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DIPLOMA IN ML AND NLP

ICT Course Duration: 3 Weeks

This Diploma Program in ML and NLP has been developed by industry experts to help you learn the basics and advance term of Natural Language Processing. It has been designed for fresh graduates and working professionals looking to build their career in Data Science. [Note: The program comes in two formats: weekday format for fresh graduates and weekend format for working professionals.] ________________________________________

Fee - Rs. 6000/-

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.