Project Based Text Mining in Python

Introduction 5 lectures 11:11 Course Introduction Course Introduction Instructor's Introduction Introduction of the Instructor Course Outline Outline of the course, briefly describing the content of its 9 modules. Course Overview An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. Session 1: Resources Introduction 5 lectures 11:11 Course Introduction Course Introduction Instructor's Introduction Introduction of the Instructor Course Outline Outline of the course, briefly describing the content of its 9 modules. Course Overview An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. Session 1: Resources Course Introduction Course Introduction Course Introduction Course Introduction Course Introduction Course Introduction Course Introduction Course Introduction Course Introduction Course Introduction Instructor's Introduction Introduction of the Instructor Instructor's Introduction Introduction of the Instructor Instructor's Introduction Introduction of the Instructor Instructor's Introduction Introduction of the Instructor Introduction of the Instructor Introduction of the Instructor Course Outline Outline of the course, briefly describing the content of its 9 modules. Course Outline Outline of the course, briefly describing the content of its 9 modules. Course Outline Outline of the course, briefly describing the content of its 9 modules. Course Outline Outline of the course, briefly describing the content of its 9 modules. Outline of the course, briefly describing the content of its 9 modules. Outline of the course, briefly describing the content of its 9 modules. Course Overview An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. Course Overview An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. Course Overview An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. Course Overview An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. An overview of the project to do at the end of this course is given. It almost covers all the topics that we study during this course. Session 1: Resources Session 1: Resources Session 1: Resources Session 1: Resources Text Representation 8 lectures 20:16 2.1.1 Theoretical Concepts of Text Representation In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. 2.2.1 Structuring One Document Corpus In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. 2.2.2 Structuring a Multiple Document Corpus In this lecture, a multi-document corpus is structured to generate a matrix representation with multiple row representing document. A cell value represents the frequency or binary value of the feature in column in the document in row. 2.2.3 Setting Parameters In this video, we experiment with the different parameter settings for the constructor CountVectorizer. Setting the binary = 'true' converts all the values that are greater than 1 to 1. The parameters max_df and min_df define the maximum and minimum range of document frequencies for a word. Anything beyond this range is ignored. max_features help to restrict the number of features to a specific number. ngram_range helps to provide a lower and upper limit for ngrams to be considered as features. 2.2.4 Using TF-IDF Representation This lecture covers representing the textual documents in a structured format while having tf-idf values for each word in a document. These values are generated with the formula. For any word w in document d, TF-IDF(w) = TF(w) x IDF(w) TF(w) = count of w in d / size of d IDF(w) = log [M +1 / k] Where M is the total number of documents while k is the count of documents containing w. 2.2.5 Reading Data from a Labeled Dataset 2.2.6 Using Textual Dataset from UCI Respository Applying the text representation techniques on a dataset from UCI repository. Session 2: Resources Text Representation. 8 lectures 20:16 2.1.1 Theoretical Concepts of Text Representation In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. 2.2.1 Structuring One Document Corpus In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. 2.2.2 Structuring a Multiple Document Corpus In this lecture, a multi-document corpus is structured to generate a matrix representation with multiple row representing document. A cell value represents the frequency or binary value of the feature in column in the document in row. 2.2.3 Setting Parameters In this video, we experiment with the different parameter settings for the constructor CountVectorizer. Setting the binary = 'true' converts all the values that are greater than 1 to 1. The parameters max_df and min_df define the maximum and minimum range of document frequencies for a word. Anything beyond this range is ignored. max_features help to restrict the number of features to a specific number. ngram_range helps to provide a lower and upper limit for ngrams to be considered as features. 2.2.4 Using TF-IDF Representation This lecture covers representing the textual documents in a structured format while having tf-idf values for each word in a document. These values are generated with the formula. For any word w in document d, TF-IDF(w) = TF(w) x IDF(w) TF(w) = count of w in d / size of d IDF(w) = log [M +1 / k] Where M is the total number of documents while k is the count of documents containing w. 2.2.5 Reading Data from a Labeled Dataset 2.2.6 Using Textual Dataset from UCI Respository Applying the text representation techniques on a dataset from UCI repository. Session 2: Resources 2.1.1 Theoretical Concepts of Text Representation In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. 2.1.1 Theoretical Concepts of Text Representation In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. 2.1.1 Theoretical Concepts of Text Representation In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. 2.1.1 Theoretical Concepts of Text Representation In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. In this lecture, the students will learn about the need of transforming textual data into a structured format so that they can be consumed by machine learning techniques for analysis. Textual data is raw and is far from being structured despite of having paragraphs and sentences. In terms of machine learning the data should be in the form of a matrix having rows and columns with each cell holding a numerical value. In this lecture we will look into structuring the textual data and applying different representation schemes on it. 2.2.1 Structuring One Document Corpus In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. 2.2.1 Structuring One Document Corpus In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. 2.2.1 Structuring One Document Corpus In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. 2.2.1 Structuring One Document Corpus In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. In this lecture, we implemented the text representation technique with a very basic corpus consisting of only one document. The document is converted in a matrix of one row and few features representing unique words in the corpus. 2.2.2 Structuring a Multiple Document Corpus In this lecture, a multi-document corpus is structured to generate a matrix representation with multiple row representing document. A cell value represents the frequency or binary value of the feature in column in the document in row. 2.2.2 Structuring a Multiple Document Corpus In this lecture, a multi-document corpus is structured to generate a matrix representation with multiple row representing document. A cell value represents the frequency or binary value of the feature in column in the document in row. 2.2.2 Structuring a Multiple Document Corpus In this lecture, a multi-document corpus is structured to generate a matrix representation with multiple row representing document. A cell value represents the frequency or binary value of the feature in column in the document in row. 2...