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It's acceptable that Natural Language Processing, or NLP, is one of the most significant and demanded technologies of the present world. You can think that it's everywhere as individuals communicate nearly everything in language: it is available in web searches, advertisement, emails, customer service, language translation, summaries, etc.
These days, with understanding that processing complex expressions is a significant part of artificial intelligence, deep learning approaches have gotten superior across various NLP tasks. Let's look at some of the top NLP courses which you can study at home in your comfort – Online!
This course covers a wide scope of tasks in Natural Language Processing from essential to cutting-edge: sentiment analysis, summarization, dialogue state tracking, to give some examples. After finishing, you will have the option to perceive NLP tasks in your everyday work, propose approaches, and judge what strategies are probably going to function admirably.
This course is the part of the deep learning specialization that instructs you to utilize TensorFlow to assemble NLP frameworks. It includes topics like tokenizing and representing sentences as vectors with the goal that they can be utilized as inputs to neural systems. After the model has been made by utilizing the applicable procedures, you will figure out how to train the LSTM that can be valuable in making unique verses.
The course is structured as a prologue to the crucial concepts of Natural Language Processing (NLP) with Python. Mainly centered around working with NLTK, it gives the possibility of such NLP tasks as word tagging and chunking. As an enhancement, it presents certain machine learning algorithms, for example, credulous Bayes.
This course centers around "how to build and comprehend", not only "how to utilize". Anybody can figure out how to utilize an API shortly in the wake of reading some documentation. It's not tied in with "remembering facts", it's tied in with "seeing with your own eyes" through experimentation. It will show you how to visualize what's going on in the model inside. If you need something beyond a shallow look at machine learning models, this course is for you.
This program is intended to offer you an overview of the methods to take a shot at NLP and pertinent machine learning procedures. Apart from this, you will likewise find out about statistical machine translation, DSSM and how they can be applied to build solutions to real-life problems. End the classes by seeing how reinforcement learning can be applied.
An expert level course addressing NLP tasks from the point of view of Artificial Intelligence. The course will lead you through exemplary machine learning techniques applied to tackle NLP issues, including Statistical Machine Translation, Deep Semantic Similarity Models as well as strategies applied in Natural Language Understanding and Image captioning and visual question answering.
The course will show you those key concepts of natural language processing by executing practical exercises which depend on real world examples. You will become familiar with the theory, yet get hands on work on building these natural language processing algorithms.
In this intelligent course, you will start with the nuts and bolts, and ideas of NLP like how to identify words and recover points from a text. In the practical exercises, you will see how to create fake news classifiers and utilize common libraries to solve issues. After the culmination of the program, you will be prepared to take on intermediate and advanced topics of this area.
This course will assist you in executing the methods utilizing real data obtained from different sources. Numerous courses go through made-up information that doesn't engage students to implement R based data science in real life. Subsequent to taking this course, you'll effectively utilize packages like caret, dplyr to work with real data in R.
You will likewise figure out how to utilize common social media mining and natural language processing packages to extract insights from text data.
A far reaching course giving extensive further readings. It offers a scientific methodology with theories lying behind the models.The course covers vector representations, window-based neural networks, recurrent neural networks, long-short-term-memory models, recursive neural networks, convolutional neural networks as well as some latest models involving a memory component.
