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.


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.


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 Tutorial - Learn Machine Learning - Intellipaat

Machine Learning Tutorial - Learn Machine Learning - Intellipaat

This Machine Learning tutorial for beginners will enable you to learn Machine Learning algorithms with python examples. Become a pro in Machine Learning.

Mastering the Machine Learning Course would easily develop one's career. This is the reason why studying Machine Learning Tutorial becomes so important in the career of a particular student.
Making a part of the machine learning course would enact and studying the Machine Learning Tutorial would make one carve out a new niche.

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Top Machine Learning Framework: 5 Machine Learning Frameworks of 2019

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5...

Machine Learning (ML) is one of the fastest-growing technologies today. ML has a lot of frameworks to build a successful app, and so as a developer, you might be getting confused about using the right framework. Herein we have curated top 5 machine learning frameworks that are cutting edge technology in your hands.

Through the machine learning frameworks, mobile phones and tablets are getting powerful enough to run the software that can learn and react in real-time. It is a complex discipline. But the implementation of ML models is far less daunting and difficult than it used to be. Now, it automatically improves the performance with the pace of time, interactions, and experiences, and the most important acquisition of useful data pertaining to the tasks allocated.

As we know that ML is considered as a subset of Artificial Intelligence (AI). The scientific study of statistical models and algorithms help a computing system to accomplish designated tasks efficiently. Now, as a mobile app developer, when you are planning to choose machine learning frameworks you must keep the following things in mind.

The framework should be performance-oriented
The grasping and coding should be quick
It allows to distribute the computational process, the framework must have parallelization
It should consist of a facility to create models and provide a developer-friendly tool
Let’s learn about the top five machine learning frameworks to make the right choice for your next ML application development project. Before we dive deeper into these mentioned frameworks, know the different types of ML frameworks that are available on the web. Here are some ML frameworks:

Mathematical oriented
Neural networks-based
Linear algebra tools
Statistical tools
Now, let’s have an insight into ML frameworks that will help you in selecting the right framework for your ML application.

Don’t Miss Out on These 5 Machine Learning Frameworks of 2019
#1 TensorFlow
TensorFlow is an open-source software library for data-based programming across multiple tasks. The framework is based on computational graphs which is essentially a network of codes. Each node represents a mathematical operation that runs some function as simple or as complex as multivariate analysis. This framework is said to be best among all the ML libraries as it supports regressions, classifications, and neural networks like complicated tasks and algorithms.

machine learning frameworks
This machine learning library demands additional efforts while learning TensorFlow Python framework. Your job becomes easy in the n-dimensional array of the framework when you have grasped the Python frameworks and libraries.

The benefits of this framework are flexibility. TensorFlow allows non-automatic migration to newer versions. It runs on the GPU, CPU, servers, desktops, and mobile devices. It provides auto differentiation and performance. There are a few goliaths like Airbus, Twitter, IBM, who have innovatively used the TensorFlow frameworks.

#2 FireBase ML Kit
Firebase machine learning framework is a library that allows effortless, minimal code, with highly accurate, pre-trained deep models. We at Space-O Technologies use this machine learning technology for image classification and object detection. The Firebase framework offers models both locally and on the Google Cloud.

machine learning frameworks
This is one of our ML tutorials to make you understand the Firebase frameworks. First of all, we collected photos of empty glass, half watered glass, full watered glass, and targeted into the machine learning algorithms. This helped the machine to search and analyze according to the nature, behavior, and patterns of the object placed in front of it.

The first photo that we targeted through machine learning algorithms was to recognize an empty glass. Thus, the app did its analysis and search for the correct answer, we provided it with certain empty glass images prior to the experiment.
The other photo that we targeted was a half water glass. The core of the machine learning app is to assemble data and to manage it as per its analysis. It was able to recognize the image accurately because of the little bits and pieces of the glass given to it beforehand.
The last one is a full glass recognition image.
Note: For correct recognition, there has to be 1 label that carries at least 100 images of a particular object.

#3 CAFFE (Convolutional Architecture for Fast Feature Embedding)
CAFFE framework is the fastest way to apply deep neural networks. It is the best machine learning framework known for its model-Zoo a pre-trained ML model that is capable of performing a great variety of tasks. Image classification, machine vision, recommender system are some of the tasks performed easily through this ML library.

machine learning frameworks
This framework is majorly written in CPP. It can run on multiple hardware and can switch between CPU and GPU with the use of a single flag. It has systematically organized the structure of Mat lab and python interface.

Now, if you have to make a machine learning app development, then it is mainly used in academic research projects and to design startups prototypes. It is the aptest machine learning technology for research experiments and industry deployment. At a time this framework can manage 60 million pictures every day with a solitary Nvidia K40 GPU.

#4 Apache Spark
The Apache Spark machine learning is a cluster-computing framework written in different languages like Java, Scala, R, and Python. Spark’s machine learning library, MLlib is considered as foundational for the Spark’s success. Building MLlib on top of Spark makes it possible to tackle the distinct needs of a single tool instead of many disjointed ones.

machine learning frameworks
The advantages of such ML library lower learning curves, less complex development and production environments, which ultimately results in a shorter time to deliver high-performing models. The key benefit of MLlib is that it allows data scientists to solve multiple data problems in addition to their machine learning problems.

It can easily solve graph computations (via GraphX), streaming (real-time calculations), and real-time interactive query processing with Spark SQL and DataFrames. The data professionals can focus on solving the data problems instead of learning and maintaining a different tool for each scenario.

#5 Scikit-Learn
Scikit-learn is said to be one of the greatest feats of Python community. This machine learning framework efficiently handles data mining and supports multiple practical tasks. It is built on foundations like SciPy, Numpy, and matplotlib. This framework is known for supervised & unsupervised learning algorithms as well as cross-validation. The Scikit learn is largely written in Python with some core algorithms in Cython to achieve performance.

machine learning frameworks
The machine learning framework can work on multiple tasks without compromising on speed. There are some remarkable machine learning apps using this framework like Spotify, Evernote, AWeber, Inria.

With the help of machine learning to build iOS apps, Android apps powered by ML have become quite an easy process. With this emerging technology trend varieties of available data, computational processing has become cheaper and more powerful, and affordable data storage. So being an app developer or having an idea for machine learning apps should definitely dive into the niche.

Still have any query or confusion regarding ML frameworks, machine learning app development guide, the difference between Artificial Intelligence and machine learning, ML algorithms from scratch, how this technology is helpful for your business? Just fill our contact us form. Our sales representatives will get back to you shortly and resolve your queries. The consultation is absolutely free of cost.

Author Bio: This blog is written with the help of Jigar Mistry, who has over 13 years of experience in the web and mobile app development industry. He has guided to develop over 200 mobile apps and has special expertise in different mobile app categories like Uber like apps, Health and Fitness apps, On-Demand apps and Machine Learning apps. So, we took his help to write this complete guide on machine learning technology and machine app development areas.