Natural Language Processing- 50 Hours

20%

Course Description

Data Science, Machine Learning, Deep Learning and Artificial Intelligence

Objectives of the Program

• To enable participants understand how Data Science is used in every aspect of our daily lives and businesses
• To enable the participants to learn basics of Python.
• To enable the participants to learn Data Science/Machine learning/Deep Learning using Python.
• To empower the participants with sufficient knowledge to use latest technologies of Data Science (Hands-On).
• To make the participants understand overview of how Exploratory Data Analysis works.
• To provide the participants Hands-on experience in Data science concepts.
• To provide the participants Hands-on experience in Machine Learning concepts.
• To empower the participants with sufficient knowledge to use latest technologies of Data Science/Machine Learning/Deep Learning/ Artificial Intelligence (Hands-On).
• Overview of the Program Content
• Introduction to Data Science, Machine Learning, Deep Learning and Artificial Intelligence
• Basics of Python and Hands-on on Python
• Introduction to Data Science using Python
• Hands on session on Data Science with Python [Database Connectivity with PYTHON: Performing Database Transactions (Inserting, Deleting, and Updating the Database)]
• Introduction to Machine Learning and its types
• Examples Overview of Machine Learning packages in Python
• Introduction to Deep Learning and its types
• Project in Data Science/Machine Learning /Artificial Intelligence

CURRICULUM DETAILS: MODULES AND CHAPTERS

Module 1: Natural Language Processing (NLP) Basics

• Introduction to NLP
• Text Preprocessing
• Bag of words Model and TF-IDF
• Introduction to Language Model: N-grams/Word2vec/Glove

Module 2: Sequential Models and NLP

• Introduction Recurrent Neural Network (RNN)
• Introduction to Long Short term Memory (LSTM)
• Introduction to GRU
• Introduction to Encoder –Decoder
• Introduction to Transformer
• Introduction to BERT
• Introduction to GPT3

Module 3: Unsupervised Deep Learning

o Introduction to GAN and its types
o Introduction to Autoencoder

Tools, Languages and Libraries Used:

1. Sql
2. Python
3. Anaconda
4. Jupyter Notebook
5. Google Colab
6. Tableau
7. Numpy
8. Pandas
9. Matplotlib

10. Seaborn
11. Sklearn
12. Scipy
13.Tensorflow
14. Keras

Mr. Arpit Yadav
Senior Data Scientist in AIML
Subject Matter Expert in AIML