Deep Learning and Neural Network Training & Certification

  • Course Duration40 Hrs.
  • Course ModeInstructor Led Training
  • Course Fee₹ 11700

About The Course

AICouncils’ Deep learning course is designed by AI professionals in such a way that each and every concept related to artificial neural networks, tensorflow framework, complex algorithms and data and related projects development and deployment can be understood with real time Hands on activities and learnings. On completion participants can develop their own deep learning models and build up some real world projects viable across healthcare, genomics, cybersecurity, e-commerce, agriculture and other sectors.

Key Features

Instructor–led training

Highly interactive instructor-led training

Free lifetime access to recorded classes

Get lifetime access of all recored classes in your profile

Regular assignment and assessments

Real-time projects after every module

Lifetime accessibility

Lifetime access and free upgrade to the latest version

3 Years of technical support

Lifetime 24/7 technical support and query resolution

Globally Recognized Certification

Get global industry-recognized certifications


  • Deep Learning Fundamentals development
  • Building of Artificial Neural Networks and Statistical Models
  • Deep Learning techniques and working with TensorFlow
  • Bulding up ANN, CNN and RNN
  • Using computer vision and sequence modelling
  • Natural Language Processings

Mode of Learning and Duration

  • Weekdays – 7 to 8 weeks
  • Weekend – 9 to 10 weeks
  • FastTrack – 6 to 7 weeks
  • Weekdays – 7 to 8 weeks
  • Weekend – 9 to 10 weeks
  • FastTrack – 6 to 7 weeks


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
  • Fundamentals of statistics
  • Hypothesis testing
  • Probability distributions
  • Hidden Markov models
  • Multi-layer network introduction
  • Regularization
  • Deep neural networks
  • Multi-layer perceptron
  • Overfitting and capacity
  • Neural network hyperparameters
  • Logic gates
  • Different activation functions used in neural networks: - ReLu, Softmax, Sigmoid, and hyperbolic functions
  • Back propagation, forward propagation, convergence, hyper parameters
  • Various methods that are used to train artificial neural networks
  • Perceptron learning rule
  • Gradient descent 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
  • Unsupervised pre-training
  • Xavier initialization and more
  • 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
  • Introduction to deep neural networks (DNNs)
  • Several building blocks of artificial neural networks (ANNs)
  • The architecture of DNN and its building blocks
  • Reinforcement learning in DNN concepts
  • various parameters, layers, and optimization algorithms in DNN
  • activation functions
  • 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 the RNN model
  • Use cases of RNN
  • Modeling sequences
  • Training RNNs with back propagation
  • Long short-term memory (LSTM)
  • Recursive Neural Tensor Network theory
  • The basic RNN cell
  • Unfolded RNN
  • Dynamic RNN
  • Time-series predictions
  • GPU in Deep Learning v/s CPU in Deep Learning
  • The significance of GPUs in training Deep Learning networks
  • Forward pass and backward pass training techniques
  • Introduction to RBM and autoencoders
  • Deploying RBM for deep neural networks
  • Using RBM for collaborative filtering
  • Autoencoders’ features
  • Applications of autoencoders
  • Hands on: Autoencoder Model for MNIST Data
  • What is Time Series
  • Techniques and applications
  • Components of Time Series
  • Moving average
  • Smoothing techniques
  • Exponential smoothing
  • Univariate time series models
  • Multivariate time series analysis
  • Arima model
  • Time Series in Python
  • Sentiment analysis in Python (Twitter sentiment analysis)
  • Text analysis
  • Hands-on Exercise – Forecasting feature value in a series after analysing time-series data and sequence of measurements to understand nature of phenomenon
  • Time series techniques and applications
  • Time series components
  • Moving average, smoothing techniques, exponential smoothing
  • Univariate time series models
  • Multivariate time series analysis
  • ARIMA model
  • Time series in Python
  • Sentiment analysis in Python (Twitter sentiment analysis)
  • Text analysis
  • Hands-on Exercise – Analysing and Forecasting using time series
  • Image processing
  • Natural Language Processing (NLP)
  • speech recognition
  • Video analytics
  • Automated conversation bots leveraging any of the following descriptive techniques: IBM Watson, Microsoft’s Luis, Google API.AI, Amazon Lex, Open–Closed domain bots, Generative model
  • Sequence to sequence model (LSTM)



Industry: - Stock market trading

Problem Statement: - Make a prediction model to predict price of stock

This project is to predict the volatility and price value of a stock by analysing the change with time and comparing multiple stocks with time. By implementing recurrent neural network, LSTM and time series you can make a predictive model which can generates the output close enough to the real stock prices in real time.

Industry: - Miscellaneous

Problem Statement: - Make a prediction model to predict handwritten characters or numbers.

Here you will get hands on experience with how to use features of images for building up predictive model. We will develop a CNN model using features of hand written images of characters or numbers to make a prediction over future input values by the user in the form of random images.

Industry: - Ecommerce

Problem Statement: - Build an Artificially intelligent chatbot

There is an Ecommerce platform wants to provide best in class services to the user through most interactive AI based chatbot. Here you will use NLP and neural network based model to understand the customer need and respond accordingly. It will be great hands on experience with Tensorflow components, natural language processing and querry handling.

Industry: - Search Engine

Problem Statement: - Build a model to search most relatable image over internet using the image given by user

This project will be build up using Tensorflow and CNN to best analyse an image after the training given to the model. You need to train a model, make the losses to least possible value and distribution of activation and gradients. Complete feature engineering over unstructured data set will be understood and practised on completion of this hands-on experience.



Career Support

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


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.