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    Certified Artificial Intelligence Practitioner Course

    BY
    Henry Harvin

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    Mode

    Online

    Duration

    32 Hours

    Fees

    ₹ 13500 15000

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    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

    Module 4: Complimentary Module 1: Soft Skills Development

    • Business Communication
    • Preparation for the Interview
    • Presentation Skills

    Module 5: Complimentary Module 2: Resume Writing

    • Resume Writing

    Module 6: Resource library

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