Training & Internship - Deep Learning(Neural Network), Computer Vision, NLP & Generative AI

  • Course Duration 6 Weeks - 35 to 40 Hours
  • Course Mode Instructor Led Online Training
  • Date & Time 01-Feb-2025

About The Course

Explore the intricate world of Deep Learning and Neural Networks through this comprehensive course designed by AiCouncil. Delve into Multi-layered Neural Networks, Artificial Neural Networks, and advanced frameworks like Keras and TensorFlow. Learn Convolutional Neural Networks for Computer Vision, dive into Natural Language Processing techniques, and discover the fascinating realm of Generative AI. With hands-on exercises and projects, master text representation, sentiment analysis, and sequence-to-sequence models. Culminate with a showcase of NLP and Generative AI projects. This course equips you with essential skills and knowledge for tackling real-world challenges in AI and propels you towards exciting future opportunities.

Key Features

Instructor-Led, Interactive Training

Live, expert-led sessions for hands-on learning

Lifetime Access to Recordings

Revisit recorded classes anytime, at your convenience

Assignments & Real-Time Projects

Apply skills through practical projects after every module

Lifetime Job Assistance

Ongoing support for AI and Data Science job opportunities

3 Years of Technical Support

24/7 query resolution and technical help for 3 years

Globally Recognized Certification

Certified by AiCouncil, backed by Microsoft & AWS

Highlights

  • Comprehensive exploration of Deep Learning and Neural Networks
  • In-depth coverage of Multi-layered Neural Networks and Artificial Neural Networks
  • Hands-on experience with advanced frameworks like Keras and TensorFlow
  • Practical understanding of Convolutional Neural Networks for Computer Vision tasks
  • Dive into Natural Language Processing techniques for text analysis and generation
  • Introduction to Generative AI and its applications
  • Showcase your skills with NLP and Generative AI projects

Course Agenda

  • Role of Machine Learning in field of Artificial Intelligence
  • Deep Learning v/s Machine Learning
  • Brief History of AI Recap: SL, UL and RL
  • Classification and regression in supervised learning,
  • Clustering and association in unsupervised learning
  • Algorithms that are used in these categories
  • Introduction to AI and neural networks
  • Deep Learning: Successes Last Decade
  • What is AI and Deep Learning
  • Discussion: Self-Driving Car Object Detection
  • Applications of Deep Learning
  • Challenges of Deep Learning
  • Discussion: Sentiment Analysis Using LSTM
  • Multi-layer network introduction
  • Regularization
  • Deep neural networks
  • Overfitting and capacity
  • Neural network hyperparameters
  • Different activation functions used in neural networks: - ReLu, Softmax, Sigmoid, and hyperbolic functions
  • Back propagation, forward propagation, convergence, hyperparameters
  • Various methods that are used to train artificial neural networks
  • Perceptron learning rule
  • Tuning the learning rate
  • Regularization techniques
  • Optimization techniques
  • Stochastic process
  • Vanishing gradients
  • Transfer learning
  • Dropout layer
  • Regression techniques: - including Lasso L1 and Ridge L2
  • Understanding how Deep Learning works
  • Activation functions
  • Illustrating perceptron
  • Perceptron training
  • Multi-layer perceptron
  • Key parameters of perceptron
  • TensorFlow
  • Tensorflow and Its Ecosystem
  • Python libraries in TensorFlow : - code basics, variables, constants, placeholders, graph visualization
  • Use-case implementation
  • Keras
  • Keras high-level neural network for working on top of TensorFlow
  • Defining complex multi-output models
  • Composing models using Keras
  • Sequential and functional composition
  • Batch normalization
  • Deploying Keras with TensorBoard
  • Neural network training process customization.
  • Hands On: Build a Deep Learning Model Using Keras
  • Hands On: Build a Deep Learning Model Using Tensorflow
  • Using TFLearn API to implement neural networks
  • Defining and composing models
  • Deploying TensorBoard.
  • DNNs (Deep Neural Networks)
  • Introduction to deep neural networks (DNNs)
  • Several building blocks of artificial neural networks (ANNs)
  • The architecture of DNN and its building blocks
  • Various parameters, layers, and optimization algorithms in DNN activation functions.
  • Deep Neural Net optimization, tuning, interpretability
  • Optimization Algorithms
  • SGD, Momentum, NAG, Adagrad, Adadelta , RMSprop, Adam
  • Hands on Exercise: MNIST Dataset
  • Batch Normalization
  • Understanding Exploding and Vanishing Gradients
  • Hyperparameter Tuning
  • Interpretability
  • Width vs Depth
  • What is a convolutional neural network?
  • Understanding the architecture and use-cases of CNN
  • Pooling layers
  • Visualize CNN
  • How to fine-tune a convolutional neural network
  • Transfer learning
  • Understanding recurrent neural networks
  • Kernel filter
  • Feature maps, and pooling
  • Deploying convolutional neural networks in TensorFlow.
  • Introduction to unstructured data
  • Working with Image and Video
  • Image feature extraction & analysis
  • Image feature engineering
  • Creating Image feature matrix
  • Using Image features for Deep Neural Network
  • Facial Image Recognition
  • Object detection Model
  • Creating User Interface for face detection
  • Creating User Interface for object detection
  • Model Deployment for face and object detection
  • Server Deployment of model
  • Overview of NLP and its applications
  • Basic text preprocessing techniques (tokenization, stemming, lemmatization)
  • Introduction to NLTK and SpaCy libraries
  • Hands-on exercise: Text preprocessing with NLTK and SpaCy
  • Bag-of-Words model
  • TF-IDF (Term Frequency-Inverse Document Frequency)
  • Word embeddings (Word2Vec, GloVe)
  • Hands-on exercise: Implementing Bag-of-Words and Word Embeddings
  • Introduction to Deep Learning for NLP
  • Introduction to recurrent neural networks (RNNs)
  • Long Short-Term Memory (LSTM) networks
  • Applications of RNNs in sequence modeling and text generation
  • Hands-on exercise: Building an LSTM model for text generation
  • Sequence-to-Sequence Models
  • Introduction to sequence-to-sequence (seq2seq) models
  • Encoder-Decoder architecture
  • Attention mechanisms
  • Applications of seq2seq models in machine translation and text summarization
  • Hands-on exercise: Implementing a seq2seq model for machine translation
  • Advanced NLP Techniques
  • Named Entity Recognition (NER)
  • Part-of-Speech (POS) tagging
  • Sentiment analysis
  • Hands-on exercise: Implementing NER and sentiment analysis using pre-trained models
  • Overview of Generative AI and its applications
  • LLM Model
  • ChatGPT API
  • Prompt Engineering
  • Hands-on exercise: Generating text using RNN-LSTM
  • Participants present their NLP and Generative AI projects
  • Recap of key concepts and techniques covered in the course
  • Future directions in NLP and Generative AI
  • Course conclusion and certification distribution

 

Projects

Description: Develop a sophisticated facial recognition system using Convolutional Neural Networks (CNNs) and OpenCV. Utilize deep learning techniques to detect and recognize faces in images or real-time video streams. Implement features like face detection, identification, and authentication. Deploy the system with a user-friendly interface for seamless integration into security systems, access control, or surveillance applications.

Description: Build a prediction model to predict handwritten characters or numbers. Gain hands-on experience with utilizing image features for building predictive models. Develop a CNN model using features of handwritten images of characters or numbers to predict future input values by the user in the form of random images.

Description: Build an interactive sentiment analysis application using Natural Language Processing (NLP) techniques. Train Long Short-Term Memory (LSTM) networks to analyze the sentiment of text data, such as customer reviews or social media comments. Develop a user-friendly interface for users to input text and receive sentiment analysis results in real-time. This project can be applied to sentiment monitoring, brand reputation management, and market analysis.

Description: Develop an AI-powered chatbot capable of generating human-like text responses using Generative AI techniques. Train a language model, such as GPT (Generative Pre-trained Transformer), on a large corpus of text data. Implement a conversational interface where users can interact with the chatbot, asking questions or engaging in conversations. The chatbot should be able to generate coherent and contextually relevant responses, making it suitable for customer support, virtual assistants, or entertainment purposes.

Certification

Career Support

We have a dedicated team which is taking care of our learners learning objectives.

FAQ

There is no such prerequisite if you are enrolling for Master’s Course as everything will start from scratch. Whether you are a working IT professional or a fresher you will find a course well planned and designed to incorporate trainee from various professional backgrounds.
AI Council offers 24/7 query resolution, you can raise a ticket with a dedicated support team and expect a revert within 24 Hrs. Email support can resolve all your query but if still it wasn’t resolved then we can schedule one-on-one session with our instructor or dedicated team. You can even contact our support after completing the training as well. There are no limits on number of tickets raised.
AI council provide two different modes for training one can choose for instructor lead training or learning with prerecorded video on demand. We also offer faculty development programs for college and schools. apart from this corporate training for organization/companies to enhance and update technical skills of the employees. We have highly qualified trainers who are working in the training industry from a very long time and have delivered the sessions and training for top colleges/schools and companies.
We are providing a 24/7 assistance for the ease of the student. Any query can be raised through the interface itself as well as can be communicated through email also. If someone is facing difficulties with above methods mentioned above we can arrange a one on one session with the trainer to help you with difficulties faced in learning. You can raise the query throughout the total training period as well as after the completion of the training.
AI Council offers you the latest, appropriate and most importantly the real-world projects throughout your training period. This makes student to gain industry level experience and converting the learning’s into solution to create the projects. Each Training Module is having Task or projects designed for the students so that you can evaluate your learning’s. You will be working on projects related to different industries such as marketing, e-commerce, automation, sales etc.
Yes, we do provide the job assistance so that a learner can apply for a job directly after the completion of the training. We have tied-ups with companies so when required we refers our students to those companies for interviews. Our team will help you to build a good resume and will trained you for your job interview.
After the successful completion of the training program and the submission of assignments/quiz, projects you have to secure at least B grade in qualifying exam, AI Council certified certificate will be awarded to you. Every certificate will be having a unique number through which same can be verified on our site.
To be very professional and transparent No, we don’t guarantee the job. the job assistance will help to provide you an opportunity to grab a dream job. The selection totally depends upon the performance of the candidate in the interview and the demand of the recruiter.
Our most of the programs are having both the modes of training i.e. instructor led and self-paced. One can choose any of the modes depending upon their work schedule. We provide flexibility to choose the type of training modes. While registering for courses you will be asked to submit your preference to select any of the modes. If any of the course is not offered in both modes so you can check in which mode, the training is going on and then you can register for the same. In any case if you feel you need any other training mode you can contact our team.
Yes, definitely you can opt for multiple courses at a time. We provide flexible timings. If you are having a desire for learning different topics while continuing with your daily hectic schedule our course timing and modes will help you a lot to carry on the learning’s.
Whenever you are enrolling in any of the courses we will send the notification you on your contact details. You will be provided with unique registration id and after successful enrollment all of the courses will be added to your account profile on our website.AI Council provides lifetime access to course content whenever needed.
A Capstone project is an outcome of the culminating learning throughout the academic years. It is the final project that represents your knowledge, efforts in the field of educational learning. It can be chosen by the mentor or by the students to come with a solution.
Yes, for obtaining the certificate of diploma programmer you have to submit the capstone project.