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Our machine learning training course aims to equip students with the skills and knowledge necessary to excel in the rapidly growing fields of artificial intelligence and data science. Our comprehensive curriculum provides a solid foundation in machine learning concepts, algorithms, and applications, enabling students to tackle real-world problems effectively.
Robotic Process Automation is an automation technology which helps to reduce the business process task and action barrier to handle the user interface in the application and to improve the productivity. RPA training will help you to learn the automated tools and robotic automation to develop the system based on the AI concepts that require for the business. Earning RPA certification demonstrates your knowledge to manage and execute business process automation that carries you an RPA job globally.
Artificial Intelligence Training provides individuals with a comprehensive understanding of AI's fundamental concepts, algorithms, and techniques. It equips them with the practical skills to design and implement AI solutions. From supervised and unsupervised learning to neural networks and deep learning, our AI training ensures learners are well-prepared to tackle real-world AI challenges.
Unlock the power of UiPath Studio and become proficient in Robotic Process Automation (RPA). Whether you have a programming background or are new to automation, our UiPath training is designed to cater to different learning paces. With a structured curriculum, hands-on exercises, and access to active forums, you’ll gain the skills needed to automate complex workflows efficiently.
Natural language processing (NLP) is an incredible technology that understands the human language that is spoken or written, which I usually referred as natural language. Natural language processing is a branch of artificial intelligence that existed for over fifty years. The roots of natural language processing are in linguistics.
Natural language processing is a part of linguistics, computer science, and artificial intelligence. This technology is widely used in analyzing heavy text-based data and unstructured data.
The course in Natural language processing will impart all the necessary skills and knowledge to design applications that will transform the future with artificial intelligence.
By learning this course, you will learn,
The NLP specialization is an amalgamation of several courses and modules. You can get started with one to begin with and then proceed with others at your convenience. You will work on hands-on projects and also earn a shareable certificate.
The entire course is imparted in several modules.
Natural language processing with classification: In this module, you will gain the fundamental knowledge of natural language processing, followed by concepts of neural networks for sentiment analysis. You will then learn to analyze sentiment using naïve Bayes and logistic regression. The course delves into supervised machine learning and sentiment analysis. You can identify and discover the relationship between words with vector space models. Use the K-nearest neighbour search to write a translation algorithm.
Natural language processing with probabilistic models: In this module, you will use probabilistic models to auto-correct words, correct misspelt words, and build dynamic programming. You will learn to make your spell checker to correct incorrectly spelt words. You can create parts of speech with Markov chains and Hidden Markov models. You can build your autocomplete language model with text corpus and N-gram language models. You can also create your continuous bag of words model with neural networks. You will also learn endless bag of words (CBOW) architecture and learn to implement it.
Recurrent neural networks for language modelling: In this module, you will learn to use recurrent neural networks(RNN) and Gated recurrent unit networks (GRU) on sequential data and predict text. You will also appreciate comparing traditional language models and RNN & GRU.
Vector space models: In this module, you will learn to capture the semantic relationship between words with the Vector space model. You will learn to capture the dependencies and relationships between words and visualize the same using PCA. The vector space model represents the objects or text as vectors in an algebraic model.
Machine translation and document search: In this module, you will learn to assign and transform word vectors. You can assign them to subsets using locality-sensitive hashing. You will learn to perform machine translation and document search. You will learn about K nearest neighbours, hash tables, multiple planes, searching documents, hash tables and hash functions, approximate nearest neighbours, etc.
Sentiment analysis with logistic regression: In this module, you will learn to extract features from the text. You can then convert text to numerical vectors and build binary classifiers using logistic regression. You will learn supervised machine learning and sentimental analysis, vocabulary, and feature extraction concepts.
Sentiment analysis with Naïve Bayes: In this module, you will learn the basics of sentimental analysis. You will know logistical regression cost function and gradient. You will understand how to apply the Naïve Bayes, the underlying assumptions, and error analysis.
Long short-term memory units (LSTMs) and Named Entity Recognition systems: In this module, you will learn to solve the vanish gradient problem with long-term memory. You will also learn to extract critical information from text with a named entity recognition system. To implement LSTM, you will learn LTSM architecture and LTSM equations.
Siamese Networks: In this module, you will learn about a special type of neural network that can identify duplicates from a Quora data set. The Siamese network is a special neural network built with two identical networks. The present module imparts knowledge of Siamese Networks, Siamese architecture, and cost function.
Neural Machine Translation: In this module, you will understand the shortcomings of the sequence two sequence model and how to resolve the issues around it. The attention mechanism certainly helps to determine the shortcomings of the traditional sequence two sequence model. You will learn to build the neural machine translation model with an attention mechanism which enables translation from English to German. You will get acquainted with concepts of beam search, Seq2seq, queries, keys, values and attention.
Text Summarizations: In this module, you will learn to create a tool that generates text summaries. You will gain knowledge of comparing the RNN and sequential models. In this module, you will get an overview of transformers and transformer applications, understand the concepts of multi-head attention and masked-self attention, transformer decoder, multi-head attention, Scaled and Dot-Product Attention and many more challenging concepts.
Question and answering: You will learn ELMo, GPT, T5 and BERT models in this model. You will learn transfer learning in natural language processing and get introduced to Hugging face introduction.
Chatbots: In this model, you will learn to build chatbots using reformer models. You will also get acquainted with the unique challenges that transformer models face and how to solve them. You will learn the concepts of long sequences, LSH attention, Reversible Residual Layers, Transformer Complexity, and Motivation for Reversible Layers. You will review KNN & LSH concepts.
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