What you'll learn

  • Python End to End
  • Statistics
  • API
  • Advanced NLP
  • Chatbot
  • DataBases
  • Exploratory Data Analysis
  • Deep Learning
  • Advanced Computer Vision

Why Learn Deep learning with Advance Computer Vision and NLP Masters At iNeuron?

Learn Deep learning with Advance Computer Vision and NLP at iNeuron and empower yourself with immense knowledge. The course consists of a vast curriculum that will provide the candidate basic as well as advanced knowledge combined with multiple live-projects providing the candidate with an all-round Deep learning with Advance Computer Vision and NLP knowledge and experience.

  • Students:14000+
  • Duration:8 months
  • Major Projects:30+
  • Minor Projects:10+
  • Mode:Online
  • Job Assistence:100%
  • Certificate:Yes
  • Mock Interviews:Yes

Course Curriculum

  • Computational Linguistic
  • History of NLP
  • Why NLP?
  • Use of NLP

  • Tensorflow installation 2.0
  • Tensorflow installation 1x with Virtual Environment
  • Tensorflow 2.0 function
  • Tensorflow 2.0 neural network creation
  • Tensorflow 1x functions
  • Tensorflow 1x neural network and its function
  • Keras introduction
  • Keras in depth with neural network creation
  • Mini project in Tensorflow

  • Pytorch installation
  • Pytorch functional overview
  • Pytorch neural network creation

  • A simple Perceptron
  • Neural network overview and its use case
  • Various neural network architecture overview
  • Use case of neural network in NLP and computer vision
  • Multilayer Network
  • Loss Functions
  • The Learning Mechanism
  • Optimizers
  • Forward and Backward Propagation
  • Gradient descent

  • CNN defination and various CNN based architecture explanation
  • End to end CNN network training and its deployment in Azure cloud
  • Preformance tuning of CNN trained CNN network

  • GAN
  • Generative Model Using GAN
  • BERT
  • Semi-supervised learning using GAN
  • CNN Architectures
  • LeNet-5
  • AlexNet
  • GoogLeNet
  • VGGNet
  • ResNet
  • SSD
  • Faster R CNN

  • Mask R-CNN
  • Xception
  • Facenet
  • Implementing a ResNet-34 CNN Using Keras
  • Using Pretrained Models From Keras
  • Pretrained Models for Transfer Learning
  • Classification and Localization
  • Tensorflow Object Detection
  • You Only Look Once (YOLO)
  • Semantic Segmentation

  • Intents and Entities
  • Fulfilment and Integrations
  • Chatbot using Microsoft bot builder and LUIS, deployment to Telegram, Skype
  • Chatbot using Goole Dialogflow, deployment to Telegram, Skype
  • Chatbot using Amazon Lex, deployment to Telegram, Skype
  • Chatbot using RASA NLU, deployment to Telegram, Skype

  • Importing Text
  • Web Scrapping
  • Text Processing
  • Understanding Regex
  • Text Normalization
  • Word count
  • Frequency Distribution
  • Text annotation
  • Use of anotator
  • String Tokenization
  • Annotator creation
  • Sentence processing
  • Lemmatization in text processing
  • POS
  • Named Entity Recognition
  • Dependency Parsing in text
  • Coreference Resolution
  • Information Extraction from text
  • Sentiment analysis

  • Spacy overview
  • Spacy funtion
  • Spacy function implementation in text processing
  • POS tagging, challenges and Accuracy
  • Entities and named Entity Recognition
  • Interpolation, Language models

  • Morphology and Diversity
  • Ambiguity and Paradigms
  • Structures and Meanings
  • Lexical Knowledge, Network Metaphors and co references
  • Lexical ambiguity
  • Polysemy and homonymy
  • Coreference resolution
  • Anaphora and cataphora resolution
  • Multi-sentential resolution
  • Humans and ambiguity
  • Machines and ambiguity
  • Co-occurrence and distributional similarity
  • Similarity and relatedness
  • Knowledge graphs and repositories
  • Computational linguistics
  • Word embeddings and co-occurrence vectors
  • WordSim353 Dataset examples
  • Word2Vec
  • Part-of-speech tagging

  • Recurrent Neural Networks
  • Long Short Term Memory(LSTM)
  • Bi LSTM
  • GRU Implementation
  • Building a Story writer using character level RNN

  • Seq2Seq
  • Encoders and Decoders
  • Attention Mechanism
  • Attention Neural Networks
  • Self Attention

  • GPU introduction
  • Various type of GPU configuration
  • GPU provider and its pricing
  • Paperspace GPU setup
  • Running model in GPU

  • Introduction to Transformers
  • BERT Model
  • ELMo Model
  • GPT1 Model
  • GPT2 Model
  • ALBERT Model
  • DistilBERT Model

  • Machine Translation
  • Abstractive Text Summarization
  • Keyword Spotting
  • Language Modelling
  • Document Summarization

  • Deep learning model deployment strategies
  • Deep learning project architecture
  • Deep learning model deployment phase
  • Deep learning model retraining approach
  • Deep learning model deployment in AWS
  • Deep learning model deployment in Azure
  • Deep learning model deployment in GCloud

Projects

  • Machine Translation
  • Question Answering(Like Chat-bot)
  • Sentiment Analysis imdb
  • Text Search(with Synonyms)
  • Text Classifications
  • Spelling Corrector
  • Entity(Person, Place, or Brand) Recognition
  • Text Summarization
  • Text Similarity(Pharaphrase)
  • Topic Detection
  • Langauage Identification
  • Document Ranking
  • Fake News Detection
  • Plagiarism Checker
  • Text summariaton extractive
  • Text summariaton abstaractive

  • Movie review using BERT
  • Ner using BERT
  • POS BERT
  • Text generation gpt 2
  • Text summarization xlnet
  • Abstract BERT
  • Machine translation
  • NLP text summarization custom Keras/Tensorflow
  • Language identification
  • Text classification using fast-bert
  • neuralcoref
  • detecting-fake-text_using_gltr_with_bert_and_gpt-2
  • FakeNewsDetectorUsingGPT2
  • Python-Plagiarism-CheckerType a message
  • Question answering

  • Topic modeling
  • Word sense disambiguation
  • Text to speech
  • Keyword Spotting
  • Document Ranking
  • Text Search(with Synonyms)
  • Language Modelling
  • Spam detector
  • Image captioning

  • Traffic Surveillance System
  • Object Identification
  • Object Tracking
  • Object Classification
  • Tensorflow Object Detection
  • Image to Text processing
  • Speech to Speech analysis
  • Vision Based Attendance System

Career services

  • Resume discussion - 1st round
  • Resume discussion -2nd round
  • Devops infrastructure
  • Discussion on project explanation in interview
  • Data scientist day to day work
  • Data scientist roles and responsibilities
  • Companies which hire a data scientist
  • Interview prepration
  • Career Counselling
  • Job alerts
  • Project support
  • Learning support

Batch Timings & Fee

Fee Structure

₹3,540

incl GST
Online

Start Date : 10th October 2020

Sat & Sun(Live Session):
  • 10:00 AM to 12:0 PM IST

Thursday(Live Doubt Clearing): 8:00 PM - 10:00 PM IST

Deep learning with Advance Computer Vision and NLP Masters from iNeuron

Complete your Deep learning with Advance Computer Vision and NLP at iNeuron and get your certificate.

  • 5 Months live session training
  • 3 Months in-house internship
  • Resume discussion - 1st round
  • Resume discussion -2nd round
  • Devops infrastructure
  • Discussion on project explanation in interview
  • Data scientist day to day work
  • Data scientist roles and responsibilities
  • Companies which hire a data scientist
  • Interview prepration
  • Career Counselling
  • Job alerts
  • Project support
  • Learning support
Certificate

Our Features

5 Months Classroom Training

Once on-board our candidates will go through an intense classroom training session of 5 months conducted by our best team of experienced senior data scientists, who will provide all conceptual knowledge in an innovative as well as an interactive manner.

Career Counselling

After successful completion of our course, we train our candidates via mock interviews, personal interviews, and group discussions as well as provide them with professional mentoring and help them build an attractive resume which will enable them to fetch a lucrative job.

3 Months Of In-House Projects

What makes us different from other training institutes is that we are also into product development and that enables us to provide hands-on experience to our candidates to contribute in live projects, which will result in a deeper understanding of the course with industry-level knowledge.

Admission Process

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