Machine Learning Training in Vizag


Machine Learning is an advanced technology that allows different computers to tackle the tasks carried out by people. Whether it is a self-driving car or a translation speech, Machine Learning is the initial boost in the capabilities of Artificial Intelligence. But to get complete knowledge in this field, you have to join the Machine Learning course in Vizag and know whether the current boom in machine learning is possible?


Machine Learning Training in Vizag and know about Machine Learning?


  • In general, it is a process of making any computer system understand to make accurate predictions through a given set of data.
  • These predictions can be of any type from picking out the right fruit from the basket to observing people on the road in front of a self-driving car.
  • Machine Learning is also involved in finding the correct word in the sentence to decide an email and put it in the spam folder.
  • It can also recognize speech and generate captions for the YouTube video.


The main difference between Machine Learning and the traditional computer is, the developer doesn’t need to write any code for ordering the system to make the correct predictions.


Rather, a machine learning model knows itself to find the difference between all the predictions through a large amount of data fed in it. It is the data only which makes machine learning in predicting the right answer.


Difference between Artificial Intelligence and Machine Learning Course


  • Although Machine Learning has gained a lot of success and theories, it far steps behind in achieving the level of Artificial Intelligence.
  • In 1950, the birth year of Artificial Intelligence, it was defined as the machine having the ability to perform any task a human mind can do.
  • These systems are capable of performing some of the following functionalities like planning, reasoning, problem-solving, perception, social intelligence, etc.
  • On the other hand, Machine Learning involves various techniques and ways used in building Artificial Intelligence systems. It includes all the evolutionary computations needed for the system to undergo various mutations and combinations to give the perfect output.
  • The computers are programmed in such a way that they are capable of copying the exact behavior of the human, irrespective of any domain. The best example of this is an autopilot system in a flying plane.


Types of Machine Learning


  • Supervised Learning
  • Unsupervised Learning
  • Reinforcement Learning


Supervised Learning


  • In supervised learning, the machines are made to learn by feeding large amounts of data in it. At the time of training, for example, these systems are filled with large amounts of data of handwritten figures to indicate the right answers.
  • If they are packed with a sufficient amount of data, they can easily learn to judge different clusters of pixels and shapes associated with each number.
  • As a result of which they can easily be able to recognize handwritten numbers and be able to differentiate between 4 and 8 or so.
  • However, building a supervised machine learning system requires a lot of data in millions to give the best output as a result. Therefore, the datasets that are used to train these systems are extremely vast.
  • Some of the examples are like Google’s Open Images Dataset that has around 9 million images, the next is YouTube-8M videos suggestions that has around 7 million labeled videos to suggest each category or ImageNet that has around 14 million categorized images.
  • The most interesting part of these datasets is that they continuously keep growing, for example, Facebook has announced recently that it has assembled around 3.5 billion images available on Instagram.
  • These one billion photos are responsible for making the Facebook environment work efficiently and provide you the right suggestion through its image recognition system. 


Unsupervised Learning


  • In unsupervised learning, the machines are capable of learning with their past experiences through certain algorithms. Here, they try to find patterns in data and spot out the similarities and then divide them into different categories. 
  • The clustering of different houses available on rent under any website or grouping of the same stories together in Google News are the best examples of Unsupervised Learning.
  • The algorithms in unsupervised learning are not designed to give the output based on any specific data types. They simply learn from their past experiences and then they give the output based on similarities or differences. 


Semi-Supervised Learning


  • As the name suggests, the semi-supervised learning is the combination of both supervised and unsupervised learning.
  • It involves the technique of using a small amount of labeled and a large amount of unlabeled data to train the systems. In semi-supervised learning, initially, the labeled data is used to train the machine model, and then this model is used to label all the unlabeled data.
  • This process is known as pseudo-labeling. This model then automatically gets trained with the combination of both labeled and unlabeled data.
  • In recent years, the usage of semi-supervised learning has increased incredibly high by the Generative Adversarial Networks (GANs).
  • These are the machine learning systems that can generate new data from the past labeled data.
  • The example for this is creating new images of Pokemon from existing ones which in return helps train a machine learning model.


If by any means, semi-supervised learning can become as successful as supervised learning, the usage of huge amounts of computer power may end up. As a result, machine learning systems can be trained successfully without giving much access to a huge amount of labeled datasets.


Reinforcement Learning

  • Reinforcement Learning means whenever the machine gives any output, it is simply awarded or punished based on the result. Let us try to understand reinforcement learning with the help of a very simple example.
  • The technique of reinforcement learning is similar to a young boy who wants to play computer games for the first time. Initially, he must be not familiar with the rules and controls of the game.
  • But after playing for a certain limit, he starts learning all the rules of the game and relation between the controls. As a result, he scores more and more, and his performance upgrades every time he plays.


  • The best example of reinforcement machine learning is Google DeepMind’s Deep Q-network. It is one of the hardest video games which has beaten many users because of its learning ability. Here, the machine is fed with various pixels and then it decides the information about every state of the game.
  • It then gives the score based on the state of the game and the action it performs.
  • The more number of processes the machine completes through playing, the more advanced it will become, and it will be then more difficult to beat the machine. 


Why is Machine Learning So Successful?


The use of machine learning is not a new approach, but the interest in this area has boosted very much in recent years. The reason for the revival of machine learning is the deep learning that has set its records in the areas of speech and language recognition. 


There are mainly two factors that led to this enormous growth of machine learning.


  • The first one is the huge quantity of images, speech, videos, and texts through which the machine learning systems can be trained highly.
  • And the second and more important factor is the vast amounts of parallel processing power systems.
  • They are equipped with modern graphics processing units (GPUs) and if linked together into clusters, they are capable of forming machine learning powerhouses. 


Today, with an internet connection, anyone can easily use these clusters to train these models through services provided by big companies like Amazon, Google, and Microsoft. 


  • The use of Machine Learning has boosted exponentially, therefore, most of the companies are now designing specialized hardware adaptable to train various machine learning models.
  • An example of this can be taken as the custom chips like Google’s Tensor Processor Unit (TPU).
  • These are the latest version chips that can increase the rate of machine learning models and their libraries through which they are trained.


These chips are not limited to train only Google DeepMind or Google Brain but also have a powerful impact on Google Translate and image recognition in Google Photos. It also helps in the services that allow users to build machine learning models with Google’s TensorFlow Research Cloud. However, after the launching of the second generation of these chips at Google’s I/O conference, they became more capable and advanced with the new TPUs. 


Where is Machine Learning Used?


  • Machine Learning is used everywhere around us and is the basic element of the modern internet. It is used to recommend the product you want to buy from Amazon or suggest a video to be watched on NetFlix. 
  • Even all the search engines including Google use machine learning algorithms to personalize your search experience for obtaining better results.
  • The virtual assistants like Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft Cortana, are all built using machine learning algorithms to optimize your queries.
  • There is no limit to the advantages of machine learning in the modern era. 


Therefore, Machine Learning is an extremely vast field. I believe, through this article, you must have got a brief understanding of Machine Learning. Further, to learn more you can enroll in the best Machine Learning Training in Vizag.