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Chapter 1: Natural Language Processing & Artificial Intelligence OverviewChapter Goal: This is an introductory chapter. This chapter provides a quick refresher of the topics to be covered in this book. Since this book teaches projects surrounding a specific area of technology, we will provide a brief introduction to the key concepts required for these projects. We will not be working on a specific project, rather discuss some important concepts without going into details. The depth on each of these topics will be covered in the specific chaptersNo of pages: 25Sub - Topics: 1. Artificial intelligence paradigm2. NLP and AI life cycle3. NLP concepts (TF-IDF, word embeddings, many more)4. Machine learning concepts (supervised learning, classification, unsupervised learning)5. Deep learning concepts (CNN, RNN, LSTM)
Chapter 2: Product360 - Sentiment, Emotion & Trend Capturing SystemChapter Goal: Sentiment analysis involves finding the polarity of a sentence and labels it as positive, negative or neutral. Emotion detection involves identifying emotions(sad, anger, happy, etc) from the sentences. Data is extracted from social media like Twitter, Facebook etc. and Ecommerce website, processed and analyzed using different NLP techniques will provide a 360 degree view of that product which enables better decision making. This chapter introduces sentiment analysis to the reader and the various techniques that can be used to analyze text. We will apply sentiment, emotion and trend analysis on reviews data for any E-commerce website like Amazon, Zomato, and IMDb, etc. which contains millions of customer reviews and star ratings. For this task, we will use Python libraries such as Vader, Textblob, etc. No of pages: 30Sub - Topics 1. Text mining and various available libraries. 2. Data preprocessing.3. Data cleaning tricks, optimized feature engineering4. EDA5. Sentiment analysis6. Emotion and trend analysis
Chapter 3: TED Talks Segmentation & Topics Extraction Using Machine LearningChapter Goal: Document clustering is an unsupervised learning process for grouping documents. For example, there are number of e-books and they have to be grouped to build a structure around them saves time while finding the books. Articles grouping, product clustering are the other few examples. Once we identify the clusters, it is important to understand the properties of clusters. So, Topic modeling is performed to extract topics from a set of documents and articles to understand the content of the documents using keywords and be able to tag the articles or documents using those topics. In this chapter will see how to group TED talks based on description using various clustering techniques like K-Means and Hierarchical clustering. Then we will perform topic modeling using Latent Dirichlet Allocation (LDA) to understand what defines each cluster. Important libraries include Gensim, NLTK, Scikit-learn and word2vec for this problem. We will use over 100k articles from different American publications. No of pages: 30Sub - Topics 1. Data understanding and pre-processing2. Computing TF-IDF 3. K-Means and hierarchical clustering4. Evaluation and visualization5. Topic modeling using Latent Dirichlet Allocation
Chapter 4: Enhancing E-commerce Through Advanced Search Engine and Recommendation SystemChapter Goal: An information retrieval system will search product descriptions based on a search query text and gives the results. Search engines are the most common and best use case of information retrieval models. The concept of information retrieval started from a string or word comp
