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    Certified Artificial Intelligence Practitioner Course

    BY
    Henry Harvin

    Brochure Enquire
    Mode

    Online

    Duration

    32 Hours

    Fees

    ₹ 13500 15000

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study, Virtual Classroom
    Mode of Delivery Video and Text Based
    Frequency of Classes Weekends

    Course and certificate fees

    Fees information
    ₹ 13,500  ₹15,000
    certificate availability

    Yes

    certificate providing authority

    Henry Harvin

    The syllabus

    Module 1: Neural Networks

    • 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
    • Quiz Module 1

    Module 2: Computer Vision

    • 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
    • Quiz Module 2

    Module 3: Natural Language Processing

    • 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
    • Quiz Module 3

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