DIPLOMA IN ML AND NLP

Upon completion of this course, you will be able to:

MODULE 1 - NATURAL LANGUAGE PROCESSING & NLTK

What is NLP? | Typical NLP Tasks | Morphology | Sentence Segmentation & Tokenization | Pattern

Matching with Regular Expression | Stemming, Lemmatization | Part of Speech – POS | Named

Entity Recognition (NER) | Parsing, Chunking | Stop Words Removal (English) | Corpora/Corpus |

Context Window – Bi-gram, N-gram | Applications of NLP | Introduction to the NLTK Library |

Processing Raw Text | Regular Expression | Normalizing Text | Processing Raw Text – Tokenize

Sentences | String Processing with Regular Expression | Normalizing Text | Extracting Features

from Text | Bag-of-Words(BoW), TF-IDF | Similarity score - Cosine similarity | Naïve Bayes Classifier

ADVANCED NATURAL LANGUAGE PROCESSING & SPACY

Introduction to spaCy | Tokenization | POS Tagging | Dependency Parsing | Named Entity

Recognition (NER) | Word Vectors and Word Similarity | Visualizations using display |

Applications | Support Vector Classifier

MODULE 2 – DATA VISUALIZATION

Introduction to Supervised Learning and Unsupervised Learning | Linear Regression | Logistic Regression | | Decision Trees and Random Forest | Support Vector Machines (SVM) | K-Means Clustering | Hierarchical Agglomerative Clustering

Course Project

·       CREATE DASHBOARD

Course Duration and Learning pattern

Duration of Certificate – 3 Week

Classes’ distribution – 50% Theory Classes and 50% Implementation

Key Highlights

Assignments Students will receive timely assignment related with course topics.

          Attendance Policy 85%

         Instructor Feedback One can directly report about the feedback of instructor.