Slides from a lightning talk on classification methods, originally given at Open Source Open Mic Chicago 01/2016. Yes, I know I left things you. You try covering this in 5 minutes.
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clusteri...Simplilearn
This presentation about hierarchical clustering will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering, and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity.
Below topics are explained in this "Hierarchical Clustering" presentation:
1. What is clustering?
2. What is hierarchical clustering
3. How hierarchical clustering works?
4. Distance measure
5. What is agglomerative clustering
6. What is divisive clustering
7. Demo: to group states based on their sales
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
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 www.simplilearn.com
You will learn the basic concepts of machine learning classification and will be introduced to some different algorithms that can be used. This is from a very high level and will not be getting into the nitty-gritty details.
The Text Classification slides contains the research results about the possible natural language processing algorithms. Specifically, it contains the brief overview of the natural language processing steps, the common algorithms used to transform words into meaningful vectors/data, and the algorithms used to learn and classify the data.
To learn more about RAX Automation Suite, visit: www.raxsuite.com
Hierarchical Clustering | Hierarchical Clustering in R |Hierarchical Clusteri...Simplilearn
This presentation about hierarchical clustering will help you understand what is clustering, what is hierarchical clustering, how does hierarchical clustering work, what is distance measure, what is agglomerative clustering, what is divisive clustering and you will also see a demo on how to group states based on their sales using clustering method. Clustering is the method of dividing the objects into clusters which are similar between them and are dissimilar to the objects belonging to another cluster. It is used to find data clusters such that each cluster has the most closely matched data. Prototype-based clustering, hierarchical clustering, and density-based clustering are the three types of clustering algorithms. Lets us discuss hierarchical clustering in this video. In simple terms, Hierarchical clustering is separating data into different groups based on some measure of similarity.
Below topics are explained in this "Hierarchical Clustering" presentation:
1. What is clustering?
2. What is hierarchical clustering
3. How hierarchical clustering works?
4. Distance measure
5. What is agglomerative clustering
6. What is divisive clustering
7. Demo: to group states based on their sales
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
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 www.simplilearn.com
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
It's Not Magic - Explaining classification algorithmsBrian Lange
As organizations increasingly leverage data and machine learning methods, people throughout those organizations need to build a basic "data literacy" in those topics. In this session, data scientist and instructor Brian Lange provides simple, visual, and equation free explanations for a variety of classification algorithms, geared towards helping anyone understand how they work. Now with Python code examples!
Classification of common clustering algorithm and techniques, e.g., hierarchical clustering, distance measures, K-means, Squared error, SOFM, Clustering large databases.
Naive Bayes is a kind of classifier which uses the Bayes Theorem. It predicts membership probabilities for each class such as the probability that given record or data point belongs to a particular class.
Ensemble Learning is a technique that creates multiple models and then combines them to produce improved results.
Ensemble learning usually produces more accurate solutions than a single model would.
This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques.This course is all about the data mining that how we get the optimized results. it included with all types and how we use these techniques
Welcome to the Supervised Machine Learning and Data Sciences.
Algorithms for building models. Support Vector Machines.
Classification algorithm explanation and code in Python ( SVM ) .
How to validate a model?
What is a best model ?
Types of data
Types of errors
The problem of over fitting
The problem of under fitting
Bias Variance Tradeoff
Cross validation
K-Fold Cross validation
Boot strap Cross validation
Decision tree is a type of supervised learning algorithm (having a pre-defined target variable) that is mostly used in classification problems. It is a tree in which each branch node represents a choice between a number of alternatives, and each leaf node represents a decision.
This slide will try to communicate via pictures, instead of going technical mumbo-jumbo. We might go somewhere but slide is full of pictures. If you dont understand any part of it, let me know.
It's Not Magic - Explaining classification algorithmsBrian Lange
As organizations increasingly leverage data and machine learning methods, people throughout those organizations need to build a basic "data literacy" in those topics. In this session, data scientist and instructor Brian Lange provides simple, visual, and equation free explanations for a variety of classification algorithms, geared towards helping anyone understand how they work. Now with Python code examples!
Big data classification refers to the process of categorizing or classifying data based on predefined categories or classes. It involves using machine learning algorithms and statistical techniques to analyze large volumes of data and assign them to specific classes or categories.
Here are some key aspects of big data classification:
Training Data: Classification algorithms require a labeled dataset for training. This dataset consists of examples where the class or category of each data point is known. The training data is used to build a classification model that can generalize patterns and relationships between the input features and the corresponding classes.
Feature Selection: In classification, the features or attributes of the data play a crucial role in determining the class labels. Feature selection involves identifying the most relevant and informative features that contribute to accurate classification. This helps reduce dimensionality and improve the performance of the classification model.
Classification Algorithms: Various machine learning algorithms can be used for big data classification, such as decision trees, random forests, support vector machines (SVM), logistic regression, and deep learning techniques like neural networks. Each algorithm has its strengths, limitations, and suitability for different types of data and classification tasks.
Model Training and Evaluation: The classification model is trained using the labeled training data. The model learns the patterns and relationships between the features and the corresponding class labels. After training, the model is evaluated using evaluation metrics such as accuracy, precision, recall, F1 score, and area under the receiver operating characteristic (ROC) curve to assess its performance and generalization ability.
Predictive Classification: Once the classification model is trained and evaluated, it can be used to predict the classes of new, unseen data. The model takes the input features of the new data and applies the learned patterns to assign the appropriate class label.
Handling Big Data Challenges: Big data classification comes with specific challenges, including the volume, velocity, variety, and veracity of data. Processing large-scale datasets requires distributed computing frameworks like Apache Hadoop or Apache Spark. Additionally, data preprocessing, feature engineering, and model optimization techniques are used to handle the complexity and scalability of big data.
Big data classification finds applications in various domains, including customer segmentation, fraud detection, image recognition, sentiment analysis, and medical diagnosis, among others. It enables organizations to extract valuable insights from massive datasets and automate the process of categorizing and organizing data.
To successfully perform big data classification, it is important to have a good understanding of the data, the domain, and the appropriate choice of algorithms and techniqu
This presentation is a part of the COP2271C college level course taught at the Florida Polytechnic University located in Lakeland Florida. The purpose of this course is to introduce Freshmen students to both the process of software development and to the Python language.
The course is one semester in length and meets for 2 hours twice a week. The Instructor is Dr. Jim Anderson.
A video of Dr. Anderson using these slides is available on YouTube at:https://www.youtube.com/watch?feature=player_embedded&v=_fdNoErxjBw
DutchMLSchool. Logistic Regression, Deepnets, Time SeriesBigML, Inc
DutchMLSchool. Logistic Regression, Deepnets, and Time Series (Supervised Learning II) - Main Conference: Introduction to Machine Learning.
DutchMLSchool: 1st edition of the Machine Learning Summer School in The Netherlands.
Practicing with unsupervised machine learning techniques I scraped data from Denver's "For Free" Craigslist category to categorize and better understand products available.
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5. things to know
- you need data labeled with the correct answers to
“train” these algorithms before they work
- feature = dimension = attribute of the data
- class = category = Harry Potter house
15. logistic regression
“divide it with a log function”
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
+ gives you probabilities
+ the model is a formula
+ can “threshold” to make model more or less
conservative
💩💩💩💩💩💩💩💩💩💩💩
- only works with linear decision boundaries
16. SVMs (support vector machines)
“*advanced* draw a line through it”
- better definition of “shitty”
- lines can turn into non-linear
shapes if you transform your
data
26. SVMs (support vector machines)
“*advanced* draw a line through it”
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
works well on a lot of different shapes of data
thanks to the kernel trick
💩💩💩💩💩💩💩💩💩💩💩
not super easy to explain to people
can only kinda do probabilities
34. KNN (k-nearest neighbors)
“what do similar cases look like?”
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
+ no training, adding new data is easy
+ you get to define “distance”
💩💩💩💩💩💩💩💩💩💩💩
- can be outlier-sensitive
- you have to define “distance”
39. decision tree learners
make a flow chart of it
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
+ fit all kinds of arbitrary shapes
+ output is a clear set of
conditionals
💩💩💩💩💩💩💩💩💩💩💩
- extremely prone to overfitting
- have to rebuild when you get new
data
- no probability estimates
41. ensemble models
make a bunch of models and combine them
🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉🎉
- don’t overfit as much as their component parts
- Generally don’t require much parameter tweaking
- If data doesn’t change very often, you can make
them semi-online by just adding new trees
- Can provide probabilities
💩💩💩💩💩💩💩💩💩💩💩
- Slower than their component parts (though if
those are fast, it doesn’t matter)