Introduction to Text Mining and NLP

• Overview of Text Mining

• Need of Text Mining

• Natural Language Processing (NLP) in Text Mining

• Applications of Text Mining

• OS Module

• Reading, Writing to text and word files

• Setting the NLTK Environment

• Accessing the NLTK Corpora

Extracting, Cleaning and Preprocessing Text

• Tokenization

• Frequency Distribution

• Different Types of Tokenizers

• Bigrams, Trigrams & Ngrams

• Stemming

• Lemmatization

• Stopwords

• POS Tagging

• Named Entity Recognition

Analyzing Sentence Structure

• Syntax Trees

• Chunking

• Chinking

• Context Free Grammars (CFG)

• Automating Text Paraphrasing

Text Classification - I

• Machine Learning: Brush Up

• Bag of Words

• Count Vectorizer

• Term Frequency (TF)

• Inverse Document Frequency (IDF)

Getting Started with TensorFlow 2.0

• 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

• Using Adam Optimizer

Introduction to Deep Learning

• What is Deep Learning

• Curse of Dimensionality

• Machine Learning vs. Deep Learning

• Use cases of Deep Learning

• Human Brain vs. Neural Network

• What is Perceptron

• Learning Rate

• Epoch

• Batch Size

• Activation Function

• Single Layer Perceptron

Neural Networks

• What is NN

• Types of NN

• Creation of simple neural network using tensorflow

Convolution Neural Network

• 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

Image Processing and Computer Vision

• Introduction to Vision

• Importance of Image Processing

• Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories

• Introduction to Image – Image Transformation, Image Processing Operations & Simple Point Operations

• Noise Reduction – Moving Average & 2D Moving Average

• Image Filtering – Linear & Gaussian Filtering

• Disadvantage of Correlation Filter

• Introduction to Convolution

• Boundary Effects – Zero, Wrap, Clamp & Mirror

• Image Sharpening

• Template Matching

• Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector

• Effect of Noise

• Laplacian Filter

• Smoothing with Gaussian

• LOG Filter – Blob Detection

• Noise – Reduction using Salt & Pepper Noise using Gaussian Filter

• Nonlinear Filters

• Bilateral Filters

• Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding

• Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling

• Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation

• Introduction to the dnn module
• Deep Learning Deployment Toolkit
• Use of DLDT with OpenCV4.0

• OpenVINO Toolkit
• Introduction
• Model Optimization of pre-trained models
• Inference Engine and Deployment process