Artificial Intelligence in Hyderabad

This module will equip the candidate with the knowledge of Nural Networks. Gain Comprehensive knowledge about the Activation Functions and feedforward neural network. Learn about backpropagation and gradient descent. Know about the full connected layer forward and backward pass. Get acquainted with Data Preprocessing, Data Augmentation, weight initialization, working with google collab, and more

• Introduction to Neural Networks and Deep Learning
• Activation Functions
• Feedforward Neural Network
• Backpropagation and Gradient Descent
• Learning Rate Setting and Tuning
• Introduction to Keras
• Fully Connected Layer-Forward and Backward Pass
• Softmax and Cross-Entropy Loss
• Introduction to babysitting the learning Process12
• Data Preprocessing
• Data Augmentation
• Weight Initialization
• Regularization-Batch Normalization
• Regularization-Dropout
• Working with Google collab

This module will help the candidate to gain knowledge of Computer Vision. Learn to work with images. Gain knowledge about Convolutions 2D for images. Know about the CNN architectures. Get acquainted with the knowledge of Semantic segmentation using UNet, and more

• Working with Images
• Introduction to convolutions2D convolutions for Images
• Convolution-Forward and Backward
• Transposed Convolution and Fully Connected Layer as a Convolution
• Pooling: Max Pooling and other Poolings
• CNN Architectures
• ALexNet, LeNet,ZFNet,VGGNet, GoogleNet, ResNet
• GPU Vs CPU
• Transfer Learning
• Semantic Segmentation using UNet
• Inception and Mobile Net Models
• Object Detection with region proposals, YOLO and SSD
• Bounding Box Regressor
• Siamese Network for Metric Learning

This module will equip the candidate with the knowledge of Natural Langiage Processing. Learn the Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF. Know about Word embedding, POS Tagging, LSTM application, Encoder-Decoder attention, and more

• Introduction to NLP
• Preprocessing in NLP-Tokenization, Lemmatization, Stemming, Normalisation, Stop words, BOW, TF-IDF
• N-gram models and channel models
• Word Embedding
• Dense Encoding
• Word-2-Vec Vectors and Glove Vectors
• POS Tagging
• Named Entity recognition
• Sequential models
• Introduction to RNN, LSTM
• LSTM Forward Pass
• LSTM Backprop through time
• Applications of LSTM
• Advanced LSTM Structures
• Encoder-Decoder Attention

Soft Skills Training

Resume Writing

- HR: Analyze the Attrition rate of Employees

- Sales: Predicting Department wise Sales

- Multi-Domain: Business Analytics Optimization

- Marketing: Website Trend Analysis

- Financial Analysis: Stock Market Prediction

- Finance: Analyze ETF Trends