Machine Learning Algorithms | Machine Learning Tutorial | Data Science Tutorial | Edureka
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This Edureka Machine Learning Algorithms tutorial will help you understand all the basics of machine learning and different kind of algorithms along with examples. This tutorial is ideal for both beginners as well as professionals who want to learn or brush up their Data Science concepts. Below are the topics covered in this tutorial: 1. What is an Algorithm? 2. What is Machine Learning? 3. How is a problem solved using Machine Learning? 4. Types of Machine Learning 5. Machine Learning Algorithms
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Introduction to Machine Learning
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Machine LearningKumar P Machine learning helps predict behavior and recognize patterns that humans cannot by learning from data without relying on programmed rules. It is an algorithmic approach that differs from statistical modeling which formalizes relationships through mathematical equations. Machine learning is a part of the broader field of artificial intelligence which aims to develop systems that can act and respond intelligently like humans. The machine learning workflow involves collecting and preprocessing data, selecting algorithms, training models, and evaluating performance. Common machine learning algorithms include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Popular tools for machine learning include Python, R, TensorFlow, and Spark.
Introduction to ML (Machine Learning)
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Machine Learning With Logistic Regression
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Data science
Data scienceMohamed Loey The document outlines a data science roadmap that covers fundamental concepts, statistics, programming, machine learning, text mining, data visualization, big data, data ingestion, data munging, and tools. It provides the percentage of time that should be spent on each topic, and lists specific techniques in each area, such as linear regression, decision trees, and MapReduce in big data.
Introduction to data science
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Data science applications and usecases
Data science applications and usecasesSreenatha Reddy K R The document provides an overview of data science applications and use cases. It defines data science as using computer science, statistics, machine learning and other techniques to analyze data and create data products to help businesses make better decisions. It discusses big data challenges, the differences between data science and software engineering, and key areas of data science competence including data analytics, engineering, domain expertise and data management. Finally, it outlines several common data science applications and use cases such as recommender systems, credit scoring, dynamic pricing, customer churn analysis and fraud detection with examples of how each works and real world cases.
Data Science Training | Data Science For Beginners | Data Science With Python...
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This Data Science presentation will cover the following topics:
1. What is Data Science?
2. Who is a Data Scientist?
3. What does a Data Scientist do?
This Data Science with Python course will establish your mastery of data science and analytics techniques using Python. With this Python for Data Science Course, you’ll learn the essential concepts of Python programming and become an expert in data analytics, machine learning, data visualization, web scraping and natural language processing. Python is a required skill for many data science positions, so jumpstart your career with this interactive, hands-on course.
Why learn Data Science?
Data Scientists are being deployed in all kinds of industries, creating a huge demand for skilled professionals. A data scientist is the pinnacle rank in an analytics organization. Glassdoor has ranked data scientist first in the 25 Best Jobs for 2016, and good data scientists are scarce and in great demand. As a data you will be required to understand the business problem, design the analysis, collect and format the required data, apply algorithms or techniques using the correct tools, and finally make recommendations backed by data.
You can gain in-depth knowledge of Data Science by taking our Data Science with python certification training course. With Simplilearn’s Data Science certification training course, you will prepare for a career as a Data Scientist as you master all the concepts and techniques. Those who complete the course will be able to:
1. Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
Install the required Python environment and other auxiliary tools and libraries
2. Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
3. Perform high-level mathematical computing using the NumPy package and its largelibrary of mathematical functions.
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Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...
Machine Learning Tutorial Part - 1 | Machine Learning Tutorial For Beginners ...Simplilearn This presentation on Machine Learning will help you understand why Machine Learning came into picture, what is Machine Learning, types of Machine Learning, Machine Learning algorithms with a detailed explanation on linear regression, decision tree & support vector machine and at the end you will also see a use case implementation where we classify whether a recipe is of a cupcake or muffin using SVM algorithm. Machine learning is a core sub-area of artificial intelligence; it enables computers to get into a mode of self-learning without being explicitly programmed. When exposed to new data, these computer programs are enabled to learn, grow, change, and develop by themselves. So, to put simply, the iterative aspect of machine learning is the ability to adapt to new data independently. Now, let us get started with this Machine Learning presentation and understand what it is and why it matters.
Below topics are explained in this Machine Learning presentation:
1. Why Machine Learning?
2. What is Machine Learning?
3. Types of Machine Learning
4. Machine Learning Algorithms
- Linear Regression
- Decision Trees
- Support Vector Machine
5. Use case: Classify whether a recipe is of a cupcake or a muffin using SVM
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars. This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
We recommend this Machine Learning training course for the following professionals in particular:
1. Developers aspiring to be a data scientist or Machine Learning engineer
2. Information architects who want to gain expertise in Machine Learning algorithms
3. Analytics professionals who want to work in Machine Learning or artificial intelligence
4. Graduates looking to build a career in data science and Machine Learning
Learn more at: https://www.simplilearn.com/
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...
What Is Data Science? Data Science Course - Data Science Tutorial For Beginne...Edureka! This Edureka Data Science course slides will take you through the basics of Data Science - why Data Science, what is Data Science, use cases, BI vs Data Science, Data Science tools and Data Science lifecycle process. This is ideal for beginners to get started with learning data science.
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Presentation on supervised learning
Presentation on supervised learningTonmoy Bhagawati This document discusses computational intelligence and supervised learning techniques for classification. It provides examples of applications in medical diagnosis and credit card approval. The goal of supervised learning is to learn from labeled training data to predict the class of new unlabeled examples. Decision trees and backpropagation neural networks are introduced as common supervised learning algorithms. Evaluation methods like holdout validation, cross-validation and performance metrics beyond accuracy are also summarized.
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...
Logistic Regression | Logistic Regression In Python | Machine Learning Algori...Simplilearn This Logistic Regression Presentation will help you understand how a Logistic Regression algorithm works in Machine Learning. In this tutorial video, you will learn what is Supervised Learning, what is Classification problem and some associated algorithms, what is Logistic Regression, how it works with simple examples, the maths behind Logistic Regression, how it is different from Linear Regression and Logistic Regression applications. At the end, you will also see an interesting demo in Python on how to predict the number present in an image using Logistic Regression.
Below topics are covered in this Machine Learning Algorithms Presentation:
1. What is supervised learning?
2. What is classification? what are some of its solutions?
3. What is logistic regression?
4. Comparing linear and logistic regression
5. Logistic regression applications
6. Use case - Predicting the number in an image
What is Machine Learning: Machine Learning is an application of Artificial Intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
- - - - - - - -
About Simplilearn Machine Learning course:
A form of artificial intelligence, Machine Learning is revolutionizing the world of computing as well as all people’s digital interactions. Machine Learning powers such innovative automated technologies as recommendation engines, facial recognition, fraud protection and even self-driving cars.This Machine Learning course prepares engineers, data scientists and other professionals with knowledge and hands-on skills required for certification and job competency in Machine Learning.
- - - - - - -
Why learn Machine Learning?
Machine Learning is taking over the world- and with that, there is a growing need among companies for professionals to know the ins and outs of Machine Learning
The Machine Learning market size is expected to grow from USD 1.03 Billion in 2016 to USD 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1% during the forecast period.
- - - - - -
What skills will you learn from this Machine Learning course?
By the end of this Machine Learning course, you will be able to:
1. Master the concepts of supervised, unsupervised and reinforcement learning concepts and modeling.
2. Gain practical mastery over principles, algorithms, and applications of Machine Learning through a hands-on approach which includes working on 28 projects and one capstone project.
3. Acquire thorough knowledge of the mathematical and heuristic aspects of Machine Learning.
4. Understand the concepts and operation of support vector machines, kernel SVM, naive bayes, decision tree classifier, random forest classifier, logistic regression, K-nearest neighbors, K-means clustering and more.
5. Be able to model a wide variety of robust Machine Learning algorithms including deep learning, clustering, and recommendation systems
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Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...
Decision Tree Algorithm With Example | Decision Tree In Machine Learning | Da...Simplilearn The document discusses decision trees and how they work. It begins with explaining what a decision tree is - a tree-shaped diagram used to determine a course of action, with each branch representing a possible decision. It then provides examples of using a decision tree to classify vegetables and animals based on their features. The document also covers key decision tree concepts like entropy, information gain, leaf nodes, decision nodes, and the root node. It demonstrates how a decision tree is built by choosing splits that maximize information gain. Finally, it presents a use case of using a decision tree to predict loan repayment.
Data Science
Data ScienceAmit Singh The document discusses data science, defining it as a field that employs techniques from many areas like statistics, computer science, and mathematics to understand and analyze real-world phenomena. It explains that data science involves collecting, processing, and analyzing large amounts of data to discover patterns and make predictions. The document also notes that data science is an in-demand field that is expected to continue growing significantly in the coming years.
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Machine Learning Interview Questions and Answers
Machine Learning Interview Questions and AnswersSatyam Jaiswal Practice Best Machine Learning Interview Questions and Answers for the best preparation of the machine learning interview. these questions are very popular and asked various times in machine learning interview.
Introduction to Machine Learning
Introduction to Machine LearningSujith Jayaprakash This document introduces machine learning concepts through a webinar presentation. It begins with introductions and definitions of machine learning from Wikipedia and O'Reilly. It then provides examples of artificial intelligence and machine learning applications. The main machine learning concepts covered include supervised learning, unsupervised learning, and reinforcement learning. Supervised learning is described as learning from labeled examples, while unsupervised learning finds patterns in unlabeled data. Reinforcement learning involves an agent interacting with an environment and receiving rewards or punishments to achieve goals. Examples of reinforcement learning applications include autonomous vehicles and game playing agents. In closing, the presenter thanks college administrators and attendees for their participation.
An Introduction to Machine Learning
An Introduction to Machine LearningVedaj Padman This was part of my inaugural lecture of Summer Internship on Machine Learning at NMAM Institute of Technology, Nitte on 7th June, 2018. A lot more than what was on this presentation was discussed. We spoke on the ethics of choices we make as developers, socio-cultural impact of AI and ML and the political repercussions of deploying ML and AI.
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machinecanthink-160226155704.pdfPranavPatil822557 This document provides an overview of machine learning. It begins with an introduction and discusses the basics, types (supervised, unsupervised, reinforcement learning), technologies, applications, and vision for the next few years. Key points covered include definitions of machine learning, examples of applications (search engines, spam filters, personalized recommendations), and descriptions of different problem types (classification, regression, clustering) and learning approaches (decision trees, neural networks, Bayesian methods).
Data Science Tutorial | Introduction To Data Science | Data Science Training ...
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Machine Can Think
Machine Can ThinkRahul Jaiman This document provides an overview of machine learning. It begins with an introduction and definitions, explaining that machine learning allows computers to learn without being explicitly programmed by exploring algorithms that can learn from data. The document then discusses the different types of machine learning problems including supervised learning, unsupervised learning, and reinforcement learning. It provides examples and applications of each type. The document also covers popular machine learning techniques like decision trees, artificial neural networks, and frameworks/tools used for machine learning.
Sentiment Analysis | Machine Learning Algorithms | Data Science Tutorial | Ed...
Sentiment Analysis | Machine Learning Algorithms | Data Science Tutorial | Ed...Edureka! This Edureka Sentiment Analysis tutorial will help you understand all the basics of Sentiment Analysis algorithm along with examples. This tutorial also has an interesting demo on Sentiment Analysis in R - El Clasico Sentiment Analysis. Below are the topics covered in this tutorial:
1. What is Machine Learning?
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Machine Learning by Rj
Machine Learning by RjShree M.L.Kakadiya MCA mahila college, Amreli This document provides an overview of machine learning presented by Mr. Raviraj Solanki. It discusses topics like introduction to machine learning, model preparation, modelling and evaluation. It defines key concepts like algorithms, models, predictor variables, response variables, training data and testing data. It also explains the differences between human learning and machine learning, types of machine learning including supervised learning and unsupervised learning. Supervised learning is further divided into classification and regression problems. Popular algorithms for supervised learning like random forest, decision trees, logistic regression, support vector machines, linear regression, regression trees and more are also mentioned.
introduction to machine learning
introduction to machine learningJohnson Ubah 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.
INTERNSHIP ON MAcHINE LEARNING.pptx
INTERNSHIP ON MAcHINE LEARNING.pptxsrikanthkallem1 The document discusses machine learning methods including supervised learning, unsupervised learning, and reinforcement learning. It provides examples of how each method is used, such as using historical data for prediction in supervised learning and organizing unlabeled data in unsupervised learning. Random forest, an ensemble supervised learning algorithm, is also summarized. It states random forest combines decision trees for improved performance and discusses its use in sectors like banking, medicine, land use, and marketing.
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With increasing data availability, the new trend in the industry demands not just data collection but making an ample sense of acquired data - thereby, the concept of Data Analytics.
Taking it a step further to further make futuristic prediction and realistic inferences - the concept
of Machine Learning.
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Importance of Digital Marketing | Edureka
Importance of Digital Marketing | EdurekaEdureka! YouTube Link:https://youtu.be/phPCkkWT76k
*** Edureka Digital Marketing Course: https://www.edureka.co/post-graduate/digital-marketing-certification***
This Edureka PPT on "Top 10 Reasons to Learn Digital Marketing" will help you understand why you should take up Digital Marketing
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RPA in 2020 | Edureka
RPA in 2020 | EdurekaEdureka! YouTube Link: https://youtu.be/R132INtDg9k
** RPA Training: https://www.edureka.co/robotic-process-automation-training**
This PPT on RPA in 2020 will provide a glimpse of the accomplishments and benefits provided by RPA. Also, it will list out the new changes and technologies that will collaborate with RPA in 2020.
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Email Notifications in Jenkins | Edureka
Email Notifications in Jenkins | EdurekaEdureka! YouTube Link: https://youtu.be/mb8WOHejlT8
**DevOps Certification Courses - https://www.edureka.co/devops-certification-training **
This PPT shows how to configure Jenkins to receive email notifications. It also includes a demo that shows how to do it in 6 simple steps in the Windows machine.
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EA Algorithm in Machine Learning | Edureka
EA Algorithm in Machine Learning | EdurekaEdureka! YouTube Link: https://youtu.be/DIADjJXrgps
** Machine Learning Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka PPT on 'EM Algorithm In Machine Learning' covers the EM algorithm along with the problem of latent variables in maximum likelihood and Gaussian mixture model.
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AWS Cloud Practitioner Tutorial | EdurekaEdureka! YouTube Link: https://youtu.be/0djPrlaxx_U
Edureka AWS Architect Certification Training - https://www.edureka.co/aws-certification-training
This Edureka PPT on AWS Cloud Practitioner will provide a complete guide to your AWS Cloud Practitioner Certification exam. It will explain the exam details, objectives, why you should get certified and also how AWS certification will help your career.
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Blue Prism Top Interview Questions | Edureka
Blue Prism Top Interview Questions | EdurekaEdureka! YouTube Link: https://youtu.be/ykbRdUNIbyQ
** RPA Training: https://www.edureka.co/robotic-process-automation-certification-courses**
This PPT on Blue Prism Interview Questions will cover the Top 50 Blue Prism related questions asked in your interviews.
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Big Data on AWS Tutorial | Edureka 
Big Data on AWS Tutorial | Edureka Edureka! YouTube Link: https://youtu.be/ge4qhkl9uKg
AWS Architect Certification Training: https://www.edureka.co/aws-certification-training
This PPT will help you in understanding how AWS deals smartly with Big Data. It also shows how AWS can solve Big Data challenges with ease.
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A star algorithm | A* Algorithm in Artificial Intelligence | Edureka
A star algorithm | A* Algorithm in Artificial Intelligence | EdurekaEdureka! YouTube Link: https://youtu.be/amlkE0g-YFU
** Artificial Intelligence and Deep Learning: https://www.edureka.co/ai-deep-learni... **
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Kubernetes Installation on Ubuntu | Edureka
Kubernetes Installation on Ubuntu | EdurekaEdureka! YouTube Link: https://youtu.be/UWg3ORRRF60
Kubernetes Certification: https://www.edureka.co/kubernetes-certification
This Edureka PPT will help you set up a Kubernetes cluster having 1 master and 1 node. The detailed step by step instructions is demonstrated in this PPT.
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Introduction to DevOps | Edureka
Introduction to DevOps | EdurekaEdureka! YouTube Link: https://youtu.be/GJQ36pIYbic
DevOps Training: https://www.edureka.co/devops-certification-training
This Edureka DevOps Tutorial for Beginners talks about What is DevOps and how it works. You will learn about several DevOps tools (Git, Jenkins, Docker, Puppet, Ansible, Nagios) involved at different DevOps stages such as version control, continuous integration, continuous delivery, continuous deployment, continuous monitoring.
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Machine Learning Algorithms | Machine Learning Tutorial | Data Science Tutorial | Edureka
- 1. www.edureka.co/data-scienceEdureka’s Data Science Certification Training
- 2. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Agenda for Today’s Session What is an Algorithm? What is Machine Learning? How is a problem solved using Machine Learning? Types of Machine Learning Machine Learning Algorithms Demo
- 3. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is an Algorithm?
- 4. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is an Algorithm? To tell a computer what it has to do, you need a program.
- 5. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is an Algorithm? To tell a computer what it has to do, you need a program. A program is nothing but logic in some language’s syntax
- 6. www.edureka.co/data-scienceEdureka’s Data Science Certification Training logic What is an Algorithm? To tell a computer what it has to do, you need a program. This logic is what an Algorithm is
- 7. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is an Algorithm? A process or set of rules to be followed in calculations or other problem-solving operations, especially by a computer
- 8. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Algorithm - Example Start Initialize X = 0 Increment X by 1 Print X X< 20 END NO YES This is a simple algorithm to print numbers from 1 to 20.
- 9. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Machine Learning?
- 10. www.edureka.co/data-scienceEdureka’s Data Science Certification Training What is Machine Learning? Machine learning is a type of artificial intelligence (AI) that provides computers with the ability to learn without being explicitly programmed. Machine learning focuses on the development of computer programs that can change when exposed to new data.
- 11. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Machine Learning Types
- 12. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Categories of Algorithms Supervised Learning Reinforcement Learning Unsupervised Learning Types of Learning
- 13. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Supervised Learning Supervised Learning Unsupervised Learning Reinforcement Learning Let’s take an example here. Say you are a teacher, and your way of teaching is, To teach by example, i.e for every problem in their life you are providing solutions to them, this type of learning is called supervised learning. Let’s take the same example forward: Supervised learning is a type of machine learning algorithm that uses a known dataset (called the training dataset) to make predictions. The training dataset includes input data and response values. From it, the supervised learning algorithm seeks to build a model that can make predictions of the response values for a new dataset. Teaching by Example
- 14. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Unsupervised Learning Supervised Learning Unsupervised Learning Reinforcement Learning When your kids are taking decisions out of their own understanding, this type of learning would be Unsupervised Learning. Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. Self Learning
- 15. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Reinforcement learning is an area of machine learning inspired by behaviorist psychology, concerned with how software agents ought to take actions in an environment so as to maximize some notion of cumulative reward. Reinforcement Learning Supervised Learning Unsupervised Learning Reinforcement Learning If a new situation comes up, the kid will take actions on his own i.e from his past experiences, but as a parent towards the end of an action you can tell him whether he did good or not. Good or Bad?
- 16. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How a problem is solved using Machine Learning?
- 17. www.edureka.co/data-scienceEdureka’s Data Science Certification Training How a problem is solved using Machine Learning? We take a top down approach to answer the same: Classification Algorithm Anomaly Detection Algorithm Regression Algorithms Clustering Algorithms Reinforcement Learning Q1. Q2. Q4. Q3. Q5. Is this A or B? Is this weird? How much or how many? How is this organized? What should I do next? These are the 5 questions which can be answered in data science. These algorithms are fitted into three types of categories, which are the following:
- 18. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Machine Learning Algorithms
- 19. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Classification Algorithms Classification Algorithms are used to classify a record. It is used for questions which can have only a limited number of answers. For Example: When you have only two choices, its called 2 class Classification, if you have more than 2 choices its called Multi Class Classification Is it cold? Will you go to work today? Yes, No or Maybe Yes or No Yes, No or Maybe
- 20. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Anomaly Detection Algorithms It analyzes a certain pattern and alerts you whenever there is change in the pattern. For Example: Anomaly In real life, your credit card company uses these anomaly detection algorithms, and flag any transaction, which is not usual as per your transaction history Did you know? Oh!
- 21. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Regression Algorithms Regression Algorithms are used to calculate numeric values. What will the temperature be tomorrow? How much discount can you give on a particular item? For example:
- 22. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Clustering Algorithms It helps you understand the structure of a dataset. These algorithms separates the data into groups or clusters, to ease out the interpretation of the data. By understanding, how data is organized, you can better predict the behavior of a particular event Group A Group B Group C
- 23. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Reinforcement Algorithms These algorithms were designed as to how brains of humans or rats respond to punishments and rewards, they learn from outcomes, and decide on next action. They are good for systems which have to make lot of small decisions without human guidance. For example: A system which plays chess A temperature control system, when it has to decide whether temperature should be increased or decreased
- 24. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Demo
- 25. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Dataset Training Dataset Testing Dataset A sample dataset to be used is divided like this:
- 26. www.edureka.co/data-scienceEdureka’s Data Science Certification Training Thank You … Questions/Queries/Feedback