Search results “Data mining bayes classifier python”

Naive Bayes Classifier- Fun and Easy Machine Learning
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
Now Naïve Bayes is based on Bayes Theorem also known as conditional Theorem, which you can think of it as an evidence theorem or trust theorem. So basically how much can you trust the evidence that is coming in, and it’s a formula that describes how much you should believe the evidence that you are being presented with. An example would be a dog barking in the middle of the night. If the dog always barks for no good reason, you would become desensitized to it and not go check if anything is wrong, this is known as false positives. However if the dog barks only whenever someone enters your premises, you’d be more likely to act on the alert and trust or rely on the evidence from the dog. So Bayes theorem is a mathematic formula for how much you should trust evidence.
So lets take a look deeper at the formula,
• We can start of with the Prior Probability which describes the degree to which we believe the model accurately describes reality based on all of our prior information, So how probable was our hypothesis before observing the evidence.
• Here we have the likelihood which describes how well the model predicts the data. This is term over here is the normalizing constant, the constant that makes the posterior density integrate to one. Like we seen over here.
• And finally the output that we want is the posterior probability which represents the degree to which we believe a given model accurately describes the situation given the available data and all of our prior information. So how probable is our hypothesis given the observed evidence.
So with our example above. We can view the probability that we play golf given it is sunny = the probability that we play golf given a yes times the probability it being sunny divided by probability of a yes. This uses the golf example to explain Naive Bayes.
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Augmented Startups

** Machine Learning Training with Python: https://www.edureka.co/python **
This Edureka video will provide you with a detailed and comprehensive knowledge of Naive Bayes Classifier Algorithm in python. At the end of the video, you will learn from a demo example on Naive Bayes. Below are the topics covered in this tutorial:
1. What is Naive Bayes?
2. Bayes Theorem and its use
3. Mathematical Working of Naive Bayes
4. Step by step Programming in Naive Bayes
5. Prediction Using Naive Bayes
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After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
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Data Science is a set of techniques that enable the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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Views: 9725
edureka!

In this Python for Data Science tutorial, You will learn about Naive Bayes classifier (Multinomial Bernoulli Gaussian) using scikit learn and Urllib in Python to how to detect Spam using Jupyter Notebook.
Multinomial Naive Bayes Classifier
Bernoulli Naive Bayes Classifier
Gaussian Naive Bayes Classifier
This is the 32th Video of Python for Data Science Course! In This series I will explain to you Python and Data Science all the time! It is a deep rooted fact, Python is the best programming language for data analysis because of its libraries for manipulating, storing, and gaining understanding from data. Watch this video to learn about the language that make Python the data science powerhouse. Jupyter Notebooks have become very popular in the last few years, and for good reason. They allow you to create and share documents that contain live code, equations, visualizations and markdown text. This can all be run from directly in the browser. It is an essential tool to learn if you are getting started in Data Science, but will also have tons of benefits outside of that field. Harvard Business Review named data scientist "the sexiest job of the 21st century." Python pandas is a commonly-used tool in the industry to easily and professionally clean, analyze, and visualize data of varying sizes and types. We'll learn how to use pandas, Scipy, Sci-kit learn and matplotlib tools to extract meaningful insights and recommendations from real-world datasets.
Download Link for Cars Data Set:
https://www.4shared.com/s/fWRwKoPDaei
Download Link for Enrollment Forecast:
https://www.4shared.com/s/fz7QqHUivca
Download Link for Iris Data Set:
https://www.4shared.com/s/f2LIihSMUei
https://www.4shared.com/s/fpnGCDSl0ei
Download Link for Snow Inventory:
https://www.4shared.com/s/fjUlUogqqei
Download Link for Super Store Sales:
https://www.4shared.com/s/f58VakVuFca
Download Link for States:
https://www.4shared.com/s/fvepo3gOAei
Download Link for Spam-base Data Base:
https://www.4shared.com/s/fq6ImfShUca
Download Link for Parsed Data:
https://www.4shared.com/s/fFVxFjzm_ca
Download Link for HTML File:
https://www.4shared.com/s/ftPVgKp2Lca

Views: 14726
TheEngineeringWorld

This Naive Bayes Classifier tutorial video will introduce you to the basic concepts of Naive Bayes classifier, what is Naive Bayes and Bayes theorem, conditional probability concepts used in Bayes theorem, where is Naive Bayes classifier used, how Naive Bayes algorithm works with solved examples, advantages of Naive Bayes. By the end of this video, you will also implement Naive Bayes algorithm for text classification in Python.
The topics covered in this Naive Bayes video are as follows:
1. What is Naive Bayes? ( 01:06 )
2. Naive Bayes and Machine Learning ( 05:45 )
3. Why do we need Naive Bayes? ( 05:46 )
4. Understanding Naive Bayes Classifier ( 06:30 )
5. Advantages of Naive Bayes Classifier ( 20:17 )
6. Demo - Text Classification using Naive Bayes ( 22:36 )
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Views: 19202
Simplilearn

simple and easy explanation of Naive Bayes Algorithm in Hindi

Views: 6901
Red Apple Tutorials

The algorithm of choice, at least at a basic level, for text analysis is often the Naive Bayes classifier. Part of the reason for this is that text data is almost always massive in size. The Naive Bayes algorithm is so simple that it can be used at scale very easily with minimal process requirements.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Views: 63501
sentdex

( Data Science Training - https://www.edureka.co/data-science )
This Naive Bayes Tutorial video from Edureka will help you understand all the concepts of Naive Bayes classifier, use cases and how it can be used in the industry. This video is ideal for both beginners as well as professionals who want to learn or brush up their concepts in Data Science and Machine Learning through Naive Bayes. Below are the topics covered in this tutorial:
1. What is Machine Learning?
2. Introduction to Classification
3. Classification Algorithms
4. What is Naive Bayes?
5. Use Cases of Naive Bayes
6. Demo – Employee Salary Prediction in R
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The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
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Views: 39683
edureka!

This is a low math introduction and tutorial to classifying text using Naive Bayes. One of the most seminal methods to do so.

Views: 86353
Francisco Iacobelli

This lecture continues to build on Bayes rule that we learned last time. We define training and testing data sets and build a Bayesian classifier. Specifically we will define prior, likelihood and posterior. We will express the posterior in terms of the likelihood and prior and apply this for text classification. We use titles and product descriptions from a retailer and attempt to find the top level category that the product is listed under. The likelihood corresponds to per category word frequencies and the prior correspond to the number of products under each category. We run into implementation issues such as laplacian smoothing, numerical instability etc which we deal with in a quick and hacky manner. But this lecture builds a full classifier from scratch in both the design and complete python implementation.

Views: 49473
BloomReach

We have implemented Text Classification in Python using Naive Bayes Classifier. It explains the text classification algorithm from beginner to pro.
For understanding the co behind it, refer:
https://www.youtube.com/watch?v=Zt83JnjD8zg
Here, we have used 20 Newsgroup dataset to train our model for the classification.
Link to download the 20 Newsgroup dataset:
http://qwone.com/~jason/20Newsgroups/20news-bydate.tar.gz
Packages used here are:
1. sklearn
2. Tfidf Vectorizer
3. Multinomial Naive Bayes Classifier
4. Pipeline
5. Metrics
Refer the entire code at:
https://github.com/codewrestling/TextClassification/blob/master/Text%20Classification.py
For slides, refer:
https://github.com/codewrestling/TextClassification/raw/master/Text%20Classification.pdf
Follow us on Github for more codes:
https://github.com/codewrestling
machine learning python beginner,machine learning python basics,machine learning python regression,machine learning game python,machine learning applications python

Views: 994
Code Wrestling

In this video I will show you how to do text classification with machine learning using python, nltk, scikit and pandas. The concepts shown in this video will enable you to build your own models for your own use cases. So let's go!
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Views: 17103
Coding-Maniac

Join me as I build a spam filtering bot using Python and Scikit-learn.
In this video, we are going to preprocess some data to make it suitable to train a model on.
Code is optimised for Python 2.
Download the dataset here: http://www.aueb.gr/users/ion/data/enron-spam/preprocessed/enron1.tar.gz
Part 2: https://youtu.be/6Wd1C0-3RXM
Entire code available here: https://gist.github.com/SouravJohar/bcbbad0d0b7e881cd0dca3481e32381f

Views: 9311
Sourav Johar

Now that we understand some of the basics of of natural language processing with the Python NLTK module, we're ready to try out text classification. This is where we attempt to identify a body of text with some sort of label.
To start, we're going to use some sort of binary label. Examples of this could be identifying text as spam or not, or, like what we'll be doing, positive sentiment or negative sentiment.
Playlist link: https://www.youtube.com/watch?v=FLZvOKSCkxY&list=PLQVvvaa0QuDf2JswnfiGkliBInZnIC4HL&index=1
sample code: http://pythonprogramming.net
http://hkinsley.com
https://twitter.com/sentdex
http://sentdex.com
http://seaofbtc.com

Views: 90389
sentdex

How to apply naive bayes algorithm | classifier in weka tool ?
In this video, I explained that how can you apply naive bayes algorithm in weka tool.

Views: 4182
DataMining Tutorials

Full course: https://www.udemy.com/data-science-and-machine-learning-with-python-hands-on/?couponCode=DATATUBE
We'll actually write a working spam classifier, using real email training data and a surprisingly small amount of code!

Views: 4924
Sundog Education

In this video, I show how to use Bayes classifiers to determine if a piece of text is "positive" or "negative". In other words, I show you how to make a program with feelings!
The kind of classifier I show is called a Bernoulli naive Bayes classifier:
https://en.wikipedia.org/wiki/Naive_Bayes_classifier#Bernoulli_naive_Bayes
The demo at the beginning of the video can be found at:
http://macheads101.com/demos/sentiment/
The source for the demo, as well as for my program to graph the mood over books, can be found here:
https://github.com/unixpickle/sentigraph

Views: 6276
macheads101

Naive Bayes Classification Algorithm – Solved Numerical Question 1 in Hindi
Data Warehouse and Data Mining Lectures in Hindi

Views: 13106
Easy Engineering Classes

Website + download source code @ http://www.zaneacademy.com

Views: 4527
zaneacademy

We'll build a Spam Detector using a machine learning model called a Naive Bayes Classifier! This is our first real dip into probability theory in the series; I'll talk about the types of probability, then we'll use Bayes Theorem to help us build our classifier.
Code for this video:
https://github.com/llSourcell/naive_bayes_classifier/
Hammad's Winning Code:
https://github.com/hammadshaikhha/Math-of-Machine-Learning-Course-by-Siraj/tree/master/Principal%20Component%20Analysis
Kristian's Runner up Code:
https://github.com/kwichmann/PCA_and_autoencoders
Please Subscribe! And like. And comment. That's what keeps me going.
More Learning Resources:
http://machinelearningmastery.com/naive-bayes-tutorial-for-machine-learning/
http://blog.datumbox.com/machine-learning-tutorial-the-naive-bayes-text-classifier/
http://machinelearningmastery.com/naive-bayes-classifier-scratch-python/
https://www.analyticsvidhya.com/blog/2015/09/naive-bayes-explained/
https://www.youtube.com/watch?v=psHrcSacU9Y
https://hackernoon.com/how-to-build-a-simple-spam-detecting-machine-learning-classifier-4471fe6b816e
https://www.autonlab.org/tutorials/naive.html
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Views: 84349
Siraj Raval

Naive Bayes is a machine learning algorithm for classification problems. It is based on Bayes’ probability theorem. It is primarily used for text classification which involves high dimensional training data sets. A few examples are spam filtration, sentimental analysis, and classifying news articles. It is not only known for its simplicity, but also for its effectiveness. It is fast to build models and make predictions with Naive Bayes algorithm. Naive Bayes is the first algorithm that should be considered for solving text classification problem. Hence, you should learn this algorithm thoroughly.
This video will talk about below:
1. Machine Learning Classification
2. Naive Bayes Theorem
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HackerEarth

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Views: 2684
The New Edge

Views: 18370
Mike Bernico

Naive Bayes | Naive Bayes Algorithm | Naive Bayes Algorithm In Data Mining
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Views: 759
Learning With Mahamud

Links:
1) https://www.youtube.com/watch?v=Ea6CVk1Uiac&index=21&list=PLh6SAYydrIpc6eezCoBpPjBv0jtXbG4rJ

Views: 1728
MachineLearningGod

My web page:
www.imperial.ac.uk/people/n.sadawi

Views: 177209
Noureddin Sadawi

Hierarchical Clustering - Fun and Easy Machine Learning with Examples
https://www.udemy.com/machine-learning-fun-and-easy-using-python-and-keras/?couponCode=YOUTUBE_ML
Hierarchical Clustering
Looking at the formal definition of Hierarchical clustering, as the name suggests is an algorithm that builds hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left.
The results of hierarchical clustering can be shown using Dendogram as we seen before which can be thought of as binary tree
Difference between K Means and Hierarchical clustering
Hierarchical clustering can’t handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2).
In K Means clustering, since we start with random choice of clusters, the results produced by running the algorithm multiple times might differ. While results are reproducible in Hierarchical clustering.
K Means is found to work well when the shape of the clusters is hyper spherical (like circle in 2D, sphere in 3D).
K Means clustering requires prior knowledge of K i.e. no. of clusters you want to divide your data into. However with HCA , you can stop at whatever number of clusters you find appropriate in hierarchical clustering by interpreting the Dendogram.
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Views: 19607
Augmented Startups

A quick tutorial on analysing data in Orange using Classification.

Views: 38686
haikel5

[http://bit.ly/N-Bayes] How can we use Naive Bayes classifier with continuous (real-valued) attributes? We estimate the priors and the means / variances for the Gaussians (two in this example).

Views: 28070
Victor Lavrenko

Website + download source code @ http://www.zaneacademy.com

Views: 1197
zaneacademy

Data Warehouse and Mining
For more: http://www.anuradhabhatia.com

Views: 6199
Anuradha Bhatia

This video is part of an online course, Intro to Machine Learning. Check out the course here: https://www.udacity.com/course/ud120. This course was designed as part of a program to help you and others become a Data Analyst.
You can check out the full details of the program here: https://www.udacity.com/course/nd002.

Views: 44932
Udacity

Provides steps for applying Naive Bayes Classification with R.
Data: https://goo.gl/nCFX1x
R file: https://goo.gl/Feo5mT
Machine Learning videos: https://goo.gl/WHHqWP
Naive Bayes Classification is an important tool related to analyzing big data or working in data science field.
R is a free software environment for statistical computing and graphics, and is widely used by both academia and industry. R software works on both Windows and Mac-OS. It was ranked no. 1 in a KDnuggets poll on top languages for analytics, data mining, and data science. RStudio is a user friendly environment for R that has become popular.

Views: 13197
Bharatendra Rai

** Machine Learning with Python : https://www.edureka.co/machine-learning-certification-training **
This Edureka video on Decision Tree Algorithm in Python will take you through the fundamentals of decision tree machine learning algorithm concepts and its demo in Python. Below are the topics covered in this tutorial:
1. What is Classification?
2. Types of Classification
3. Classification Use Case
4. What is Decision Tree?
5. Decision Tree Terminology
6. Visualizing a Decision Tree
7 Writing a Decision Tree Classifier fro Scratch in Python using CART Algorithm
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#decisiontree #decisiontreepython #machinelearningalgorithms
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About the Course
Edureka’s Machine Learning Course using Python is designed to make you grab the concepts of Machine Learning. The Machine Learning training will provide deep understanding of Machine Learning and its mechanism. As a Data Scientist, you will be learning the importance of Machine Learning and its implementation in python programming language. Furthermore, you will be taught Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to automate real life scenarios using Machine Learning Algorithms. Towards the end of the course, we will be discussing various practical use cases of Machine Learning in python programming language to enhance your learning experience.
After completing this Machine Learning Certification Training using Python, you should be able to:
Gain insight into the 'Roles' played by a Machine Learning Engineer
Automate data analysis using python
Describe Machine Learning
Work with real-time data
Learn tools and techniques for predictive modeling
Discuss Machine Learning algorithms and their implementation
Validate Machine Learning algorithms
Explain Time Series and it’s related concepts
Gain expertise to handle business in future, living the present
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Why learn Machine Learning with Python?
Data Science is a set of techniques that enables the computers to learn the desired behavior from data without explicitly being programmed. It employs techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science. This course exposes you to different classes of machine learning algorithms like supervised, unsupervised and reinforcement algorithms. This course imparts you the necessary skills like data pre-processing, dimensional reduction, model evaluation and also exposes you to different machine learning algorithms like regression, clustering, decision trees, random forest, Naive Bayes and Q-Learning.
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edureka!

#MachineLearningText #NLP #CountVectorizer #DataScience #ScikitLearn #TextFeatures #DataAnalytics #MachineLearning
Text cannot be used as an input to ML algorithms, therefore we use certain techniques to extract features from text. Count Vectorizer extracts features based on word count.
We then apply the features to Multinomial Naive bayes Classifier to classify Spam/ Non Spam messages.
For dataset and Ipython Notebooks.
GitHub: https://github.com/shreyans29/thesemicolon
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The SemiColon

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Machine Learning- Sudeshna Sarkar

Source Code: https://goo.gl/Q3Gt5m
References: https://www.analyticsvidhya.com/blog/2017/09/naive-bayes-explained/
http://www.inf.ed.ac.uk/teaching/courses/inf2b/learnnotes/inf2b-learn-note07-2up.pdf
https://data.world/datasets/twitter
In this video I explain how you can use machine learning algorithms on text data, using the example of twitter sentiment analysis. I have got the dataset of trump related tweets. It is there in the above mentioned website. This code looks though all the data and then figures out if a tweet is a positive tweet or a negative tweet. After the classification(positive sentiment/negative sentiment) it saves the data in a file.
Code work offers you a variety of educational videos to enhance your programming skills. At times I create videos without prior preparations so that I can show you the mistakes I am making so that you don't repeat those mistakes yourself.
It's humanly to make errors, so if you find some errors in my videos please leave a comment below and I will address them or you can email me at [email protected] stating the problem.
I shall try to address all of you .
Finally please hit hike . . . and do subscribe so that you get to know at once when some video is being released.
Happy coding . ..
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code works

In this third video text analytics in R, I've talked about modeling process using the naive bayes classifier that helps us creating a statistical text classifier model which helps classifying the data in ham or spam sms message. You will see how you can tune the parameters also and make the best use of naive bayes classifier model.

Views: 3937
Data Science Tutorials

MSBI - SSAS - Data Mining - Naive Bayes

Views: 156
M R Dhandhukia

Simple example of the Naive Bayes classification algorithm

Views: 121952
Francisco Iacobelli

[http://bit.ly/N-Bayes] How can we distinguish spam from non-spam with a Naive Bayes classifier? We estimate the priors and multiple Bernoulli distributions for each class. Also learn how Naive Bayes can misclassify its own training examples.

Views: 31665
Victor Lavrenko

This playlist/video has been uploaded for Marketing purposes and contains only selective videos.
For the entire video course and code, visit [http://bit.ly/2FtnxpQ].
Naive Bayes is an algorithm that uses probability to classify the data according to Bayes theorem for the strong independence of the features. Bayes theorem estimates the probability of an event based on prior conditions. So, overall, we will use a set of feature values to estimate a value assuming the same conditions hold true when those features have similar values. Also, we will implement naive Bayes using the R programming language.
• Install the package and load the library
• Measure the accuracy of the model
• Determine the accuracy of the model
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Packt Video

naive Bayes classifiers in data mining or machine learning are a family of simple probabilistic classifiers based on applying Bayes' theorem with strong (naive) independence assumptions between the features.
Naive Bayes has been studied extensively since the 1950s. It was introduced under a different name into the text retrieval community in the early 1960s,and remains a popular (baseline) method for text categorization, the problem of judging documents as belonging to one category or the other (such as spam or legitimate, sports or politics, etc.) with word frequencies as the features. With appropriate pre-processing, it is competitive in this domain with more advanced methods including support vector machines. It also finds application in automatic medical diagnosis.
for more refer to
https://en.wikipedia.org/wiki/Naive_Bayes_classifier
naive bayes classifier example for play-tennis
Download PDF of the sum on below link
https://britsol.blogspot.in/2017/11/naive-bayes-classifier-example-pdf.html
*****************************************************NOTE*********************************************************************************
The steps explained in this video is correct but
please don't refer the given sum from the book mentioned in this video coz the solution for this problem might be wrong due to printing mistake.
****************************************************************************************************************************************
All data mining algorithm videos
Data mining algorithms Playlist:
http://www.youtube.com/playlist?list=PLNmFIlsXKJMmekmO4Gh6ZBZUVZp24ltEr
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book name: techmax publications datawarehousing and mining by arti deshpande n pallavi halarnkar
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Views: 38736
fun 2 code

This Decision Tree algorithm in Machine Learning tutorial video will help you understand all the basics of Decision Tree along with what is Machine Learning, problems in Machine Learning, what is Decision Tree, advantages and disadvantages of Decision Tree, how Decision Tree algorithm works with solved examples and at the end we will implement a Decision Tree use case/ demo in Python on loan payment prediction. This Decision Tree tutorial is ideal for both beginners as well as professionals who want to learn Machine Learning Algorithms.
Below topics are covered in this Decision Tree Algorithm Tutorial:
1. What is Machine Learning? ( 02:25 )
2. Types of Machine Learning? ( 03:27 )
3. Problems in Machine Learning ( 04:43 )
4. What is Decision Tree? ( 06:29 )
5. What are the problems a Decision Tree Solves? ( 07:11 )
6. Advantages of Decision Tree ( 07:54 )
7. How does Decision Tree Work? ( 10:55 )
8. Use Case - Loan Repayment Prediction ( 14:32 )
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.
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To gain in-depth knowledge of Machine Learning, check our Machine Learning certification training course: https://www.simplilearn.com/big-data-and-analytics/machine-learning-certification-training-course?utm_campaign=Decision-Tree-Algorithm-With-Example-RmajweUFKvM&utm_medium=Tutorials&utm_source=youtube
#MachineLearningAlgorithms #Datasciencecourse #DataScience #SimplilearnMachineLearning #MachineLearningCourse
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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.
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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.
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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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Who should take this Machine Learning Training Course?
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
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Simplilearn

Watch on Udacity: https://www.udacity.com/course/viewer#!/c-ud262/l-478818537/m-482228628
Check out the full Advanced Operating Systems course for free at: https://www.udacity.com/course/ud262
Georgia Tech online Master's program: https://www.udacity.com/georgia-tech

Views: 86184
Udacity

Here are some of the most commonly used classification algorithms -- Logistic Regression, Naïve Bayes, Stochastic Gradient Descent, K-Nearest Neighbours, Decision Tree, Random Forest and Support Vector Machine.
https://analyticsindiamag.com/7-types-classification-algorithms/
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Analytics India Magazine

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