Oral  Brekke

Oral Brekke


Difference between: AI vs Machine Learning vs Deep Learning

AI vs Machine Learning vs Deep Learning, these terms have confused a lot of people. If you too are one among them then this blog – AI vs Machine Learning vs Deep Learning is definitely for you. 

AI vs Machine Learning vs Deep Learning

Artificial Intelligence is the broader umbrella under which Machine Learning and Deep Learning come. And you can also see in the diagram that even deep learning is a subset of Machine Learning. So all three of them AI, machine learning and deep learning are just the subsets of each other. So let us move on and understand how exactly they are different from each other. There are various Deep learning course Programs available to help you master Deep Learning with Tensorflow. Join the program and get certified!

AI vs ML vs Deep Learning - Machine Learning Tutorial - Edureka

Starting with Artificial Intelligence

The term artificial intelligence was first coined in the year 1956, but AI has become more popular these days why? Well, it’s because of the tremendous increase in data volumes, advanced algorithms, and improvements in computing power and storage.

The data we had was not enough to predict the accurate result. But now there is a tremendous increase in the amount of data. Statistics suggest that By 2020, the accumulated volume of big data will increase from 4.4 zettabytes to roughly 44 zettabytes or 44 trillion GBs of data.

Now we even have more advanced algorithms and high end computing power and storage that can deal with such large amount of data. As a result, it is expected that 70% of the enterprise will implement AI over the next 12 months, which is up from 40% in 2016 and 51% in 2017.

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What is Artificial Intelligence?

Artificial Intelligence is a technique that allows machines to act like humans by replicating their behavior and nature.

Artificial Intelligence makes it possible for machines to learn from their experience. The machines adjust their response based on new inputs thereby performing human-like tasks by processing large amounts of data and recognizing patterns in them.

AI Explained with an Analogy: Construction of a Church

You can consider that building artificial intelligence is like building a church.

The first church took generations to finish, so most of the workers working on it never saw the final outcome. Those working on it took pride in their craft, building bricks and chiseling stones that were to be placed into the Great Structure. So, as AI researchers, we should think of ourselves as humble brick makers, whose job it is to study how to build components (e.g. parsers, planners, learning algorithms, etc) that someday someone, somewhere, will integrate into intelligent systems.

Some of the examples of Artificial Intelligence from our day to day life are Apple’s Siri, the chess-playing computer, tesla’s self-driving car and many more. These examples are based on deep learning and natural language processing.

Well, this was about what is AI and how it gained its hype. So moving on ahead let’s discuss machine learning and see what it is and why was it even introduced.

Machine Learning came into existence in the late 80’s and early 90’s. But what were the issues with the people which made Machine Learning come into existence?

Statistics: How to efficiently train large complex models?

Computer Science & Artificial Intelligence: How to train more robust versions of the AI systems?

Neuroscience: How to design operational models of the brain?

What is Machine Learning?

“Machine Learning is a subset of artificial intelligence. It allows the machines to learn and make predictions based on its experience(data)“

Understanding Machine Learning with an Example

Let’s say you want to create a system which could predict the expected weight of a person based on its height. The first thing you do is collect the data. Let us say this is how your data looks like:

Linear Regression - AI vs Machine Learning vs Deep Learning - Edureka

Each point on the graph represents one data point. To start with we can draw a simple line to predict the weight based on the height. For example, a simple line:

 W = H – 100

Where W is weight in kg and H is height in cm

This line can help us to make predictions. Our main goal is to reduce the difference between the estimated value and actual value. So in order to achieve it, we try to draw a straight line that fits through all these different points and minimize the error and make them as small as possible. Decreasing the error or the difference between the actual value and the estimated value increases the performance.

Further, the more data points we collect, the better will our model become. We can also improve our model by adding more variables (e.g. Gender) and creating different prediction lines for them. Once the line is created, so in future, if a new data (for example height of a person) is fed to the model, it would easily predict the data for you and will tell his predicted weight.

I hope you got a clear understanding of machine learning. So moving on ahead let’s learn about Deep Learning.

What is Deep Learning?

“Deep learning is a particular kind of machine learning that achieves great power and flexibility by learning to represent the world as nested hierarchy of concepts or abstraction”

You can consider deep learning models as a rocket engine and its fuel is the huge amount of data that we feed to these algorithms.

The concept of deep learning is not new. But recently its hype has increased, and deep learning is getting more attention. This field is a special kind of machine learning which is inspired by the functionality of our brain cells called artificial neural network. It simply takes data connections between all artificial neurons and adjusts them according to the data pattern. More neurons are needed if the size of the data is large. It automatically features learning at multiple levels of abstraction thereby allowing a system to learn complex functions mapping without depending on any specific algorithm.

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Understanding Deep Learning with Analogies

Let me start with a simple example which explains how things work at a conceptual level.

Example 1:

Let us try and understand how you recognize a square from other shapes.

The first thing is to check whether there are 4 lines associated with a figure or not (simple concept right!). If yes, we further check, if they are connected and closed, again if yes we finally check whether it is perpendicular and all its sides are equal (Correct!). Well, this nothing but a nested hierarchy of concept.

What we did, we took a complex task of identifying a square in this case and broke it into simpler tasks. Now, this Deep Learning also does this but on a larger scale.

Example 2:

Let’s take an example of a machine which recognises the animals. The task of the machine is to recognize whether the given image is of a cat or of a dog.

cat vs dog - analogy - AI vs Machine Learning vs Deep Learning

What if we’re asked to resolve the same issue using the concepts of machine learning, what we would do? First, we would define the features such as checking whether the animal has whiskers or not, or checking if the animal has pointed ears or not, or whether its tail is straight or curved.

In short, we will define the facial features and let the system identify which features are more important in classifying a particular animal.

Now when it comes to deep learning. It takes this one step ahead. Deep Learning automatically finds out the features which are important for classification, compared to Machine Learning where we had to manually give the features.

By now I guess my blog- AI vs Machine Learning vs Deep Learning has made you clear that AI is a bigger picture, and Machine Learning and Deep Learning are its subparts, so concluding it I would say the easiest way of understanding the difference between machine learning and deep learning is to know that deep learning is machine learning. More specifically, it’s the next evolution of machine learning. 

If you’re trying to grow your career in this exciting field, check out our Deep Learning Course. This course equips students with information about the tools, techniques, and tools they require to advance their careers.

Are you wondering how to advance once you know the basics of what Machine Learning is? Take a look at Edureka’s Machine Learning Certification, which will help you get on the right path to succeed in this fascinating field. Learn the fundamentals of Machine Learning, machine learning steps and methods that include unsupervised and supervised learning, mathematical and heuristic aspects, and hands-on modeling to create algorithms. You will be prepared for the position of Machine Learning engineer.

You can also take a Machine Learning Course Masters Program. The program will provide you with the most in-depth and practical information on machine-learning applications in real-world situations. Additionally, you’ll learn the essentials needed to be successful in the field of machine learning, such as statistical analysis, Python, and data science.

Original article source at: https://www.edureka.co/

#ai #machinelearning #deeplearning 

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Difference between: AI vs Machine Learning vs Deep Learning
Kennith  Kuhic

Kennith Kuhic


Machine Learning Vs Deep Learning: Difference Between Machine Learning and Deep Learning

Machine learning and Deep learning both are the buzzwords in the tech industry. Machine learning and deep learning both are the subdivision of artificial intelligence technology. If we further breakdown, deep learning is a subdivision of machine learning technology.

If you are familiar with the basics of machine learning and deep learning, it is excellent news!

However, if you are new to the AI field, then you must be confused. What is the difference between machine learning and deep learning?

There is nothing to worry about. This article will explain the differences in easy to understand language.

What is Machine Learning?

Machine learning is a branch of technology that studies computer algorithms. These algorithms allow the system to learn from data or improve by itself through experience. Machine learning algorithms make predictions or decisions without being explicitly programmed.

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Marget D

Marget D


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Nora Joy


Applications of machine learning in different industry domains

Machine learning applications are a staple of modern business in this digital age as they allow them to perform tasks on a scale and scope previously impossible to accomplish.Businesses from different domains realize the importance of incorporating machine learning in business processes.Today this trending technology transforming almost every single industry ,business from different industry domains hire dedicated machine learning developers for skyrocket the business growth.Following are the applications of machine learning in different industry domains.

Transportation industry

Machine learning is one of the technologies that have already begun their promising marks in the transportation industry.Autonomous Vehicles,Smartphone Apps,Traffic Management Solutions,Law Enforcement,Passenger Transportation etc are the applications of AI and ML in the transportation industry.Following challenges in the transportation industry can be solved by machine learning and Artificial Intelligence.

  • ML and AI can offer high security in the transportation industry.
  • It offers high reliability of their services or vehicles.
  • The adoption of this technology in the transportation industry can increase the efficiency of the service.
  • In the transportation industry ML helps scientists and engineers come up with far more environmentally sustainable methods for powering and operating vehicles and machinery for travel and transport.

Healthcare industry

Technology-enabled smart healthcare is the latest trend in the healthcare industry. Different areas of healthcare, such as patient care, medical records, billing, alternative models of staffing, IP capitalization, smart healthcare, and administrative and supply cost reduction. Hire dedicated machine learning developers for any of the following applications.

  • Identifying Diseases and Diagnosis
  • Drug Discovery and Manufacturing
  • Medical Imaging Diagnosis
  • Personalized Medicine
  • Machine Learning-based Behavioral Modification
  • Smart Health Records
  • Clinical Trial and Research
  • Better Radiotherapy
  • Crowdsourced Data Collection
  • Outbreak Prediction

Finance industry**

In financial industries organizations like banks, fintech, regulators and insurance are Adopting machine learning to improve their facilities.Following are the use cases of machine learning in finance.

  • Fraud prevention
  • Risk management
  • Investment predictions
  • Customer service
  • Digital assistants
  • Marketing
  • Network security
  • Loan underwriting
  • Algorithmic trading
  • Process automation
  • Document interpretation
  • Content creation
  • Trade settlements
  • Money-laundering prevention
  • Custom machine learning solutions

Education industry

Education industry is one of the industries which is investing in machine learning as it offers more efficient and easierlearning.AdaptiveLearning,IncreasingEfficiency,Learning Analytics,Predictive Analytics,Personalized Learning,Evaluating Assessments etc are the applications of machine learning in the education industry.

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Future of machine learning

Continuous technological advances are bound to hit the field of machine learning, which will shape the future of machine learning as an intensively evolving language.

  • Improved Unsupervised Algorithms
  • Increased Adoption of Quantum Computing
  • Enhanced Personalization
  • Improved Cognitive Services
  • Rise of Robots

Today most of the business from different industries are hire machine learning developers in India and achieve their business goals. This technology may have multiple applications, and, interestingly, it hasn’t even started yet but having taken such a massive leap, it also opens up so many possibilities in the existing business models in such a short period of time. There is no question that the increase of machine learning also brings the demand for mobile apps, so most companies and agencies employ Android developers and hire iOS developers to incorporate machine learning features into them.

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sophia tondon

sophia tondon


5 Latest Technology Trends of Machine Learning for 2021

Check out the 5 latest technologies of machine learning trends to boost business growth in 2021 by considering the best version of digital development tools. It is the right time to accelerate user experience by bringing advancement in their lifestyle.

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Verner  Hahn

Verner Hahn


AI vs Machine Learning vs Deep Learning | AI vs ML vs DL | Machine Learning Training with Python

This video is about the difference between the three terms Artificial Intelligence, Machine Learning & Deep Learning.
AI vs ML vs DL

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