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Edureka Artificial Intelligence Certification Course

Edureka Artificial Intelligence Certification Course#

Course Information#

Edureka Artificial Intelligence Certification Course

  • Instructor: Edureka

  • Duration: 10 weeks

  • Level: Intermediate

Table of Contents#

Advanced Artificial Intelligence Course#

1.: Introduction to Text Mining and NLP Topics: • Overview of Text Mining • Need of Text Mining • Natural Language Processing (NLP) in Text Mining • Applications of Text Mining • OS •. Reading, Writing to text and word files • Setting the NLTK Environment • Accessing the NLTK Corpora 2.: Extracting, Cleaning and Preprocessing Text Topics: • Tokenization • Frequency Distribution • Different Types of Tokenizers • Bigrams, Trigrams & Ngrams Discover Learning • Stemming • Lemmatization • Stopwords • POS Tagging • Named Entity Recognition 3.: Analyzing Sentence Structure Topics: • Syntax Trees • Chunking • Chinking • Context Free Grammars (CFG) • Automating Text Paraphrasing 4.: Text Classification-I Topics: • Machine Learning: Brush Up • Bag of Words • Count Vectorizer • Term Frequency (TF) • Inverse Document Frequency (IDF) 5.: Introduction to Deep Learning Topics: • What is Deep Learning? • Curse of Dimensionality

• Use cases of Deep Learning • Human Brain vs. Neural Network • What is Perceptron? • Learning Rate • Epoch • Batch Size • Activation Function • Single Layer Perceptron 6.: Getting Started with TensorFlow 2.0 Topics: • Introduction to TensorFlow 2.x • Installing TensorFlow 2.x • Defining Sequence model layers • Activation Function • Layer Types • Model Compilation • Model Optimizer • Model Loss Function • Model Training • Digit Classification using Simple Neural Network in TensorFlow 2.x • Improving the model • Adding Hidden Layer • Adding Dropout

7.: Convolution Neural Network Topics: • Image Classification Example • What is Convolution • Convolutional Layer Network • Convolutional Layer • Filtering • ReLU Layer • Pooling • Data Flattening • Fully Connected Layer • Predicting a cat or a dog • Saving and Loading a Model • Face Detection using OpenCV 8.: Regional CNN Topics: • Regional-CNN • Selective Search Algorithm • Bounding Box Regression • SVM in RCNN • Pre-trained Model • Model Accuracy • Model Inference Time • Model Size Comparison • Transfer Learning

• mAP • IoU • RCNN – Speed Bottleneck • Fast R-CNN • RoI Pooling • Fast R-CNN – Speed Bottleneck • Faster R-CNN • Feature Pyramid Network (FPN) • Regional Proposal Network (RPN) • Mask R-CNN 9.: Boltzmann Machine & Autoencoder Topics: • What is Boltzmann Machine (BM)? • Identify the issues with BM • Why did RBM come into the picture? • Step-by-step implementation of RBM • Distribution of Boltzmann Machine • Understanding Autoencoders • Architecture of Autoencoders • Brief on types of Autoencoders • Applications of Autoencoders 1.0: Generative Adversarial Network (GAN) Topics: • Which Face is Fake?

• What is Generative Adversarial Network? • How does GAN work? • Step by step Generative Adversarial Network implementation • Types of GAN • Recent Advances: GAN 1.1: Emotion and Gender Detection (Self-paced) Topics: • Which Face is Fake? • Understanding GAN • What is Generative Adversarial Network? • How does GAN work? • Step by step Generative Adversarial Network implementation • Types of GAN • Recent Advances: GAN 1.2: Introduction to RNN and GRU (Self-paced) Topics: • Issues with Feed Forward Network • Recurrent Neural Network (RNN) • Architecture of RNN • Calculation in RNN • Backpropagation and Loss calculation • Applications of RNN • Vanishing Gradient • Exploding Gradient

• Components of GRU • Update gate • Reset gate • Current memory content • Final memory at current time step 1.3: LSTM (Self-paced) Topics: • What is LSTM? • Structure of LSTM • Forget Gate • Input Gate • Output Gate • LSTM architecture • Types of Sequence-Based Model • Sequence Prediction • Sequence Classification • Sequence Generation • Types of LSTM • Vanilla LSTM • Stacked LSTM • CNN LSTM • Bidirectional LSTM • How to increase the efficiency of the model? • Backpropagation through time

1.4: Auto Image Captioning Using CNN LSTM (Self-paced) Topics: • Auto Image Captioning • COCO dataset • Pre-trained model • Inception V3 model • The architecture of Inception V3 • Modify the last layer of a pre-trained model • Freeze model • CNN for image processing • LSTM or text processing 1.5: Developing a Criminal Identification and Detection Application Using OpenCV (Self-paced) Topics: • Why is OpenCV used? • What is OpenCV • Applications • Demo: Build a Criminal Identification and Detection App 1.6: TensorFlow for Deployment (Self-paced) Topics: • Use Case: Amazon’s Virtual Try-Out Room. • Why Deploy models? • Model Deployment: Intuit AI models • Model Deployment: Instagram’s Image Classification Models

• Types of Model Deployment Techniques • TensorFlow Serving • Browser-based Models • What is TensorFlow Serving? • What are Servables? • Demo: Deploy the Model in Practice using TensorFlow Serving • Introduction to Browser based Models • Demo: Deploy a Deep Learning Model in your Browser. 1.7: Text Classification-II (Self-paced) Topics: • Converting text to features and labels • Multinomial Naive Bayes Classifier • Leveraging Confusion Matrix 1.8: In Class Project (Self-paced)

Price#

  • Cost: : ₹35,000

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