6 Top Applications of Machine Learning

6 Top Applications of Machine Learning

Here are some top machine learning applications. Have a loot at benefits and advantages of machine learning...

Here are some top machine learning applications. Have a loot at benefits and advantages of machine learning...

Isn’t it equal to a miracle that your upcoming actions are being predicted by computers and software? Of course yes! And the charm of this miracle is being spread by one of the thrilling technology that is “Machine learning”.

Machine learning technology does not need to be introduced as it has already made its place in the hearts of the people. But still, for the sake of the beginners, we would like to give a brief introduction to it.

Machine learning is the application of Artificial Intelligence which makes the computers to predict the outcomes automatically without the intervention of human beings.

MACHINE LEARNING ALGORITHMS

Commonly three types of Machine learning algorithms are available:

A supervised Machine Learning algorithm

In Supervised Machine learning, you are having both input and output variable and then the algorithm is used there in order to predict the output variable.

Unsupervised Machine Learning

In unsupervised machine Learning, only input variable is available instead of an output variable. In Unsupervised Machine learning, data is divided into groups in order to get more information.

Whatever the smart actions we are taking today is the offering of this smart technology that is Machine Learning. There is no doubt that we are implementing machine Learning in most of the actions unknowingly.

***Also Read: ***All That You Need To Know About The Future of AI
We don’t even know what activities are under the shadow of Machine learning. We are just making use of it unknowingly. So, the application of Machine learning is being done in various sectors whether it is our personal life or other sectors.

So, let’s focus once various sectors of the applications of Machine learning:

1. Machine Learning Application in Financial Services

Machine Learning technology can protect the companies that are dealing with finance, from financial fraud that may occur in the future. Apart from this, machine learning can help to predict the upcoming opportunities that could be implemented for further investments.

Cyber surveillance helps to protect those institutions which are more under the shadow of financial risk and is able to take action so that particular fraud could be stopped. So, it is needed to step inside the doors of machine learning companies** **as soon as possible in order to protect the finance related issues.

2. Machine Learning Application in Virtual Personal Assistants

On hearing the name of Assistants, the first thing that strikes our mind is that: Assistants are those who help to guide and assist for a particular direction. And here we are talking about the Machine learning based personal assistants that on the basis of the previous setting decide our upcoming actions through smart devices.

Siri, Alexa, Google Now are some of the examples of Virtual Personal Assistants which help in assisting information. You just have to ask through voice and you will get the result instantly according to the search.

Machine learning based virtual assistants like Amazon’s Alexa, Google Assistant, and Apple’s Siri that are running on our smart speakers and smartphones are making our day to day life easier and entertaining.

Role of these smart assistants individually

In Alexa, you need to set a routine up. And whenever you say “Alexa, good morning,” the lights of your room will turn on automatically and your favorite playlist would start playing itself as this Alexa, a virtual assistant run on smart speakers.

Let’s know how *Siri *which is a smart device of the iPhone may entertain your day to day life. Well, for this follow this example: If you say “Siri, I am going home”. This will automatically open the navigation direction as well as sends the text message to your family at the same time.

Yes! We cannot deny that Machine Learning is the base of these personal assistants as they get input and gives the output ( based on the previous involvement) in the form of the result according to your requirements.

3. Machine Learning Application in Marketing and Sales

Marketing and sales on the basis of machine learning technology are such an amazing strategy to keep the customers always in touch in order to buy your products.

Well, how could this be possible? Simple! With the help of Machine learning technology, you would be able to analyze the purchase history of the customers and would suggest those products in the recommendations in order to make the customers buy it for the next time.

So, it could be said that as it is told before that Machine learning technology predicts future events on the basis of previous involvement, similarly in the case of marketing and sales, it is must say that, on the basis of previous captured customer’s likings, it promotes future sales and marketing.

4. Machine Learning Application in Predictions while Traveling

So, what kind of prediction Machine learning does while traveling? Well, you almost know this. Today is the time when everyone travels with the help of Gps navigation. when you travel while taking the help of GPS navigation, Machine learning technology here predicts the upcoming traffic on the way as for that time being connected with the GPS, your current location and velocities are being connected with central server of managing traffic.

How Machine learning leave its another kind of impact on traveling?

You might have booked cab online, and you have seen that it automatically shows you the estimated cost of the ride. So this is all because of Machine learning.

Sometimes it also happens when you select the option of “sharing ride”, it automatically reduces the price of the ride. This is again happening with the intelligence of machine learning technology.

5. Machine Learning Application in Healthcare

Machine learning is playing an important role in the healthcare sector too. Sensors that are fixed in the wearable of the patient in order to provide information regarding the patient’s condition, heartbeat, blood pressure, etc.

The information that is gathered through the sensors could help the doctors in analyzing the health and condition of an individual. Doctors can predict the upcoming health issues that may worry about the patients.

And in case, if you are running a healthcare department, do consult some good software development company in India which may help you in various ways in order to maintain good relation with your patients.

6. Machine Learning Application in Social Media Services

How entertaining and colorful your social media has become? Whatever the thing is wandering in your mind, social media starts flashing the ads of that particular interest. So, this is all about that Social Media has smartly connected with Machine learning in order to make your social presence beneficial and knowledgeable.

Let’s see the impact of Machine learning on Facebook

Here is a very simple concept where Machine learning is dominating the most used app Facebook.

How?

Well, actually by suggesting the various friend suggestion. On the basis of experience, Facebook keeps on noticing the friends you may connect and the profiles that you have ever visited.

Another way where Machine learning is working on Facebook. And this is when you upload a picture with some friend of yours, Facebook instantly recognizes the unique feature of that person after going through your friend list.

Wrapping up

This is how Machine learning is continuously making your lives easier and entertaining. From the above-given examples, you might have understood how Machine learning is helping to predict your output in the form of future activities.

As it is well known that machine learning is amazingly revolutionizing the world, there are various mobile app development companies in India that are giving the provision of building ML-based applications.

Machine Learning Full Course - Learn Machine Learning

Machine Learning Full Course - Learn Machine Learning

This complete Machine Learning full course video covers all the topics that you need to know to become a master in the field of Machine Learning.

Machine Learning Full Course | Learn Machine Learning | Machine Learning Tutorial

It covers all the basics of Machine Learning (01:46), the different types of Machine Learning (18:32), and the various applications of Machine Learning used in different industries (04:54:48).This video will help you learn different Machine Learning algorithms in Python. Linear Regression, Logistic Regression (23:38), K Means Clustering (01:26:20), Decision Tree (02:15:15), and Support Vector Machines (03:48:31) are some of the important algorithms you will understand with a hands-on demo. Finally, you will see the essential skills required to become a Machine Learning Engineer (04:59:46) and come across a few important Machine Learning interview questions (05:09:03). Now, let's get started with Machine Learning.

Below topics are explained in this Machine Learning course for beginners:

  1. Basics of Machine Learning - 01:46

  2. Why Machine Learning - 09:18

  3. What is Machine Learning - 13:25

  4. Types of Machine Learning - 18:32

  5. Supervised Learning - 18:44

  6. Reinforcement Learning - 21:06

  7. Supervised VS Unsupervised - 22:26

  8. Linear Regression - 23:38

  9. Introduction to Machine Learning - 25:08

  10. Application of Linear Regression - 26:40

  11. Understanding Linear Regression - 27:19

  12. Regression Equation - 28:00

  13. Multiple Linear Regression - 35:57

  14. Logistic Regression - 55:45

  15. What is Logistic Regression - 56:04

  16. What is Linear Regression - 59:35

  17. Comparing Linear & Logistic Regression - 01:05:28

  18. What is K-Means Clustering - 01:26:20

  19. How does K-Means Clustering work - 01:38:00

  20. What is Decision Tree - 02:15:15

  21. How does Decision Tree work - 02:25:15 

  22. Random Forest Tutorial - 02:39:56

  23. Why Random Forest - 02:41:52

  24. What is Random Forest - 02:43:21

  25. How does Decision Tree work- 02:52:02

  26. K-Nearest Neighbors Algorithm Tutorial - 03:22:02

  27. Why KNN - 03:24:11

  28. What is KNN - 03:24:24

  29. How do we choose 'K' - 03:25:38

  30. When do we use KNN - 03:27:37

  31. Applications of Support Vector Machine - 03:48:31

  32. Why Support Vector Machine - 03:48:55

  33. What Support Vector Machine - 03:50:34

  34. Advantages of Support Vector Machine - 03:54:54

  35. What is Naive Bayes - 04:13:06

  36. Where is Naive Bayes used - 04:17:45

  37. Top 10 Application of Machine Learning - 04:54:48

  38. How to become a Machine Learning Engineer - 04:59:46

  39. Machine Learning Interview Questions - 05:09:03

Machine Learning | Machine Learning Guide for Beginners

Machine Learning | Machine Learning Guide for Beginners

Machine learning problems can generally be divided into three types. Classification and regression, which are known as supervised learning, and unsupervised learning which in the context of machine learning applications often refers to clustering.

Machine learning problems can generally be divided into three types. Classification and regression, which are known as supervised learning, and unsupervised learning which in the context of machine learning applications often refers to clustering.

In the following article, I am going to give a brief introduction to each of these three problems and will include a walkthrough in the popular python library scikit-learn.

Before I start I’ll give a brief explanation for the meaning behind the terms supervised and unsupervised learning.

Supervised Learning: In supervised learning, you have a known set of inputs (features) and a known set of outputs (labels). Traditionally these are known as X and y. The goal of the algorithm is to learn the mapping function that maps the input to the output. So that when given new examples of X the machine can correctly predict the corresponding y labels.

Unsupervised Learning: In unsupervised learning, you only have a set of inputs (X) and no corresponding labels (y). The goal of the algorithm is to find previously unknown patterns in the data. Quite often these algorithms are used to find meaningful clusters of similar samples of X so in effect finding the categories intrinsic to the data.

Classification

In classification, the outputs (y) are categories. These can be binary, for example, if we were classifying spam email vs not spam email. They can also be multiple categories such as classifying species of flowers, this is known as multiclass classification.

Let’s walk through a simple example of classification using scikit-learn. If you don’t already have this installed it can be installed either via pip or conda as outlined here.

Scikit-learn has a number of datasets that can be directly accessed via the library. For ease in this article, I will be using these example datasets throughout. To illustrate classification I will use the wine dataset which is a multiclass classification problem. In the dataset, the inputs (X) consist of 13 features relating to various properties of each wine type. The known outputs (y) are wine types which in the dataset have been given a number 0, 1 or 2.

The imports I am using for all the code in this article are shown below.

import pandas as pd
import numpy as np
from sklearn.datasets import load_wine
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn import preprocessing
from sklearn.metrics import f1_score
from sklearn.metrics import mean_squared_error
from math import sqrt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC, LinearSVC, NuSVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
from sklearn import linear_model
from sklearn.linear_model import ElasticNetCV
from sklearn.svm import SVR
from sklearn.cluster import KMeans
from yellowbrick.cluster import KElbowVisualizer
from yellowbrick.cluster import SilhouetteVisualizer

In the below code I am downloading the data and converting to a pandas data frame.

wine = load_wine()
wine_df = pd.DataFrame(wine.data, columns=wine.feature_names)
wine_df['TARGET'] = pd.Series(wine.target)

The next stage in a supervised learning problem is to split the data into test and train sets. The train set can be used by the algorithm to learn the mapping between inputs and outputs, and then the reserved test set can be used to evaluate how well the model has learned this mapping. In the below code I am using the scikit-learn model_selection function train_test_split to do this.

X_w = wine_df.drop(['TARGET'], axis=1)
y_w = wine_df['TARGET']
X_train_w, X_test_w, y_train_w, y_test_w = train_test_split(X_w, y_w, test_size=0.2)

In the next step, we need to choose the algorithm that will be best suited to learn the mapping in your chosen dataset. In scikit-learn there are many different algorithms to choose from, all of which use different functions and methods to learn the mapping, you can view the full list here.

To determine the best model I am running the following code. I am training the model using a selection of algorithms and obtaining the F1-score for each one. The F1 score is a good indicator of the overall accuracy of a classifier. I have written a detailed description of the various metrics that can be used to evaluate a classifier here.

classifiers = [
    KNeighborsClassifier(3),
    SVC(kernel="rbf", C=0.025, probability=True),
    NuSVC(probability=True),
    DecisionTreeClassifier(),
    RandomForestClassifier(),
    AdaBoostClassifier(),
    GradientBoostingClassifier()
    ]
for classifier in classifiers:
    model = classifier
    model.fit(X_train_w, y_train_w)  
    y_pred_w = model.predict(X_test_w)
    print(classifier)
    print("model score: %.3f" % f1_score(y_test_w, y_pred_w, average='weighted'))

A perfect F1 score would be 1.0, therefore, the closer the number is to 1.0 the better the model performance. The results above suggest that the Random Forest Classifier is the best model for this dataset.

Regression

In regression, the outputs (y) are continuous values rather than categories. An example of regression would be predicting how many sales a store may make next month, or what the future price of your house might be.

Again to illustrate regression I will use a dataset from scikit-learn known as the boston housing dataset. This consists of 13 features (X) which are various properties of a house such as the number of rooms, the age and crime rate for the location. The output (y) is the price of the house.

I am loading the data using the code below and splitting it into test and train sets using the same method I used for the wine dataset.

boston = load_boston()
boston_df = pd.DataFrame(boston.data, columns=boston.feature_names)
boston_df['TARGET'] = pd.Series(boston.target)
X_b = boston_df.drop(['TARGET'], axis=1)
y_b = boston_df['TARGET']
X_train_b, X_test_b, y_train_b, y_test_b = train_test_split(X_b, y_b, test_size=0.2)

We can use this cheat sheet to see the available algorithms suited to regression problems in scikit-learn. We will use similar code to the classification problem to loop through a selection and print out the scores for each.

There are a number of different metrics used to evaluate regression models. These are all essentially error metrics and measure the difference between the actual and predicted values achieved by the model. I have used the root mean squared error (RMSE). For this metric, the closer to zero the value is the better the performance of the model. This article gives a really good explanation of error metrics for regression problems.

regressors = [
    linear_model.Lasso(alpha=0.1),
    linear_model.LinearRegression(),
    ElasticNetCV(alphas=None, copy_X=True, cv=5, eps=0.001, fit_intercept=True,
       l1_ratio=0.5, max_iter=1000, n_alphas=100, n_jobs=None,
       normalize=False, positive=False, precompute='auto', random_state=0,
       selection='cyclic', tol=0.0001, verbose=0),
    SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,
    gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,
    tol=0.001, verbose=False),
    linear_model.Ridge(alpha=.5)                
    ]
for regressor in regressors:
    model = regressor
    model.fit(X_train_b, y_train_b)  
    y_pred_b = model.predict(X_test_b)
    print(regressor)
    print("mean squared error: %.3f" % sqrt(mean_squared_error(y_test_b, y_pred_b)))


The RMSE score suggests that either the linear regression and ridge regression algorithms perform best for this dataset.

Unsupervised learning

There are a number of different types of unsupervised learning but for simplicity here I am going to focus on the clustering methods. There are many different algorithms for clustering all of which use slightly different techniques to find clusters of inputs.

Probably one of the most widely used methods is Kmeans. This algorithm performs an iterative process whereby a specified number of randomly generated means are initiated. A distance metric, Euclidean distance is calculated for each data point from the centroids, thus creating clusters of similar values. The centroid of each cluster then becomes the new mean and this process is repeated until the optimum result has been achieved.

Let’s use the wine dataset we used in the classification task, with the y labels removed, and see how well the k-means algorithm can identify the wine types from the inputs.

As we are only using the inputs for this model I am splitting the data into test and train using a slightly different method.

np.random.seed(0)
msk = np.random.rand(len(X_w)) < 0.8
train_w = X_w[msk]
test_w = X_w[~msk]

As Kmeans is reliant on the distance metric to determine the clusters it is usually necessary to perform feature scaling (ensuring that all features have the same scale) before training the model. In the below code I am using the MinMaxScaler to scale the features so that all values fall between 0 and 1.

x = train_w.values
min_max_scaler = preprocessing.MinMaxScaler()
x_scaled = min_max_scaler.fit_transform(x)
X_scaled = pd.DataFrame(x_scaled,columns=train_w.columns)

With K-means you have to specify the number of clusters the algorithm should use. So one of the first steps is to identify the optimum number of clusters. This is achieved by iterating through a number of values of k and plotting the results on a chart. This is known as the Elbow method as it typically produces a plot with a curve that looks a little like the curve of your elbow. The yellowbrick library (which is a great library for visualising scikit-learn models and can be pip installed) has a really nice plot for this. The code below produces this visualisation.

model = KMeans()
visualizer = KElbowVisualizer(model, k=(1,8))
visualizer.fit(X_scaled)       
visualizer.show()

Ordinarily, we wouldn’t already know how many categories we have in a dataset where we are using a clustering technique. However, in this case, we know that there are three wine types in the data — the curve has correctly selected three as the optimum number of clusters to use in the model.

The next step is to initialise the K-means algorithm and fit the model to the training data and evaluate how effectively the algorithm has clustered the data.

One method used for this is known as the silhouette score. This measures the consistency of values within the clusters. Or in other words how similar to each other the values in each cluster are, and how much separation there is between the clusters. The silhouette score is calculated for each value and will range from -1 to +1. These values are then plotted to form a silhouette plot. Again yellowbrick provides a simple way to construct this type of plot. The code below creates this visualisation for the wine dataset.

model = KMeans(3, random_state=42)
visualizer = SilhouetteVisualizer(model, colors='yellowbrick')
visualizer.fit(X_scaled)      
visualizer.show()

A silhouette plot can be interpreted in the following way:

  • The closer the mean score (which is the red dotted line in the above) is to +1 the better matched the data points are within the cluster.
  • Data points with a score of 0 are very close to the decision boundary for another cluster (so the separation is low).
  • Negative values indicate that the data points may have been assigned to the wrong cluster.
  • The width of each cluster should be reasonably uniform if they aren’t then the incorrect value of k may have been used.

The plot for the wine data set above shows that cluster 0 may not be as consistent as the others due to most data points being below the average score and a few data points having a score below 0.

Silhouette scores can be particularly useful in comparing one algorithm against another or different values of k.

In this post, I wanted to give a brief introduction to each of the three types of machine learning. There are many other steps involved in all of these processes including feature engineering, data processing and hyperparameter optimisation to determine both the best data preprocessing techniques and the best models to use.

Thanks for reading!