Data Science and Machine Learning are the hottest skills in demand but challenging to learn. Did you wish that there was one course for Data Science and Machine Learning that covers everything from Math for Machine Learning, Advance Statistics for Data Science, Data Processing, Machine Learning A-Z, Deep learning and more? Well, you have come to the right place. This Data Science and Machine Learning course has 11 projects, 250 lectures, more than 25 hours of content, one Kaggle competition project with top 1 percentile score, code templates and various quizzes. Today Data Science and Machine Learning is used in almost all the industries, including automobile, banking, healthcare, media, telecom and others.

Artificial intelligence has made stunning strides, but it often needs absurd amounts of data and computer power to get there. Now some AI researchers are focused on making the technology as efficient as possible. Last week researchers showed it is possible to squeeze a powerful AI vision algorithm onto a simple, low-power computer chip that can run for months on a battery. The trick could help bring more advanced AI capabilities, like image and voice recognition, to home appliances and wearable devices, along with medical gadgets and industrial sensors. It could also help keep data private and secure by reducing the need to send anything to the cloud.

Welcome to KGP Talkie's Natural Language Processing (NLP) course. It is designed to give you a complete understanding of Text Processing and Mining with the use of State-of-the-Art NLP algorithms in Python. We will learn Spacy in detail and we will also explore the uses of NLP in real-life. This course covers the basics of NLP to advance topics like word2vec, GloVe, Deep Learning for NLP like CNN, ANN, and LSTM. I will also show you how you can optimize your ML code by using various tools of sklean in python.

Artificial intelligence has made stunning strides, but it often needs absurd amounts of data and computer power to get there. Now some AI researchers are focused on making the technology as efficient as possible. Last week researchers showed it is possible to squeeze a powerful AI vision algorithm onto a simple, low-power computer chip that can run for months on a battery. The trick could help bring more advanced AI capabilities, like image and voice recognition, to home appliances and wearable devices, along with medical gadgets and industrial sensors. It could also help keep data private and secure by reducing the need to send anything to the cloud.

As the success of AlexNet drew great attention in the area of computer vision and reignited the hope of neural networks, large tech companies joined the force of developing deep learning frameworks. Among them, Google open sourced the famous TensorFlow framework that is still the most popular deep learning framework in ML field up to date. The inventor of Caffe joined Facebook and continued the release of Caffe2; at the same time, Facebook AI Research (FAIR) team also released another popular framework PyTorch which was based on the Torch framework but with the more popular Python APIs. Microsoft Research developed the CNTK framework. And Amazon adopted MXNet, a joint academic project from University of Washington, CMU and others.

AudioSet Dataset is developed by the Google Sound and Video Understanding team. There are other twelve contributors to AudioSet DataSet who help to build a pipeline for the data storage in the form Youtube_url Id, start_time, end_time, and other classes. AudioSet Dataset has more than 600 classes of annotated sound, 6000 hours of audio, and 2,084,320 million YouTube videos annotated videos and containing 527 labels. Each video has a 10 sec sounds clip extracted from Youtube Videos in different classes for the training and testing dataset. AudioSet Ontology is the collection of sound in hierarchical and organized.

Following MIT, researchers at NVIDIA have recently developed a new augmented method for training Generative Adversarial Networks (GANs) with a limited amount of data. The approach is an adaptive discriminator augmentation mechanism that significantly stabilised training in limited data regimes. Machine learning models are data-hungry. As a matter of fact, in the past few years, we have seen that models that are fed with silos of data produce outstanding predictive outcomes. Alongside, with significant growth, Generative Adversarial Networks have been successfully used for various applications including high-fidelity natural image synthesis, data augmentation tasks, improving image compressions, etc. From emoting realistic expressions to traversing the deep space, and from bridging the gap between humans and machines to introduce new and unique art forms, GANs have it all covered. Although deep neural network models, including GANs, have shown impressive results, yet there remains a challenge of collecting a large number of specific datasets.

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Deep learning is notorious for being an energy-intensive field that sees its applications limited. But what if these models could be run with higher energy efficiency? That is a question many researchers have asked, and a new team from IBM may have found an answer. New research being presented this week at NeurIPS (Neural Information Processing Systems -- the biggest annual AI research conference) showcases a process that could soon reduce the number of bits needed to represent data in deep learning from 16 down to four without the loss of accuracy. "In combination with previously proposed solutions for 4-bit quantization of weight and activation tensors, 4-bit training shows a non-significant loss in accuracy across application domains while enabling significant hardware acceleration ( 7 over state of the art FP16 systems)," write the researchers in their abstract.

The past week was a momentous occasion for protein structure prediction, structural biology at large, and in due time, may prove to be so for the whole of life sciences. CASP14, the conference for the biennial competition for the prediction of protein structure from sequence, took place virtually over multiple remote working platforms. DeepMind, Google's premier AI research group, entered the competition as they did the previous time, when they upended expectations of what an industrial research lab can do. The outcome this time was very, very different however. At CASP13 DeepMind made an impressive showing with AlphaFold but was ultimately within the bounds of the usual expectations of academic progress, albeit at an accelerated rate. At CASP14 DeepMind produced an advance so thorough it compelled CASP organizers to declare the protein structure prediction problem for single protein chains to be solved. In my read of most CASP14 attendees (virtual as it was), I sense that this was ...