Algorithm
Naive Bayes
Description
The Bayesian method is a classification method that makes use of the Bayesian theorem. The theorem updates the prior knowledge of an event with the independent probability of each feature that can affect the event.
Type
Regression
Classification
Naive Bayes
Description
The Bayesian method is a classification method that makes use of the Bayesian theorem. The theorem updates the prior knowledge of an event with the independent probability of each feature that can affect the event.
Type
Regression
Classification
Algorithm
Support vector machine
Description
Support Vector Machine, or SVM, is typically used for the classification task.
SVM algorithm finds a hyperplane that optimally divided the classes. It is best used with a non-linear solver.
Type
Regression (not very common)
Classification
Support vector machine
Description
Support Vector Machine, or SVM, is typically used for the classification task.
SVM algorithm finds a hyperplane that optimally divided the classes. It is best used with a non-linear solver.
Type
Regression (not very common)
Classification
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Algorithm
Random forest
Description
The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the βmajority voteβ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction.
Type
Regression
Classification
Random forest
Description
The algorithm is built upon a decision tree to improve the accuracy drastically. Random forest generates many times simple decision trees and uses the βmajority voteβ method to decide on which label to return. For the classification task, the final prediction will be the one with the most vote; while for the regression task, the average prediction of all the trees is the final prediction.
Type
Regression
Classification
Algorithm
AdaBoost
Description
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome
Type
Regression
Classification
AdaBoost
Description
Classification or regression technique that uses a multitude of models to come up with a decision but weighs them based on their accuracy in predicting the outcome
Type
Regression
Classification
Algorithm
Gradient-boosting trees
Description
Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it.
Type
Regression
Classification
Gradient-boosting trees
Description
Gradient-boosting trees is a state-of-the-art classification/regression technique. It is focusing on the error committed by the previous trees and tries to correct it.
Type
Regression
Classification
Unsupervised learning
In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
In unsupervised learning, an algorithm explores input data without being given an explicit output variable (e.g., explores customer demographic data to identify patterns)
You can use it when you do not know how to classify the data, and you want the algorithm to find patterns and classify the data for you
Algorithm Name
K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
K-means clustering
Description
Puts data into some groups (k) that each contains data with similar characteristics (as determined by the model, not in advance by humans)
Type
Clustering
Algorithm Name
Gaussian mixture model
Description
A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)
Type
Clustering
Gaussian mixture model
Description
A generalization of k-means clustering that provides more flexibility in the size and shape of groups (clusters)
Type
Clustering
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Algorithm Name
Hierarchical clustering
Description
Splits clusters along a hierarchical tree to form a classification system.
Can be used for Cluster loyalty-card customer
Type
Clustering
Hierarchical clustering
Description
Splits clusters along a hierarchical tree to form a classification system.
Can be used for Cluster loyalty-card customer
Type
Clustering
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Algorithm Name
Recommender system
Description
Help to define the relevant data for making a recommendation.
Type
Clustering
Recommender system
Description
Help to define the relevant data for making a recommendation.
Type
Clustering
Algorithm Name
PCA/T-SNE
Description
Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances
Type
Dimension Reduction
PCA/T-SNE
Description
Mostly used to decrease the dimensionality of the data. The algorithms reduce the number of features to 3 or 4 vectors with the highest variances
Type
Dimension Reduction
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βHow to Choose Machine Learning Algorithm
Now in this Machine learning basics tutorial, we will learn how to choose Machine Learning (ML) algorithm:
There are plenty of machine learning algorithms. The choice of the algorithm is based on the objective.
In the Machine learning example below, the task is to predict the type of flower among the three varieties.
The predictions are based on the length and the width of the petal. The picture depicts the results of ten different algorithms. The picture on the top left is the dataset. The data is classified into three categories: red, light blue and dark blue. There are some groupings. For instance, from the second image, everything in the upper left belongs to the red category, in the middle part, there is a mixture of uncertainty and light blue while the bottom corresponds to the dark category. The other images show different algorithms and how they try to classified the data.
Now in this Machine learning basics tutorial, we will learn how to choose Machine Learning (ML) algorithm:
There are plenty of machine learning algorithms. The choice of the algorithm is based on the objective.
In the Machine learning example below, the task is to predict the type of flower among the three varieties.
The predictions are based on the length and the width of the petal. The picture depicts the results of ten different algorithms. The picture on the top left is the dataset. The data is classified into three categories: red, light blue and dark blue. There are some groupings. For instance, from the second image, everything in the upper left belongs to the red category, in the middle part, there is a mixture of uncertainty and light blue while the bottom corresponds to the dark category. The other images show different algorithms and how they try to classified the data.
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Challenges and Limitations of Machine Learning
Now in this Machine learning tutorial, we will learn about the limitations of Machine Learning:
The primary challenge of machine learning is the lack of data or the diversity in the dataset. A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time. A machine needs to have heterogeneity to learn meaningful insight. It is rare that an algorithm can extract information when there are no or few variations. It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.
Now in this Machine learning tutorial, we will learn about the limitations of Machine Learning:
The primary challenge of machine learning is the lack of data or the diversity in the dataset. A machine cannot learn if there is no data available. Besides, a dataset with a lack of diversity gives the machine a hard time. A machine needs to have heterogeneity to learn meaningful insight. It is rare that an algorithm can extract information when there are no or few variations. It is recommended to have at least 20 observations per group to help the machine learn. This constraint leads to poor evaluation and prediction.
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Application of Machine Learning
Now in this Machine learning tutorial, letβs learn the applications of Machine Learning:
Augmentation:
Machine learning, which assists humans with their day-to-day tasks, personally or commercially without having complete control of the output. Such machine learning is used in different ways such as Virtual Assistant, Data analysis, software solutions. The primary user is to reduce errors due to human bias.
Automation:
Machine learning, which works entirely autonomously in any field without the need for any human intervention. For example, robots performing the essential process steps in manufacturing plants.
Finance Industry
Machine learning is growing in popularity in the finance industry. Banks are mainly using ML to find patterns inside the data but also to prevent fraud.
Government organization
The government makes use of ML to manage public safety and utilities. Take the example of China with the massive face recognition. The government uses Artificial intelligence to prevent jaywalker.
Healthcare industry
Healthcare was one of the first industry to use machine learning with image detection.
Marketing
Broad use of AI is done in marketing thanks to abundant access to data. Before the age of mass data, researchers develop advanced mathematical tools like Bayesian analysis to estimate the value of a customer. With the boom of data, marketing department relies on AI to optimize the customer relationship and marketing campaign.
Now in this Machine learning tutorial, letβs learn the applications of Machine Learning:
Augmentation:
Machine learning, which assists humans with their day-to-day tasks, personally or commercially without having complete control of the output. Such machine learning is used in different ways such as Virtual Assistant, Data analysis, software solutions. The primary user is to reduce errors due to human bias.
Automation:
Machine learning, which works entirely autonomously in any field without the need for any human intervention. For example, robots performing the essential process steps in manufacturing plants.
Finance Industry
Machine learning is growing in popularity in the finance industry. Banks are mainly using ML to find patterns inside the data but also to prevent fraud.
Government organization
The government makes use of ML to manage public safety and utilities. Take the example of China with the massive face recognition. The government uses Artificial intelligence to prevent jaywalker.
Healthcare industry
Healthcare was one of the first industry to use machine learning with image detection.
Marketing
Broad use of AI is done in marketing thanks to abundant access to data. Before the age of mass data, researchers develop advanced mathematical tools like Bayesian analysis to estimate the value of a customer. With the boom of data, marketing department relies on AI to optimize the customer relationship and marketing campaign.
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Tomorrow, we'll look at why machine learning is important.
That will be the final section.
That will be the final section.
Example of application of Machine Learning in Supply Chain
Machine learning gives terrific results for visual pattern recognition, opening up many potential applications in physical inspection and maintenance across the entire supply chain network.
Unsupervised learning can quickly search for comparable patterns in the diverse dataset. In turn, the machine can perform quality inspection throughout the logistics hub, shipment with damage and wear.
For instance, IBMβs Watson platform can determine shipping container damage. Watson combines visual and systems-based data to track, report and make recommendations in real-time.
In past year stock manager relies extensively on the primary method to evaluate and forecast the inventory. When combining big data and machine learning, better forecasting techniques have been implemented (an improvement of 20 to 30 % over traditional forecasting tools). In term of sales, it means an increase of 2 to 3 % due to the potential reduction in inventory costs.
Machine learning gives terrific results for visual pattern recognition, opening up many potential applications in physical inspection and maintenance across the entire supply chain network.
Unsupervised learning can quickly search for comparable patterns in the diverse dataset. In turn, the machine can perform quality inspection throughout the logistics hub, shipment with damage and wear.
For instance, IBMβs Watson platform can determine shipping container damage. Watson combines visual and systems-based data to track, report and make recommendations in real-time.
In past year stock manager relies extensively on the primary method to evaluate and forecast the inventory. When combining big data and machine learning, better forecasting techniques have been implemented (an improvement of 20 to 30 % over traditional forecasting tools). In term of sales, it means an increase of 2 to 3 % due to the potential reduction in inventory costs.
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