Mastering Text Analytics

About Course

The goal of this program is to enable your skills, essentially turn text into data for analysis with applying Machine learning, natural language processing and analytical methods. To accomplish this, text analytics courses include text data pre-processing, extracting themes and information, text visualization and predictive analytics.

Apply multiple data pre-processing techniques to convert unstructured raw data in to machine readable vector representations
Understanding the behaviour from the data using techniques like sentiment analysis
Discover the abstract from the collection of documents using techniques like topic modelling
Classify the document in to multiple classes for deeper insights using techniques like text classification
Apply machine learning algorithm for text analysis

Course Content

Machine Learning

Introduction
Introduction to Sklearn
Common ML Algorithms
Simple Linear Regression using sklearn
Logistic Regression – Code Demo
Evaluation metrics for classification models
Evaluation metrics and Naive Bayes Demo – Part 1
Evaluation metrics and Naive Bayes Demo – Part 2
Decision Trees for classification
Decision Trees for classification – Demo
Decision Trees for Regression
Decision Trees for Regression – Code demo
Naïve Bayes
Multiple Linear regression – Demo
SVM for classification
SVM for classification – Demo
KNN for Classification
KNN for Classification – Demo
Understanding K-means clustering
Understanding K-means clustering – Demo
Understanding Agglomerative Clustering
Understanding Agglomerative Clustering – Demo
Assignment 1 – IPL Player clustering using Kmeans
Assignment 2 – Restaurant classification based on Reviews
Case Study – Life Expectancy prediction
Case Study – Predict Credit Card defaults
Project – Classification of Human activity Recoginition
Project – Predictive model to forecast the sales of a supermarket
Machine Learning : Quiz

Advanced Machine Learning

Bias and Variance
Demo: Bias & Variance
Overfitting & Regularization
Demo: Overfitting & Regularization
Ensemble methods an intuition
Gradient Boosting
Feature Engineering
Feature Engineering – Demo
Special Cases in ML
Working with Time Series Data
Working with Time Series Data – Demo
Decomposing Time series Data
Time Series Forecasting
Deterministic signal vs white noise
Scope for prediction using ACF plots
Estimating models order using ACF plot – Demo
Cross Validation
Grid Search Hyperparameter Tuning
Random Forest
Random Search Hyperparameter Tuning
Forecasting using SARIMA Model
Assignment 1: Risk of Heart Disease Prediction
Assignment 2: Monthly earning classification
Case Study: Predict customers income range
Case Study: Classification model to classify the individuals based on their monthly earning
Project: Predict the numbers of comments a Facebook post will receive
Project: Ensemble Techniques to classify the customer churn
Advanced Machine Learning: Quiz

Text Analytics

Introduction to Text Analytics
Applications and Challenges with Text Data
Introduction to Text Cleaning-Part 1
Introduction to Text Cleaning-Part 2
Demo: Reading Text Files in Python
Demo: Readnig PDF files in Python
Demo: Tokenization
Demo: Stopwords removal
Demo: Bag of words analysis using wordclouds
Demo : Twitter Hashtag Analysis
Demo: Stemming
Demo: Lemmatization
Frequency Based Vector Representation
Demo: Creating Document Term Matrix
Demo: Understanding Document Term Matrix
Term Frequency – Inverse Document Frequency
Demo: TF-IDF Transformation
Word Embeddings Based Vector Representations
Demo: Loading word embeddings
Introduction to Naive Bayes Algorithm
Demo: Document vector representation using word embeddings
Demo: Text classification using Naive Bayes Algorithm
Introduction to Sentiment Analysis
Sentiment analysis using VADER technique
Demo: Sentiment analysis using VADER – Part 1
Demo: Sentiment analysis using VADER – Part 2
Demo: Sentiment classifier
Introduction to Topic Modeling
Latent Dirichlet Allocation (LDA) Algorithm
Demo: Building LDA Topic Model
Demo: Understanding Topic Modeling results
Introduction to Text Summarization
Lex Rank Algorithm
Demo: Implemenation of Text Summarization
Case Study – Sentiment Analysis on Twitter data
Case Study: Text Classification using Word Embeddings & NN
Assignment 1 – Text Preprocessing & DTM Creation
Assignment 2: Extract themes from abstracts
Project: Topic modelling system based on reviews on Amazon
Text Analytics : Quiz

About Course

What Will You Learn?

Course Content

Machine Learning

Introduction

Introduction to Sklearn

Common ML Algorithms

Simple Linear Regression using sklearn

Logistic Regression – Code Demo

Evaluation metrics for classification models

Evaluation metrics and Naive Bayes Demo – Part 1

Evaluation metrics and Naive Bayes Demo – Part 2

Decision Trees for classification

Decision Trees for classification – Demo

Decision Trees for Regression

Decision Trees for Regression – Code demo

Naïve Bayes

Multiple Linear regression – Demo

SVM for classification

SVM for classification – Demo

KNN for Classification

KNN for Classification – Demo

Understanding K-means clustering

Understanding K-means clustering – Demo

Understanding Agglomerative Clustering

Understanding Agglomerative Clustering – Demo

Assignment 1 – IPL Player clustering using Kmeans

Assignment 2 – Restaurant classification based on Reviews

Case Study – Life Expectancy prediction

Case Study – Predict Credit Card defaults

Project – Classification of Human activity Recoginition

Project – Predictive model to forecast the sales of a supermarket

Machine Learning : Quiz

Advanced Machine Learning

Bias and Variance

Demo: Bias & Variance

Overfitting & Regularization

Demo: Overfitting & Regularization

Ensemble methods an intuition

Gradient Boosting

Feature Engineering

Feature Engineering – Demo

Special Cases in ML

Working with Time Series Data

Working with Time Series Data – Demo

Decomposing Time series Data

Time Series Forecasting

Deterministic signal vs white noise

Scope for prediction using ACF plots

Estimating models order using ACF plot – Demo

Cross Validation

Grid Search Hyperparameter Tuning

Random Forest

Random Search Hyperparameter Tuning

Forecasting using SARIMA Model

Assignment 1: Risk of Heart Disease Prediction

Assignment 2: Monthly earning classification

Case Study: Predict customers income range

Case Study: Classification model to classify the individuals based on their monthly earning

Project: Predict the numbers of comments a Facebook post will receive

Project: Ensemble Techniques to classify the customer churn

Advanced Machine Learning: Quiz

Text Analytics

Introduction to Text Analytics

Applications and Challenges with Text Data

Introduction to Text Cleaning-Part 1

Introduction to Text Cleaning-Part 2

Demo: Reading Text Files in Python

Demo: Readnig PDF files in Python

Demo: Tokenization

Demo: Stopwords removal

Demo: Bag of words analysis using wordclouds

Demo : Twitter Hashtag Analysis

Demo: Stemming

Demo: Lemmatization

Frequency Based Vector Representation

Demo: Creating Document Term Matrix

Demo: Understanding Document Term Matrix

Term Frequency – Inverse Document Frequency

Demo: TF-IDF Transformation