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Hadi Harb, Instructor -  Marketing Analytics: Text Analysis & Recommendation Systems

Hadi Harb

Hadi has more than 15 years of experience in the development and management of Artificial Intelligence and Audio Signal Processing projects. Hadi holds an MEng in electrical-electronic engineering. He earned his MSc and PhD both in computer science from the Institut National des Sciences Appliquées INSA Lyon, and the Ecole Centrale de Lyon respectively.

Instructor: Hadi Harb

Techniques for: Information Retrieval, Classification, Clustering & Recommenders

  • Enables students to conduct text analysis, clustering, classification and handle recommender systems while implementing learnings in professional settings.
  • Instructor: Researched and taught Artificial Intelligence for 15 years. He also founded and operated a successful Artificial Intelligence startup in France. 

Duration: 3h 19m

Course Description

This course: 1- Describes some text analysis techniques used for information retrieval 2- Describes how to build a recommender system 3- Describes the principle of K-means clustering, Decision Trees, K-NN, Naive Bayes and Neural Networks 4- Introduces WEKA software for classification and recommender systems 5- Introduces StanfordNLP framework for text analysis

What am I going to get from this course?

Learn how to:
  • Use regular expressions to find and replace patterns in text.
  • Use Stanford NLP framework for part-of-speech tagging and named entity recognition.
  • Use GloVe tool to estimate semantic similarity between words.
  • Use TF-IDF weighting scheme in a search application.
  • Detect problems where clustering can be used.
  • Define supervised learning problems.
  • Use WEKA software to solve automatic classification problems.
  • Use WEKA software to generate rules for a recommender system.

Prerequisites and Target Audience

What will students need to know or do before starting this course?

  1. Bachelor's Degree in computer science
  2. Physics
  3. Maths
  4. Economy, or a related specialty is recommended.

Who should take this course? Who should not?

This course is adapted for:
  • A technical person willing to know how to use Artificial Intelligence techniques to solve his/her problems.
  • A management person willing to understand the practical applications of Artificial Intelligence in his/her business.
  • A technical or non-technical person wishing to start using existing frameworks for text analysis, clustering, automatic classification and recommender systems.
This course is not adapted for:
  • A technical person wishing to understand the algorithmic details so that he/she can implement Artificial Intelligence algorithms.
  • A technical person wishing to modify/improve existing Artificial Intelligence algorithms.


Module 1: Introduction

Lecture 1 Why This Course?

Module 2: Text Analysis

Lecture 2 Text Analysis

In this video you will learn about the reasons why text analysis is important. Typical applications of text analysis are listed: search applications, text classification, named entity recognition, and pattern search and replace applications.

Lecture 3 Regular Expressions - part 1

In this video you will be introduced to the regular expressions and how they can be used to find patterns in texts such as URLs, emails, dates, time ...

Lecture 4 Regular Expressions - part 2

In this video you will learn by example how to write regular expressions to: find email patterns, find date patterns and parse XML documents.

Lecture 5 Preprocessing Text

In this video you will learn about the typical pre-processing steps that are used in text analysis applications. You will learn about tokenization, tags stripping, stop-words elimination, part-of-speech tagging, and named entity recognition.

Lecture 6 Search: Information Retrieval - part 1

In this video you will be introduced to the information retrieval subject and you will learn about the vector space model.

Lecture 7 Search: Information Retrieval - part 2

In this video you will learn about TF-IDF (Term Frequency - Inverse Document Frequency) and its use in text information retrieval.

Lecture 8 Semantic Analysis - part 1

In this video you will learn about semantic analysis in text. You will be introduced to WordNet and learn about its use in enriching text analysis applications.

Lecture 9 Semantic Analysis - part 2

In this video you will learn about numerical vector representation of words (word embeddings). You will learn how this representation can be used to estimate semantic similarity or relatedness between words.

Lecture 10 Demo: Regular Expressions (RegExr)

In this video you will learn how website can be used to find regular expressions that are shared by the community. It shows you also how to test your regular expressions live.

Lecture 11 Demo: Pre-processing (Stanford NLP)

In this video you will learn about the Standford NLP framework that can be used for text processing applications. The demo shows you how the framework can be used to extract part-of-speech tags and name entities from a text.

Lecture 12 Demo: Numerical Vector Representation of Words (GLOVE)

In this video you will learn about GloVe: a tool that lets you generate word embeddings. You will see how word embeddings can be used to estimate semantic similarity between words.

Module 3: Clustering

Lecture 13 Clustering

In this video you will learn about clustering and its applications such as: customer segmentation, fast search, and visualization.

Lecture 14 K-Means Clustering

In this video you will learn about the K-means clustering algorithm and its principle of operations.

Module 4: Classification

Lecture 15 Classification

In this video you will learn about automatic classification and its applications. You will learn how to define a supervised learning problem so that you can apply a machine learning algorithm to solve it.

Lecture 16 Decision Trees - part 1

In this video you will be introduced to the Decision Trees classifiers and learn about their principle of operations.

Lecture 17 Decision Trees - part 2

In this video you learn how a Decision Tree can be automatically generated to represent data and to classify data points.

Lecture 18 Naive Bayes & K-NN

In this video you will be introduced to the Naive Baye's and K-NN (K Nearest Neighbors) classifiers. You will learn when each classifier is more adapted to be used.

Lecture 19 Neural Networks

In this video you will be introduced to Neural Networks classifiers. You will learn about the principle of error back-propagation algorithm that is typically used to train Neural Networks.

Lecture 20 Demo: Classification (WEKA)

In this video you will be introduced to the WEKA software that can be used for classification, clustering and recommendation. You will learn how WEKA can be used in the case of two classification problems: cancer recurrence classification and text classification.

Module 5: Recommenders

Lecture 21 Recommenders

In this video you will be introduced to recommender systems. You will learn about association rules and their use in recommender systems.

Lecture 22 Demo: Association Rules (WEKA)

In this video you will learn how WEKA software can be used to generate association rules from transaction data. An example of how you would generate recommendation rules based on the analysis of super-market transactions data.


8 Reviews

Kamal K

December, 2016

Martin R

May, 2017

A very informative course I ever came across. It can definitely help me solve many day-to-day problems, and enables to do text analysis, clustering, classification in professional settings and much more. It is a great experience to learn from a founder of a successful Artificial intelligence startup with fifteen years. It is easy to learn artificial intelligence from a practical perspective, AI techniques, and its basics, and open-source frameworks to build AI applications. The course provides value for the time and money invested in learning it. The video learning is a new experience for me and able to grasp.

Bill S

May, 2017

With this excellent course I could learn using regular expressions, NLP framework estimate semantic similarities and use of weka software to solve automatic classification problem.

Kevin H

May, 2017

A very beneficial excellent course as I could find.the instructor was precise in getting to very points in the course. Overall I satisfied in learning this course.

Jeff H

July, 2017

This is one of the most impressive courses I have had ever. Very good. Both challenging & valuable - showed me fundamentals of marketing analytics. The instructor is particularly communicative, and the course information is great!

Martijn D

July, 2017

So far it has been excellent. So obvious! This is an intermediate foundation to marketing analytics, but the techniques presented here are indeed what you want to have.

Debarpita P

July, 2017

This is one of the best classes I have ever taken. It is a delight, and quite comprehensive. I would usually put in 2-3 hours per week. The questions are demanding but good.

Nils E

July, 2017

You will learn much more than using just analytics alone. You learn how to ask appropriate questions and how to put together your analytics results to the C-suite or whoever asked for it. I definitely recommend this to anybody- novices and professional business/data analysts. I got a lot out of this course.