning.
“The goal of the SVM algorithm is to create the best
line or decision boundary that can segregate n-
dimensional space into classes so that we can
easily put the new data point in the correct category
in the future. This best decision boundary is called a
hyperplane.”
94.
95. Example: SVM can be understood with the example that
we have used in the KNN classifier. Suppose we see a
strange cat that also has some features of dogs, so if we
want a model that can accurately identify whether it is a cat
or dog, so such a model can be created by using the SVM
algorithm. We will first train our model with lots of images of
cats and dogs so that it can learn about different features of
cats and dogs, and then we test it with this strange
creature. So as support vector creates a decision boundary
between these two data (cat and dog) and choose extreme
cases (support vectors), it will see the extreme case of cat
and dog. On the basis of the support vectors, it will classify
it as a cat. Consider the below diagram:
96.
97. Types of SVM
SVM can be of two types:
•Linear SVM: Linear SVM is used for linearly separable data, which
means if a dataset can be classified into two classes by using a single
straight line, then such data is termed as linearly separable data, and
classifier is used called as Linear SVM classifier.
•Non-linear SVM: Non-Linear SVM is used for non-linearly separated
data, which means if a dataset cannot be classified by using a straight
line, then such data is termed as non-linear data and classifier used is
called as Non-linear SVM classifier.
“SVM algorithm can be used for Face detection, image classification,
text categorization, etc”.
98. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
100. Clustering in Machine Learning :
Clustering or cluster analysis is a machine learning technique, which groups the
unlabelled dataset. It can be defined as "A way of grouping the data points into
different clusters, consisting of similar data points. The objects with the
possible similarities remain in a group that has less or no similarities with
another group."
101. What is K-Means Algorithm?
K-Means Clustering is an Unsupervised Learning algorithm, which groups the unlabeled
dataset into
different clusters. Here K defines the number of pre-defined clusters that need to be created in
the process, as if K=2, there will be two clusters, and for K=3, there will be three clusters, and
so on.
It is an iterative algorithm that divides the unlabeled dataset into
k different clusters in such a way that each dataset belongs only one
group that has similar properties.
It is a centroid-based algorithm, where each cluster is associated
with a centroid. The main aim of this algorithm is to minimize the
sum of distances between the data point and their corresponding clusters.
102. The k-means clustering algorithm mainly performs two tasks:
•Determines the best value for K center points or centroids by an iterative process.
•Assigns each data point to its closest k-center. Those data points which are near
to the particular k-center, create a cluster.
103. How does the K-Means Algorithm Work?
The working of the K-Means algorithm is explained in the below steps:
Step-1: Select the number K to decide the number of clusters.
Step-2: Select random K points or centroids. (It can be other from the input dataset).
Step-3: Assign each data point to their closest centroid, which will form the predefined
Step-4: Calculate the variance and place a new centroid of each cluster.
Step-5: Repeat the third steps, which means reassign each datapoint to the new closest
Step-6: If any reassignment occurs, then go to step-4 else go to FINISH.
Step-7: The model is ready.
104. LINK FOR PRACTICE :
https://drive.google.com/drive/folders/1h4-
SVu9WLFfOWL8N4X2tZ_mEy9c5wIbw?usp=sharing
PRACTICE PRACTIEC PRACTICE !!!!
106. Hierarchical clustering is another unsupervised machine learning algorithm,
which is used to group the unlabeled datasets into a cluster and also known
as hierarchical cluster analysis or HCA.
In this algorithm, we develop the hierarchy of clusters in the form of a tree, and
this tree-shaped structure is known as the dendrogram.
The hierarchical clustering technique has two approaches:
1.Agglomerative: Agglomerative is a bottom-up approach, in which the
algorithm starts with taking all data points as single clusters and merging them
until one cluster is left.
2.Divisive: Divisive algorithm is the reverse of the agglomerative algorithm as it
is a top-down approach.