Purvi Makwana's Books

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

By: Dilip Siddareddy,Dr. Haewon Byeon,Purvi Makwana,Dr. Vaibhav Bhatnagar

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MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

By: Dilip Siddareddy,Dr. Haewon Byeon,Purvi Makwana,Dr. Vaibhav Bhatnagar

Because of the advancements that have been made in machine learning, the world is being changed in ways that are difficult to conceive. If you stop for a second and take a good look around, you'll see that the area of data science is everywhere you turn. Take, for example, Alexa from Amazon; she is an artificial intelligence that has been developed to be as simple and straightforward to use as is humanly conceivable. There are many other digital assistants similar to Alexa, such as Google Assistant, Cortana, and so on. Alexa is not the only one of its sort. Therefore, the question of why they were formed in the first place is the most crucial one to ask; the question of how they developed is the second most important one to ask. In any event, we are going to make an attempt to study each and every one of these issues, and we are also going to make an effort to devise answers that are both logical and technological in nature. Within the scope of this discussion, the question that has to be inquired about first and foremost is, "What exactly are Machine Learning and Data Science?" A widespread misconception is that data science and machine learning are interchangeable terms for the same thing. Those people do have a point, to some extent, considering that data science is nothing more than taking a huge amount of data and analyzing it using a variety of machine learning approaches, methodologies, and technologies. Therefore, in order to become an expert in data science, you need to have a solid understanding of mathematics and statistics, in addition to a profound comprehension of the area that you intend to specialize in. To be more specific, what does it mean to have "subject expertise"? Subject expertise is nothing more than the knowledge necessary about a given topic in order to be able to abstract and calculate the data that pertains to that field, as the name of this type of expertise indicates. In a nutshell, these three concepts are considered as the foundations of data science, and if you are successful in mastering all of them, then you should rejoice yourself because you have achieved the level of an A-level data scientist.

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Page Count

203

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Xoffencerpublication

Published Date

ISBN 10

8119534603

ISBN 13

9788119534609

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

By: Mr. Srinivas Rao Adabala,Dr. Ravi Kumar,Purvi Makwana,Dr. Haewon Byeon

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DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

By: Mr. Srinivas Rao Adabala,Dr. Ravi Kumar,Purvi Makwana,Dr. Haewon Byeon

Deep learning has developed as a useful approach for data mining tasks such as unsupervised feature learning and representation. This is thanks to its ability to learn from examples with no prior guidance. Unsupervised learning is the process of discovering patterns and structures in unlabeled data without the use of any explicit labels or annotations. This type of learning does not require the data to be annotated or labelled. This is especially helpful in situations in which labelled data are few or nonexistent. Unsupervised feature learning and representation have seen widespread application of deep learning methods such as auto encoders and generative adversarial networks (GANs). These algorithms learn to describe the data in a hierarchical fashion, where higher-level characteristics are stacked upon lower-level ones, capturing increasingly complicated and abstract patterns as they progress. Neural networks are known as Auto encoders, and they are designed to reconstruct their input data from a compressed representation known as the latent space. The hidden layers of the network are able to learn to encode valuable characteristics that capture the underlying structure of the data when an auto encoder is trained on input that does not have labels attached to it. It is possible to use the reconstruction error as a measurement of how well the auto encoder has learned to represent the data. GANs are made up of two different types of networks: a generator network and a discriminator network. While the discriminator network is taught to differentiate between real and synthetic data, the generator network is taught to generate synthetic data samples that are an accurate representation of the real data. By going through an adversarial training process, both the generator and the discriminator are able to improve their skills. The generator is able to produce more realistic samples, and the discriminator is better able to tell the difference between real and fake samples. One meaningful representation of the data could be understood as being contained within the latent space of the generator. After the deep learning model has learned a reliable representation of the data, it can be put to use for a variety of data mining activities.

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207

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Xoffencerpublication

Published Date

ISBN 10

8119534174

ISBN 13

9788119534173

MORPHOLOGICAL IMAGE PROCESSING AND INTENSITY TRANSFORMATIONS

By: Sandeep Kumar Davuluri,Ismail Keshta,Purvi Makwana,Dr. Ashok Kumar

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MORPHOLOGICAL IMAGE PROCESSING AND INTENSITY TRANSFORMATIONS

By: Sandeep Kumar Davuluri,Ismail Keshta,Purvi Makwana,Dr. Ashok Kumar

The newspaper business was one of the pioneers in the usage of digital photographs and was one of the first businesses to do so. It was also one of the first businesses to make use of images, which were first transported by photographs, which were initially delivered by an underwater cable between London and New York. This connection was made between London and New York. These pictures were sent between the two locations on several occasions, making their way between both of them in a back and forth manner. When the Bartlane cable image transmission system was first put into operation in the early 1920s, the amount of time necessary to transfer a picture across the Atlantic was reduced from more than a week to less than three hours. This was a significant improvement over the previous method of transferring pictures. Before, this process had taken far longer. The images were first encoded via the use of specialised printing equipment in order to make them suitable for transmission through cable. Upon the travellers' arrival at their final location, the encoded photos were deciphered and then reassembled. This technique was used to send Figure 1.1, and its replication was carried out with the help of a telegraph printer that had been altered to contain fonts that approximated a halftone pattern.

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Page Count

238

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Xoffencerpublication

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ISBN 10

939470762X

ISBN 13

9789394707627

Book's by Purvi Makwana

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

MACHINE LEARNING FOR DATA SCIENCE - USING ML ALGORITHMS FOR PREDICTIVE MODELING

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

DEEP LEARNING FOR DATA MINING: UNSUPERVISED FEATURE LEARNING AND REPRESENTATION

MORPHOLOGICAL IMAGE PROCESSING AND INTENSITY TRANSFORMATIONS

MORPHOLOGICAL IMAGE PROCESSING AND INTENSITY TRANSFORMATIONS

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