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Gain excellent AI skills with a contemporary Artificial Intelligence training and certification course. Join this AI training and get great exposure to industry practices. Be the most wanted candidate in the industry with this online AI training course.
Artificial Intelligence is a part of computer sciences that give special importance in the creation of intelligent machines that work and reacts like humans. It has become an important part of the IT Industry.AI is achieved by studying how the human brain thinks, learn, decide and work while trying to solve a problem and an on the basis of the outcomes of this study an intelligent software and systems developed.
Specifications
Note: Course Curriculum may increase or decrease as per industries need
- Get a full-fledged Introduction to AI
- Learn about the Components of AI
- Learn about the advantages of using AI?
- Learn about the Applications of AI in various industries
- Learn about the Meaning, Scope, and Stages Of Artificial Intelligence
- Get a sound introduction to the installation of Anaconda
- Learn about the Creating of Environment with stable Python version
- Get a sound Introduction to TensorFlow, Keras, OpenCV, Caffe, Theano
- Learn about Installation of required libraries
- Get knowledge of Industry Applications of AI
- Learn about the benefits Multiple Industries
- Get a sound Introduction to Neural Networks
- Learn about the Gradient Descent
- Get an Introduction to Perceptron & Neural Networks
- Learn about the Batch Normalization procedures
- Learn about the Activation and Loss functions
- Learn about the Hyperparameter tuning
- Learn about the Deep Neural Networks
- Get knowledge of Tensor Flow & Keras for Neural Networks & Artificial Intelligence
- Get knowledge of Understand Neural Networks in Detail
- Get knowledge of Illustrating Multi-Layer Perceptron
- Learn about the Backpropagation – Learning Algorithm
- Learn about the Understanding of Backpropagation – Using Neural Network Example
- Learn about the MLP Digit-Classifier using TensorFlow
- Learn about the Building of a multi-layered perceptron for classification
- What is a Convolutional Neural Network
- Get an Understanding the architecture of CNN
- Learn about the Use cases of CNN
- What is a pooling layer
- How to visualize using CNN
- How to fine-tune a Convolutional Neural Network
- What is Transfer Learning
- Get an Understanding of Recurrent Neural Networks
- Learn about the feature maps, Kernel filter, pooling
- Learn about the Deploying convolutional neural network in TensorFlow
- Get an introduction to RBM and autoencoders
- Learn about the Restricted Boltzmann Machine
- Learn about the Applications of RBM
- Get an Introduction to Autoencoders
- Learn about the Autoencoders applications
- Get a good Understanding Autoencoders
- Learn about the Building an Autoencoder model
- Learn about the Collaborative Filtering with RBM
- Learn how to deploy it for deep neural networks
- Learn about the Text generation using LSTM and generative recurrent networks
- Get ab Understanding about DeepDream algorithm
- Learn about the Image generation using variational autoencoders
- Get an understanding of GANs theory and practical models
- Learn about The Generator, the Discriminator, the Adversarial network
- Deep Convolutional Generative Adversarial networks
- Learn about the Producing audio using GAN
- Learn about Unsupervised learning using Autoencoders
- Learn about the Binary classification problem using MLP on IMDB dataset
- Learn about the Multi-class classification problem using MLP on Reuters dataset
- Learn about the Regression problem using MLP on Boston Housing dataset
- Get a good knowledge of the Types of Machine Learning outcomes
- Learn about the Handling imbalanced datasets and avoiding overfitting and underfitting
- Learn about the Simple hold-out validation
- Learn about the K-Fold validation
- What is Iterated K-fold validation with shuffling
- Learn what is Adding weight regularization
- Learn about the Drop Out and Drop Connect
- Get knowledge of Adding Noise – Data Noise, Label Noise, Gradient Noise
- Learn about the Batch Normalization
- Learn about the Data Augmentation
- Learn about the Weight initialization technique
Certifications
Accommodation Facilities
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- Delivery ModeOnline & offline Class
- Program Fees
₹13999
- Registration Fee
₹2499
- Payment OptionsDebit card, Credit card,UPI,Wallet, No Cost EMI
(18% GST already included)